Single cell transcriptomes of nk mono cells with HIV 2023

Introduction

There are 3 samples.

1-17032023-GEX
2-09032023-GEX
3-13032023-GEX

I had to make a new custom reference genome with the HIV sequence.

suppressPackageStartupMessages({
  library("plyr")
  library("Seurat")
  library("hdf5r")
  library("SingleCellExperiment")
  library("parallel")
  library("stringi")
  library("beeswarm")
  library("muscat")
  library("DESeq2")
  library("mitch")
  library("limma")
  library("kableExtra")
  library("gplots")
})

Load data.

load("scanalyse1.Rdata")

Normalise data

cellmetadata <- data.frame(colnames(comb) ,sapply(strsplit(colnames(comb)," "),"[[",1))
colnames(cellmetadata) <- c("cell","patient")
comb <- CreateSeuratObject(comb, project = "nkmono", assay = "RNA", meta.data = cellmetadata)
comb <- NormalizeData(comb)

## Normalizing layer: counts

comb <- FindVariableFeatures(comb, selection.method = "vst", nfeatures = 2000)

## Finding variable features for layer counts

comb <- ScaleData(comb)

## Centering and scaling data matrix

PCA and Cluster

comb <- RunPCA(comb, features = VariableFeatures(object = comb))

## PC_ 1 
## Positive:  NAMPT, PLXDC2, FCN1, IRAK3, CYBB, CTSS, DMXL2, LYZ, LRMDA, FAM49A 
##     SLC8A1, DENND5A, KYNU, VCAN, GAB2, ACSL1, CST3, S100A9, GRK3, MCTP1 
##     IFI30, AIF1, HCK, RAB31, RBM47, TLR2, MNDA, LRRK2, CD83, UBE2E2 
## Negative:  SKAP1, CD247, NKG7, GNLY, GZMA, CCL5, GZMB, STAT4, RORA, FGFBP2 
##     IL32, NCALD, GZMH, TRBC2, CARD11, CD96, SPON2, C12orf75, KLRB1, YES1 
##     CD69, PPP2R2B, CLIC3, ISG20, BCL11B, TOX, TC2N, RHOH, KLRC3, PRSS23 
## PC_ 2 
## Positive:  DPYD, NEAT1, FNDC3B, S100A9, GBP2, SAMSN1, S100A8, ARHGAP26, GNLY, CCL5 
##     NKG7, CD247, FCGR3A, GZMA, FGFBP2, CCL4, ATP2B1, S100A12, PLCB1, FCN1 
##     AQP9, BCL2A1, TNFAIP3, C5AR1, CD14, MT2A, VCAN, GZMH, SKAP1, G0S2 
## Negative:  RHEX, LINC01374, CLEC4C, COBLL1, ITM2C, SERPINF1, LILRA4, NIBAN3, AC023590.1, FAM160A1 
##     CUX2, LINC01478, DERL3, TSPAN13, EPHB1, PLD4, SCT, RGS7, LRRC26, BLNK 
##     PTPRS, SPIB, P2RY14, SCAMP5, JCHAIN, SCN9A, TCF4, TPM2, NRP1, AC007381.1 
## PC_ 3 
## Positive:  TNFAIP3, TNFAIP6, PLCB1, CXCL8, AQP9, IL1B, CCL4, CLEC4E, IRAK2, NFKB1 
##     FNDC3B, CCL3, AZIN1-AS1, STEAP4, IVNS1ABP, SLC39A8, LIMK2, ACVR2A, PDE4D, STAT4 
##     ARHGAP26, S100A12, CD247, ACSL1, IER3, GZMB, EREG, SKAP1, LINC00910, CCL3L1 
## Negative:  CDKN1C, HES4, PAPSS2, CSF1R, SNED1, FMNL2, CKB, HLA-DPA1, AC104809.2, HMOX1 
##     MS4A7, LYPD2, CD86, CTSL, VMO1, SMIM25, AC020651.2, LST1, IFITM3, CCDC26 
##     AC005237.1, SPRED1, TIMP1, TMTC1, CD79B, HLA-DPB1, FAM20A, AIF1, MYOF, LINC02432 
## PC_ 4 
## Positive:  HBEGF, SLC2A3, S100A12, ADAM28, VEGFA, GPAT3, RNF24, FOS, FCER1A, S100A8 
##     VCAN, IL7R, AC020656.1, CAMK4, TMEM170B, LYZ, LGALS2, PLBD1, SDC2, SLC25A37 
##     FOLR3, INPP4B, S100A9, LMNA, CD1C, CAMK2A, ARSI, SNHG29, PADI4, CLEC10A 
## Negative:  FCGR3A, CCL4, MTSS1, AC020651.2, TCF7L2, CDKN1C, CCL3, IL1B, HES4, IFITM3 
##     GCH1, SETBP1, MS4A7, LINC02085, KCNJ2, ZEB2, AZIN1-AS1, FMNL2, CCL4L2, MIR3142HG 
##     MIR155HG, SMIM25, GBP2, CSF1R, SERPINB9, MGLL, CCL3L1, BATF3, TNF, IL1A 
## PC_ 5 
## Positive:  IL7R, CAMK4, LTB, INPP4B, BACH2, CD28, ZEB1, TRAC, NELL2, CD3D 
##     LEF1, CD3G, CD27, TNFAIP8, THEMIS, PATJ, ANK3, CCR7, MAL, BCL11B 
##     TRAF1, GPR183, GZMK, TNFAIP3, NR3C2, AQP3, FAAH2, SNHG29, IL1B, HLA-DQA1 
## Negative:  GZMB, FGFBP2, SPON2, CLIC3, FCGR3A, BNC2, PRSS23, GZMH, GNLY, NKG7 
##     S100A8, MEGF9, ZEB2, CLEC12A, S100A12, PPM1L, S100A9, PADI4, CYP1B1, RBP7 
##     LRRK2, DYSF, ARHGAP26, SLC15A4, KIF13A, ARHGAP24, GZMA, VCAN, CD36, LINGO2

DimHeatmap(comb, dims = 1, cells = 500, balanced = TRUE)

ElbowPlot(comb)

comb <- JackStraw(comb, num.replicate = 100)
comb <- FindNeighbors(comb, dims = 1:5)

## Computing nearest neighbor graph

## Computing SNN

comb <- FindClusters(comb, resolution = 0.2)

## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 35603
## Number of edges: 1014624
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9460
## Number of communities: 9
## Elapsed time: 4 seconds

UMAP

comb <- RunUMAP(comb, dims = 1:8)

## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session

## 13:23:02 UMAP embedding parameters a = 0.9922 b = 1.112

## 13:23:02 Read 35603 rows and found 8 numeric columns

## 13:23:02 Using Annoy for neighbor search, n_neighbors = 30

## 13:23:02 Building Annoy index with metric = cosine, n_trees = 50

## 0%   10   20   30   40   50   60   70   80   90   100%

## [----|----|----|----|----|----|----|----|----|----|

## **************************************************|
## 13:23:04 Writing NN index file to temp file /tmp/RtmpydpsKt/file1a74911619b4bf
## 13:23:04 Searching Annoy index using 1 thread, search_k = 3000
## 13:23:15 Annoy recall = 100%
## 13:23:16 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 13:23:18 Initializing from normalized Laplacian + noise (using RSpectra)
## 13:23:19 Commencing optimization for 200 epochs, with 1424968 positive edges
## 13:23:28 Optimization finished

DimPlot(comb, reduction = "umap")

Assign cell type with canonical markers

Cluster ID	Markers	Cell Type
0	IL7R, CCR7	Naive CD4+ T
1	CD14, LYZ	CD14+ Mono
2	IL7R, S100A4	Memory CD4+
3	MS4A1	B
4	CD8A	CD8+ T
5	FCGR3A, MS4A7	FCGR3A+ Mono
6	GNLY, NKG7	NK
7	FCER1A, CST3	DC
8	PPBP	Platelet

message("Naive CD4+ T")

## Naive CD4+ T

VlnPlot(comb, features = c("IL7R", "CCR7"))

message("CD14+ Mono")

## CD14+ Mono

VlnPlot(comb, features = c("CD14", "LYZ"))

message("Memory CD4+ T")

## Memory CD4+ T

VlnPlot(comb, features = c("IL7R", "S100A4"))

message("B")

## B

VlnPlot(comb, features = c("MS4A1"))

message("CD8+ T")

## CD8+ T

VlnPlot(comb, features = c("CD8A"))

message("FCGR3A+ Mono")

## FCGR3A+ Mono

VlnPlot(comb, features = c("FCGR3A", "MS4A7"))

message("NK")

## NK

VlnPlot(comb, features = c("GNLY", "NKG7"))

message("DC")

## DC

VlnPlot(comb, features = c("FCER1A", "CST3"))

message("Platelet")

## Platelet

VlnPlot(comb, features = c("PPBP"))

#new.cluster.ids <- c("Naive CD4 T", "CD14+ Mono", "Memory CD4 T", "B", "CD8 T",
#    "FCGR3A+ Mono", "NK", "DC", "Platelet")

FeaturePlot(comb, features = c("MS4A1", "GNLY", "CD3E", "CD14", "FCER1A", "FCGR3A",
  "LYZ", "PPBP", "CD8A"))

#names(new.cluster.ids) <- levels(comb)

Now get the marker genes for each cluster.

myvec <- unique(comb[["seurat_clusters"]][,1])

markers <- mclapply(myvec, function(i) { FindMarkers(comb, ident.1 = i ) } , mc.cores=16)

length(markers)

## [1] 9

# NK 1 (cluster 0)
head(markers[[1]],10)

##        p_val avg_log2FC pct.1 pct.2 p_val_adj
## FGFBP2     0   4.405845 0.941 0.124         0
## GZMB       0   3.657251 0.981 0.185         0
## GZMA       0   3.273408 0.964 0.190         0
## PRF1       0   3.928052 0.912 0.145         0
## CST7       0   3.653445 0.984 0.219         0
## KLRF1      0   3.727494 0.879 0.121         0
## KLRD1      0   3.136655 0.943 0.185         0
## SPON2      0   4.307448 0.864 0.135         0
## GZMH       0   3.278490 0.855 0.132         0
## TRDC       0   3.759667 0.813 0.093         0

# CD14+ monocytes 1 (cluster 1)
head(markers[[2]],10)

##           p_val avg_log2FC pct.1 pct.2 p_val_adj
## AQP9          0   3.475060 0.880 0.230         0
## IRAK2         0   3.748144 0.800 0.162         0
## IL1B          0   4.677256 0.886 0.271         0
## CLIC4         0   3.276105 0.868 0.255         0
## ICAM1         0   2.600262 0.903 0.308         0
## AZIN1-AS1     0   3.748872 0.764 0.170         0
## IER3          0   2.314379 0.880 0.291         0
## NLRP3         0   2.662558 0.942 0.359         0
## CLEC4E        0   2.614319 0.794 0.213         0
## TNFAIP6       0   4.602969 0.654 0.079         0

# NK 2 (cluster 2)
head(markers[[3]],10)

##        p_val avg_log2FC pct.1 pct.2 p_val_adj
## CDKN1C     0   5.941590 0.894 0.071         0
## HES4       0   4.745219 0.753 0.041         0
## SMIM25     0   3.438883 0.909 0.233         0
## CSF1R      0   3.324781 0.879 0.205         0
## SNED1      0   4.117990 0.752 0.094         0
## MS4A7      0   3.433621 0.940 0.295         0
## TCF7L2     0   3.316507 0.989 0.361         0
## LRRC25     0   2.829461 0.888 0.261         0
## HMOX1      0   2.799216 0.847 0.223         0
## FMNL2      0   4.785793 0.665 0.051         0

# CD14+ monocytes 2 (cluster 3)
head(markers[[4]],10)

##          p_val avg_log2FC pct.1 pct.2 p_val_adj
## SEPTIN7      0  -2.610204 0.061 0.925         0
## YWHAZ        0  -2.023444 0.105 0.967         0
## KMT2E        0  -2.396477 0.075 0.934         0
## CDC42SE2     0  -2.315329 0.105 0.962         0
## BTG1         0  -2.389523 0.103 0.959         0
## CDC42        0  -2.129717 0.104 0.958         0
## HNRNPC       0  -2.536145 0.085 0.939         0
## ANKRD12      0  -2.067971 0.090 0.944         0
## ARHGAP15     0  -2.592570 0.097 0.949         0
## JAK1         0  -2.050399 0.100 0.951         0

# FCGR3A+ monocytes (cluster 4)
head(markers[[5]],10)

##          p_val avg_log2FC pct.1 pct.2 p_val_adj
## FCER1A       0   6.647756 0.837 0.035         0
## ADAM28       0   3.356873 0.897 0.139         0
## AFF3         0   2.785053 0.882 0.147         0
## CLEC10A      0   5.910158 0.768 0.044         0
## CCSER1       0   5.031951 0.756 0.051         0
## HLA-DQA1     0   4.165148 0.989 0.300         0
## FLT3         0   3.322687 0.751 0.085         0
## CD1C         0   6.237336 0.690 0.025         0
## PHACTR1      0   2.008151 0.897 0.247         0
## CIITA        0   2.767178 0.849 0.199         0

# cluster 5 unknown - probably T cell
# IL7R = Naive T-cells
# CD3D = CD4+ T cells
# CD3E = T cells
head(markers[[6]],10)

##       p_val avg_log2FC pct.1 pct.2 p_val_adj
## IL7R      0   6.644805 0.695 0.023         0
## CD3D      0   3.830768 0.691 0.063         0
## CD3E      0   1.959087 0.801 0.194         0
## CD3G      0   3.706040 0.643 0.050         0
## IL32      0   1.870267 0.838 0.273         0
## TC2N      0   2.537292 0.727 0.167         0
## ZEB1      0   3.176193 0.661 0.123         0
## DOCK5     0  -2.521629 0.166 0.701         0
## TRAC      0   4.678234 0.578 0.043         0
## CAMK4     0   5.860070 0.555 0.022         0

# DC (cluster 6)
head(markers[[7]],10)

##            p_val avg_log2FC pct.1 pct.2 p_val_adj
## RHEX           0   9.021711 0.980 0.010         0
## ITM2C          0   6.923569 0.992 0.037         0
## LINC01374      0   9.073702 0.964 0.011         0
## AC023590.1     0   7.189649 0.983 0.038         0
## NIBAN3         0   7.336635 0.955 0.011         0
## SERPINF1       0   7.432269 0.969 0.029         0
## CLEC4C         0   9.443771 0.943 0.004         0
## JCHAIN         0   5.052743 0.969 0.035         0
## PTPRS          0   6.421687 0.980 0.047         0
## COBLL1         0   7.473471 0.941 0.013         0

# cluster 7 unknown - probably macrophage
# CD14 = macrophages
# S100A12 = macrophages and monocytes
# MS4A6A = macrophages and monocytes
head(markers[[8]],10)

##            p_val avg_log2FC pct.1 pct.2 p_val_adj
## CD14           0   2.590196 0.943 0.215         0
## S100A12        0   2.831699 0.937 0.213         0
## MS4A6A         0   2.339090 0.943 0.246         0
## CSF3R          0   2.403534 0.986 0.304         0
## WDFY3          0   2.299412 0.927 0.258         0
## VCAN           0   2.897514 0.996 0.328         0
## CSTA           0   2.178795 0.962 0.297         0
## SLC11A1        0   2.272561 0.952 0.291         0
## AC020656.1     0   2.608471 0.918 0.261         0
## RBM47          0   2.340318 0.960 0.304         0

# B cells (cluster 8)
head(markers[[9]],10)

##         p_val avg_log2FC pct.1 pct.2 p_val_adj
## STRBP       0   3.616973 0.819 0.104         0
## COBLL1      0   4.245510 0.621 0.024         0
## GNG7        0   4.335929 0.677 0.096         0
## DENND5B     0   4.280196 0.641 0.066         0
## CD79A       0   8.257984 0.552 0.009         0
## BANK1       0   6.899614 0.596 0.053         0
## OSBPL10     0   6.749382 0.554 0.019         0
## IGHM        0   7.730666 0.563 0.043         0
## RAB30       0   4.310501 0.565 0.050         0
## FAM30A      0   7.040820 0.518 0.010         0

As there are two CD14+ mono and NK clusters, it might be a good idea to look at their differences.

# find all NK markers distinguishing cluster 0 (case) from cluster 2 (ctrl)
nk0.markers <- FindMarkers(comb, ident.1 = 0, ident.2 = 2,only.pos=TRUE)

## For a (much!) faster implementation of the Wilcoxon Rank Sum Test,
## (default method for FindMarkers) please install the presto package
## --------------------------------------------
## install.packages('devtools')
## devtools::install_github('immunogenomics/presto')
## --------------------------------------------
## After installation of presto, Seurat will automatically use the more 
## efficient implementation (no further action necessary).
## This message will be shown once per session

head(nk0.markers, n = 10)

##        p_val avg_log2FC pct.1 pct.2 p_val_adj
## FCGR3A     0   2.745818 0.886 0.261         0
## FGFBP2     0   2.651611 0.941 0.317         0
## SPON2      0   3.045026 0.864 0.277         0
## KLRF1      0   1.891888 0.879 0.305         0
## GZMB       0   2.488239 0.981 0.409         0
## TRDC       0   1.766307 0.813 0.267         0
## BNC2       0   4.207183 0.575 0.058         0
## TYROBP     0   1.723822 0.954 0.446         0
## GZMH       0   1.259498 0.855 0.380         0
## PRF1       0   2.152961 0.912 0.438         0

nk2.markers <- FindMarkers(comb, ident.1 = 2, ident.2 = 0,only.pos=TRUE)
head(nk2.markers, n = 10)

##        p_val avg_log2FC pct.1 pct.2 p_val_adj
## IL7R       0   7.195133 0.695 0.018         0
## CD3D       0   2.458322 0.691 0.130         0
## CD3G       0   2.316037 0.643 0.105         0
## CAMK4      0   5.602151 0.555 0.023         0
## LTB        0   5.130821 0.608 0.082         0
## ZEB1       0   2.657739 0.661 0.160         0
## INPP4B     0   6.085176 0.512 0.018         0
## TRAC       0   3.473439 0.578 0.086         0
## CASK       0   2.261784 0.622 0.175         0
## BACH2      0   2.536058 0.610 0.189         0

# find all monocyte markers distinguishing cluster 1 (case) from cluster 3 (ctrl)
monocyte1.markers <- FindMarkers(comb, ident.1 = 1, ident.2 = 3, only.pos=TRUE)
head(monocyte1.markers, n = 10)

##                 p_val avg_log2FC pct.1 pct.2     p_val_adj
## MAP3K1   0.000000e+00  0.9131158 0.956 0.842  0.000000e+00
## FOS     2.870720e-253  0.7017975 0.994 0.964 1.050770e-248
## TXNIP   2.558514e-252  0.6061089 0.991 0.938 9.364930e-248
## CD86    4.906385e-239  0.9719702 0.867 0.717 1.795884e-234
## HBEGF   8.447570e-238  1.5986423 0.616 0.341 3.092064e-233
## S100A6  6.590490e-225  0.4338555 0.996 0.979 2.412317e-220
## CLEC12A 1.099567e-207  0.8335866 0.871 0.756 4.024747e-203
## MEF2C   3.078758e-202  0.7487670 0.914 0.805 1.126918e-197
## TUBA1A  1.429381e-199  1.0066707 0.771 0.571 5.231964e-195
## PICALM  3.080819e-183  0.4269503 0.999 0.987 1.127672e-178

monocyte3.markers <- FindMarkers(comb, ident.1 = 3, ident.2 = 1, only.pos=TRUE)
head(monocyte3.markers, n = 10)

##           p_val avg_log2FC pct.1 pct.2 p_val_adj
## CCL4          0   4.646652 0.684 0.140         0
## TNFAIP6       0   3.244527 0.654 0.197         0
## AZIN1-AS1     0   2.668943 0.764 0.313         0
## CCL3          0   3.026348 0.820 0.380         0
## SLC39A8       0   3.992489 0.509 0.093         0
## IRAK2         0   2.496833 0.800 0.387         0
## IL1B          0   3.840793 0.886 0.492         0
## CCL3L1        0   2.286813 0.606 0.231         0
## NOTCH2NLC     0   2.161316 0.695 0.337         0
## ACVR2A        0   2.670119 0.719 0.363         0

Assign names to clusters

NK 1 (FCGR3A+)
CD14+ monocytes
NK 2 (IL7R+)
CD14+ monocytes (CCL4+)
FCGR3A+ monocytes
T
DC
macrophages
B

new.cluster.ids <- c("NK 1 (FCGRA3A+)", "CD14+ Mono", "NK 2 (IL7R+)", "CD14+ Mono (CCL4+)", "FCGR3A+ Mono", "T",
    "DC", "Macrophage", "B")

names(new.cluster.ids) <- levels(comb)

comb <- RenameIdents(comb, new.cluster.ids)

DimPlot(comb, reduction = "umap", label = TRUE, pt.size = 0.5) + NoLegend()

count cells by patient

Sample	Plate	HTO
CC0003	1	1
AH0018	1	3
PM008	1	4
PM017	1	5
PM0032	2	1
PM0028	2	2
AH0005	2	3
AH0015	2	4
PM0027	3	2
PM0020	3	3
PM001	3	4
CC0016	3	5

str(Idents(comb))

##  Factor w/ 9 levels "NK 1 (FCGRA3A+)",..: 1 4 4 1 4 1 4 1 1 1 ...
##  - attr(*, "names")= chr [1:35603] "CC0003 AAACCCAAGAATTTGG" "CC0003 AAACCCAAGCTAGTTC" "CC0003 AAACCCACATGGCCCA" "CC0003 AAACGAACACATTACG" ...

table(Idents(comb))

## 
##    NK 1 (FCGRA3A+)         CD14+ Mono       NK 2 (IL7R+) CD14+ Mono (CCL4+) 
##              10379               9021               5244               3761 
##       FCGR3A+ Mono                  T                 DC         Macrophage 
##               2819               1935               1441                644 
##                  B 
##                359

cells <- paste(Idents(comb),names(Idents(comb)))

cells2 <- strsplit((stringi::stri_reverse(cells)), " ")
cells2 <- lapply(cells2,function(x) { stringi::stri_reverse(paste(x[2:length(x)],collapse=" "))})
cells2 <- table(unlist(cells2))

cmx <- matrix(cells2,nrow=12)
colnames(cmx) <- unique(sapply(strsplit(gsub(" CC","@",gsub(" AH","@",gsub(" PM","@",names(cells2)))),"@"),"[[",1))

patient_id <- c("AH0005+","AH0015+","AH0018+","CC0003-","CC0016+","PM001+","PM0020-","PM0027-","PM0028-","PM0032-","PM008+","PM017+")
hiv_status <- c(1,1,1,0,1,1,0,0,0,0,1,1)
batch <- factor(c(2,2,1,1,3,3,3,3,2,2,1,1))
pat_df <- data.frame(patient_id,hiv_status,batch) # there were 3 chromium runs contianing multiple samples
pat_df

##    patient_id hiv_status batch
## 1     AH0005+          1     2
## 2     AH0015+          1     2
## 3     AH0018+          1     1
## 4     CC0003-          0     1
## 5     CC0016+          1     3
## 6      PM001+          1     3
## 7     PM0020-          0     3
## 8     PM0027-          0     3
## 9     PM0028-          0     2
## 10    PM0032-          0     2
## 11     PM008+          1     1
## 12     PM017+          1     1

rownames(cmx) <- patient_id

tcmx <- t(cmx)

tcmx |> kbl(caption="cell counts") |> kable_paper("hover", full_width = F)

cell counts
	AH0005+	AH0015+	AH0018+	CC0003-	CC0016+	PM001+	PM0020-	PM0027-	PM0028-	PM0032-	PM008+	PM017+
B	11	60	29	24	27	28	19	18	46	24	49	24
CD14+ Mono (CCL4+)	20	87	212	854	608	280	48	129	98	196	1032	197
CD14+ Mono	334	620	1356	29	114	1377	745	1625	935	1066	214	606
DC	59	102	96	74	103	184	54	327	135	89	128	90
FCGR3A+ Mono	114	159	487	156	293	208	328	206	244	120	320	184
Macrophage	12	16	15	100	52	81	20	69	41	71	90	77
NK 1 (FCGRA3A+)	1298	1049	251	1595	1083	563	972	80	475	264	1016	1733
NK 2 (IL7R+)	353	459	339	221	971	468	400	451	439	195	398	550
T	32	352	104	219	194	248	179	100	121	44	221	121

ntcmx <- apply(tcmx,2,function(x) { x/sum(x)*100 } )

ntcmx |> kbl(caption="cell proportions") |> kable_paper("hover", full_width = F)

cell proportions
	AH0005+	AH0015+	AH0018+	CC0003-	CC0016+	PM001+	PM0020-	PM0027-	PM0028-	PM0032-	PM008+	PM017+
B	0.4926108	2.0661157	1.0038075	0.7334963	0.7837446	0.814664	0.6871609	0.5990017	1.815312	1.159981	1.412918	0.6700168
CD14+ Mono (CCL4+)	0.8956561	2.9958678	7.3381793	26.1002445	17.6487663	8.146639	1.7359855	4.2928453	3.867403	9.473175	29.757786	5.4997208
CD14+ Mono	14.9574563	21.3498623	46.9366563	0.8863081	3.3091437	40.064009	26.9439421	54.0765391	36.898185	51.522475	6.170704	16.9179229
DC	2.6421854	3.5123967	3.3229491	2.2616137	2.9898403	5.353506	1.9529837	10.8818636	5.327545	4.301595	3.690888	2.5125628
FCGR3A+ Mono	5.1052396	5.4752066	16.8570440	4.7677262	8.5050798	6.051789	11.8625678	6.8552413	9.629045	5.799903	9.227220	5.1367951
Macrophage	0.5373936	0.5509642	0.5192108	3.0562347	1.5094340	2.356706	0.7233273	2.2961730	1.617995	3.431609	2.595156	2.1496371
NK 1 (FCGRA3A+)	58.1280788	36.1225895	8.6881274	48.7469438	31.4368650	16.380564	35.1537071	2.6622296	18.745067	12.759787	29.296425	48.3807929
NK 2 (IL7R+)	15.8083296	15.8057851	11.7341641	6.7542787	28.1857765	13.616526	14.4665461	15.0083195	17.324388	9.424843	11.476355	15.3545505
T	1.4330497	12.1212121	3.5998615	6.6931540	5.6313498	7.215595	6.4737794	3.3277870	4.775059	2.126631	6.372549	3.3780011

tntcmx <- t(ntcmx)

tntcmx |> kbl(caption="cell proportions") |> kable_paper("hover", full_width = F)

cell proportions
	B	CD14+ Mono (CCL4+)	CD14+ Mono	DC	FCGR3A+ Mono	Macrophage	NK 1 (FCGRA3A+)	NK 2 (IL7R+)	T
AH0005+	0.4926108	0.8956561	14.9574563	2.642185	5.105240	0.5373936	58.128079	15.808330	1.433050
AH0015+	2.0661157	2.9958678	21.3498623	3.512397	5.475207	0.5509642	36.122589	15.805785	12.121212
AH0018+	1.0038075	7.3381793	46.9366563	3.322949	16.857044	0.5192108	8.688127	11.734164	3.599861
CC0003-	0.7334963	26.1002445	0.8863081	2.261614	4.767726	3.0562347	48.746944	6.754279	6.693154
CC0016+	0.7837446	17.6487663	3.3091437	2.989840	8.505080	1.5094340	31.436865	28.185776	5.631350
PM001+	0.8146640	8.1466395	40.0640093	5.353506	6.051789	2.3567064	16.380564	13.616526	7.215595
PM0020-	0.6871609	1.7359855	26.9439421	1.952984	11.862568	0.7233273	35.153707	14.466546	6.473779
PM0027-	0.5990017	4.2928453	54.0765391	10.881864	6.855241	2.2961730	2.662230	15.008320	3.327787
PM0028-	1.8153118	3.8674033	36.8981847	5.327545	9.629045	1.6179953	18.745067	17.324388	4.775059
PM0032-	1.1599807	9.4731754	51.5224746	4.301595	5.799903	3.4316095	12.759787	9.424843	2.126631
PM008+	1.4129181	29.7577855	6.1707036	3.690888	9.227220	2.5951557	29.296425	11.476355	6.372549
PM017+	0.6700168	5.4997208	16.9179229	2.512563	5.136795	2.1496371	48.380793	15.354551	3.378001

tntcmx_pos <- tntcmx[grep("\\+",rownames(tntcmx)),]

tntcmx_neg <- tntcmx[grep("\\-",rownames(tntcmx)),]

par(mfrow=c(3,3))

lapply(1:ncol(cmx) , function(i) {
  cellname=colnames(tntcmx)[i]
  boxplot(tntcmx_neg[,i],tntcmx_pos[,i],col="white",main=cellname, names=c("HIV-","HIV+"))
  beeswarm(list(tntcmx_neg[,i],tntcmx_pos[,i]),add=TRUE,cex=1.5,col="gray",pch=19)
  tt <- t.test(tntcmx_neg[,i],tntcmx_pos[,i])
  mtext(paste("P=",signif(tt$p.value,3)),cex=0.8)
})

## [[1]]
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summary(tntcmx_neg)

##        B          CD14+ Mono (CCL4+)   CD14+ Mono            DC        
##  Min.   :0.5990   Min.   : 1.736     Min.   : 0.8863   Min.   : 1.953  
##  1st Qu.:0.6872   1st Qu.: 3.867     1st Qu.:26.9439   1st Qu.: 2.262  
##  Median :0.7335   Median : 4.293     Median :36.8982   Median : 4.302  
##  Mean   :0.9990   Mean   : 9.094     Mean   :34.0655   Mean   : 4.945  
##  3rd Qu.:1.1600   3rd Qu.: 9.473     3rd Qu.:51.5225   3rd Qu.: 5.328  
##  Max.   :1.8153   Max.   :26.100     Max.   :54.0765   Max.   :10.882  
##   FCGR3A+ Mono      Macrophage     NK 1 (FCGRA3A+)   NK 2 (IL7R+)   
##  Min.   : 4.768   Min.   :0.7233   Min.   : 2.662   Min.   : 6.754  
##  1st Qu.: 5.800   1st Qu.:1.6180   1st Qu.:12.760   1st Qu.: 9.425  
##  Median : 6.855   Median :2.2962   Median :18.745   Median :14.467  
##  Mean   : 7.783   Mean   :2.2251   Mean   :23.614   Mean   :12.596  
##  3rd Qu.: 9.629   3rd Qu.:3.0562   3rd Qu.:35.154   3rd Qu.:15.008  
##  Max.   :11.863   Max.   :3.4316   Max.   :48.747   Max.   :17.324  
##        T        
##  Min.   :2.127  
##  1st Qu.:3.328  
##  Median :4.775  
##  Mean   :4.679  
##  3rd Qu.:6.474  
##  Max.   :6.693

summary(tntcmx_pos)

##        B          CD14+ Mono (CCL4+)   CD14+ Mono           DC       
##  Min.   :0.4926   Min.   : 0.8957    Min.   : 3.309   Min.   :2.513  
##  1st Qu.:0.7269   1st Qu.: 4.2478    1st Qu.:10.564   1st Qu.:2.816  
##  Median :0.8147   Median : 7.3382    Median :16.918   Median :3.323  
##  Mean   :1.0348   Mean   :10.3261    Mean   :21.387   Mean   :3.432  
##  3rd Qu.:1.2084   3rd Qu.:12.8977    3rd Qu.:30.707   3rd Qu.:3.602  
##  Max.   :2.0661   Max.   :29.7578    Max.   :46.937   Max.   :5.354  
##   FCGR3A+ Mono      Macrophage     NK 1 (FCGRA3A+)   NK 2 (IL7R+)  
##  Min.   : 5.105   Min.   :0.5192   Min.   : 8.688   Min.   :11.48  
##  1st Qu.: 5.306   1st Qu.:0.5442   1st Qu.:22.838   1st Qu.:12.68  
##  Median : 6.052   Median :1.5094   Median :31.437   Median :15.35  
##  Mean   : 8.051   Mean   :1.4598   Mean   :32.633   Mean   :16.00  
##  3rd Qu.: 8.866   3rd Qu.:2.2532   3rd Qu.:42.252   3rd Qu.:15.81  
##  Max.   :16.857   Max.   :2.5952   Max.   :58.128   Max.   :28.19  
##        T         
##  Min.   : 1.433  
##  1st Qu.: 3.489  
##  Median : 5.631  
##  Mean   : 5.679  
##  3rd Qu.: 6.794  
##  Max.   :12.121

ttres <- lapply(1:ncol(cmx) , function(i) {
  t.test(tntcmx_neg[,i],tntcmx_pos[,i])
})
names(ttres) <- colnames(cmx)
ttres

## $B
## 
##  Welch Two Sample t-test
## 
## data:  tntcmx_neg[, i] and tntcmx_pos[, i]
## t = -0.11782, df = 9.1332, p-value = 0.9088
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.7226046  0.6509059
## sample estimates:
## mean of x mean of y 
## 0.9989903 1.0348396 
## 
## 
## $`CD14+ Mono (CCL4+)`
## 
##  Welch Two Sample t-test
## 
## data:  tntcmx_neg[, i] and tntcmx_pos[, i]
## t = -0.21058, df = 8.8661, p-value = 0.838
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -14.49917  12.03486
## sample estimates:
## mean of x mean of y 
##  9.093931 10.326088 
## 
## 
## $`CD14+ Mono`
## 
##  Welch Two Sample t-test
## 
## data:  tntcmx_neg[, i] and tntcmx_pos[, i]
## t = 1.1042, df = 7.1775, p-value = 0.3051
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -14.33663  39.69453
## sample estimates:
## mean of x mean of y 
##  34.06549  21.38654 
## 
## 
## $DC
## 
##  Welch Two Sample t-test
## 
## data:  tntcmx_neg[, i] and tntcmx_pos[, i]
## t = 0.91617, df = 4.4017, p-value = 0.407
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -2.911907  5.938054
## sample estimates:
## mean of x mean of y 
##  4.945120  3.432047 
## 
## 
## $`FCGR3A+ Mono`
## 
##  Welch Two Sample t-test
## 
## data:  tntcmx_neg[, i] and tntcmx_pos[, i]
## t = -0.13025, df = 10, p-value = 0.8989
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -4.857908  4.321309
## sample estimates:
## mean of x mean of y 
##  7.782897  8.051196 
## 
## 
## $Macrophage
## 
##  Welch Two Sample t-test
## 
## data:  tntcmx_neg[, i] and tntcmx_pos[, i]
## t = 1.2737, df = 7.7835, p-value = 0.2395
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.6269623  2.1575263
## sample estimates:
## mean of x mean of y 
##  2.225068  1.459786 
## 
## 
## $`NK 1 (FCGRA3A+)`
## 
##  Welch Two Sample t-test
## 
## data:  tntcmx_neg[, i] and tntcmx_pos[, i]
## t = -0.86324, df = 8.3708, p-value = 0.4121
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -32.93015  14.89054
## sample estimates:
## mean of x mean of y 
##  23.61355  32.63335 
## 
## 
## $`NK 2 (IL7R+)`
## 
##  Welch Two Sample t-test
## 
## data:  tntcmx_neg[, i] and tntcmx_pos[, i]
## t = -1.174, df = 9.8896, p-value = 0.2679
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -9.867640  3.064279
## sample estimates:
## mean of x mean of y 
##  12.59568  15.99736 
## 
## 
## $T
## 
##  Welch Two Sample t-test
## 
## data:  tntcmx_neg[, i] and tntcmx_pos[, i]
## t = -0.63272, df = 9.6951, p-value = 0.5415
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -4.534393  2.535352
## sample estimates:
## mean of x mean of y 
##  4.679282  5.678803

par(mfrow=c(1,1))

There were no significant results of the cell counting, however there were some trends that might require further investigation. In particular relative counts of IL7R+ NK 2 cells were lower, CCL4+ CD14+ Monocytes were lower, while DCs were higher in HIV+ patients.

Differential expression

We are going to use muscat for pseudobulk analysis. First need to convert seurat obj to singlecellexperiment object. Then summarise counts to pseudobulk level.

sce <- Seurat::as.SingleCellExperiment(comb, assay = "RNA")

head(colData(sce),2)

## DataFrame with 2 rows and 8 columns
##                         orig.ident nCount_RNA nFeature_RNA
##                           <factor>  <numeric>    <integer>
## CC0003 AAACCCAAGAATTTGG     nkmono       7148         2886
## CC0003 AAACCCAAGCTAGTTC     nkmono      18524         3424
##                                           cell     patient RNA_snn_res.0.2
##                                    <character> <character>        <factor>
## CC0003 AAACCCAAGAATTTGG CC0003 AAACCCAAGAATT..      CC0003               0
## CC0003 AAACCCAAGCTAGTTC CC0003 AAACCCAAGCTAG..      CC0003               3
##                         seurat_clusters              ident
##                                <factor>           <factor>
## CC0003 AAACCCAAGAATTTGG               0 NK 1 (FCGRA3A+)   
## CC0003 AAACCCAAGCTAGTTC               3 CD14+ Mono (CCL4+)

colnames(colData(sce))

## [1] "orig.ident"      "nCount_RNA"      "nFeature_RNA"    "cell"           
## [5] "patient"         "RNA_snn_res.0.2" "seurat_clusters" "ident"

patient_id <- c("AH0005","AH0015","AH0018","CC0003","CC0016","PM001","PM0020","PM0027","PM0028","PM0032","PM008","PM017")
hiv_status <- c(1,1,1,0,1,1,0,0,0,0,1,1)

colData(sce)$sample_id <- colData(sce)$patient
colData(sce)$cluster_id <- colData(sce)$ident
colData(sce)$hiv <- pat_df[match(colData(sce)$patient,pat_df$patient_id),2]

#muscat library
pb <- aggregateData(sce,
    assay = "counts", fun = "sum",
    by = c("cluster_id", "sample_id"))

# one sheet per subpopulation
assayNames(pb)

## [1] "NK 1 (FCGRA3A+)"    "CD14+ Mono"         "NK 2 (IL7R+)"      
## [4] "CD14+ Mono (CCL4+)" "FCGR3A+ Mono"       "T"                 
## [7] "DC"                 "Macrophage"         "B"

t(head(assay(pb)))

##        gene-HIV1-1 gene-HIV1-2 MIR1302-2HG FAM138A OR4F5 AL627309.1
## AH0005           0           0           0       0     0          3
## AH0015           0           0           0       0     0          3
## AH0018           0           0           0       0     0          0
## CC0003           0           0           0       0     0          3
## CC0016           0           0           0       0     0          4
## PM001            0           0           0       0     0          0
## PM0020           0           0           0       0     0          0
## PM0027           0           0           0       0     0          0
## PM0028           0           0           0       0     0          0
## PM0032           0           0           0       0     0          0
## PM008            0           0           0       0     0          1
## PM017            0           0           0       0     0          4

plotMDS(assay(pb))

par(mfrow=c(3,3))

lapply(1:length(assays(pb)) , function(i) {
  cellname=names(assays(pb))[i]
  plotMDS(assays(pb)[[i]],cex=1.5,pch=19,col=hiv_status+1,main=paste(cellname))
  mtext("red=HIV+",cex=0.7)
})

## [[1]]
## NULL
## 
## [[2]]
## NULL
## 
## [[3]]
## NULL
## 
## [[4]]
## NULL
## 
## [[5]]
## NULL
## 
## [[6]]
## NULL
## 
## [[7]]
## NULL
## 
## [[8]]
## NULL
## 
## [[9]]
## NULL

par(mfrow=c(1,1))

lapply(1:length(assays(pb)) , function(i) {
  cellname=names(assays(pb))[i]
  plotMDS(assays(pb)[[i]],cex=1.5,pch=19,labels=patient_id,col=hiv_status+1,main=paste("MDS",cellname,"red=HIV+"))
})

## [[1]]
## An object of class MDS
## $eigen.values
##  [1] 1.179771e+09 1.660820e+07 3.764860e+06 2.606268e+06 1.042673e+06
##  [6] 9.765118e+05 6.110725e+05 2.549219e+05 1.341464e+05 6.281942e+04
## [11] 6.138144e+04 7.445078e-08
## 
## $eigen.vectors
##              [,1]        [,2]         [,3]        [,4]        [,5]         [,6]
##  [1,] -0.24499847  0.43932629 -0.202701765  0.69167619 -0.05516877  0.297270678
##  [2,] -0.08429061 -0.01776285 -0.330362269  0.07893745  0.41417513 -0.720759556
##  [3,]  0.34175183 -0.13471045  0.165176069 -0.09368173  0.03630105  0.003467323
##  [4,] -0.39868817 -0.76035801 -0.102308968  0.14436343 -0.33325109  0.014470883
##  [5,] -0.09192006  0.36239526 -0.170955792 -0.48710403 -0.50377630 -0.136098483
##  [6,]  0.19344373  0.03230844 -0.049743855  0.02224649  0.18287127 -0.078676470
##  [7,] -0.07497148  0.13740234 -0.162973756 -0.17703993 -0.30890560  0.008280558
##  [8,]  0.43034361 -0.07032515  0.237696298  0.05775455 -0.10988544  0.147779357
##  [9,]  0.22285106  0.07985959 -0.003332729  0.01536062  0.07020151 -0.010326365
## [10,]  0.31534239 -0.10756792  0.185729191  0.24258397 -0.11418859 -0.048321520
## [11,] -0.09709505 -0.11343899 -0.316343931 -0.37334285  0.50218429  0.582496802
## [12,] -0.51176878  0.15287146  0.750121507 -0.12175417  0.21944253 -0.059583208
##              [,7]        [,8]          [,9]       [,10]        [,11]     [,12]
##  [1,] -0.13342450  0.05590963 -0.0455733513  0.01226410  0.171936314 0.2886751
##  [2,] -0.17058906 -0.22681257 -0.0936631104 -0.11023172  0.037042354 0.2886751
##  [3,] -0.11940640  0.09141224 -0.0805790379  0.38600216  0.752483571 0.2886751
##  [4,] -0.02113199  0.14429607  0.0889191156 -0.08537691  0.023081265 0.2886751
##  [5,] -0.41170839  0.23962470  0.0299621647  0.01334934 -0.100387959 0.2886751
##  [6,]  0.43765483  0.72768914 -0.2619827277 -0.05237687 -0.207724768 0.2886751
##  [7,]  0.68086654 -0.47597659 -0.1918444521  0.05771280  0.092215546 0.2886751
##  [8,] -0.18843494 -0.20282337 -0.2476633341 -0.70275759 -0.030435931 0.2886751
##  [9,]  0.18715124  0.00226901  0.8959777647 -0.13233149 -0.005104892 0.2886751
## [10,] -0.14465466 -0.21609656 -0.0473848985  0.55520717 -0.564637622 0.2886751
## [11,] -0.14632104 -0.12062934 -0.0453255532  0.07292211 -0.141730496 0.2886751
## [12,]  0.02999838 -0.01886236 -0.0008425799 -0.01438308 -0.026737382 0.2886751
## 
## $var.explained
##  [1] 9.783373e-01 1.377253e-02 3.122050e-03 2.161275e-03 8.646474e-04
##  [6] 8.097826e-04 5.067383e-04 2.113966e-04 1.112423e-04 5.209366e-05
## [11] 5.090120e-05 6.173909e-17
## 
## $dim.plot
## [1] 1 2
## 
## $distance.matrix.squared
##            AH0005     AH0015     AH0018     CC0003     CC0016      PM001
## AH0005 -151050375  -48813681  200079599 -220109721  -56981074  111317748
## AH0015  -48813681  -19067220   68299114  -79735070  -18105937   38259986
## AH0018  200079599   68299114 -276549438  318307941   75695701 -155738705
## CC0003 -220109721  -79735070  318307941 -394692061  -77458330  182830938
## CC0016  -56981074  -18105937   75695701  -77458330  -26558984   41861547
## PM001   111317748   38259986 -155738705  182830938   41861547  -88961016
## PM0020  -44875493  -14801430   61315799  -67206686  -18492779   33952636
## PM0027  249830923   86340229 -347580777  403124887   94492640 -196049768
## PM0028  127656604   44339549 -179284919  211674554   47565249 -101873877
## PM0032  183280988   63000814 -254856789  293835942   70357523 -143597960
## PM008   -53909004  -19668912   77949713  -93827317  -20426494   44379165
## PM017  -296426514 -100047442  412362761 -476745077 -111949063  233619306
##           PM0020     PM0027     PM0028     PM0032      PM008      PM017
## AH0005 -44875493  249830923  127656604  183280988  -53909004 -296426514
## AH0015 -14801430   86340229   44339549   63000814  -19668912 -100047442
## AH0018  61315799 -347580777 -179284919 -254856789   77949713  412362761
## CC0003 -67206686  403124887  211674554  293835942  -93827317 -476745077
## CC0016 -18492779   94492640   47565249   70357523  -20426494 -111949063
## PM001   33952636 -196049768 -101873877 -143597960   44379165  233619306
## PM0020 -15145334   76820115   39004928   56721345  -16985648  -90307453
## PM0027  76820115 -437794696 -225987897 -320883814   98908925  518779234
## PM0028  39004928 -225987897 -117664995 -165475423   51361865  268684361
## PM0032  56721345 -320883814 -165475423 -235745198   72873964  380488610
## PM008  -16985648   98908925   51361865   72873964  -25377814 -115278444
## PM017  -90307453  518779234  268684361  380488610 -115278444 -623180278
## 
## $top
## [1] 500
## 
## $gene.selection
## [1] "pairwise"
## 
## $axislabel
## [1] "Leading logFC dim"
## 
## $x
##  [1]  -8415.157  -2895.196  11738.421 -13694.058  -3157.251   6644.365
##  [7]  -2575.105  14781.353   7654.442  10831.315  -3335.001 -17578.128
## 
## $y
##  [1]  1790.39350   -72.38924  -548.98768 -3098.69925  1476.87524   131.66709
##  [7]   559.95797  -286.59721   325.45308  -438.37328  -462.29976   622.99953
## 
## 
## [[2]]
## An object of class MDS
## $eigen.values
##  [1]  2.262292e+09  2.338745e+08  3.687864e+07  1.962398e+07  6.785555e+06
##  [6]  3.189939e+06  2.566050e+06  1.708646e+06  1.227233e+06  3.038702e+05
## [11]  6.074439e+04 -1.735721e-07
## 
## $eigen.vectors
##              [,1]        [,2]        [,3]        [,4]        [,5]         [,6]
##  [1,] -0.21099277  0.10587475 -0.02868638  0.06771672 -0.12901380  0.167378644
##  [2,] -0.08199674 -0.06345661 -0.13880812 -0.28712638 -0.09915371 -0.365195571
##  [3,]  0.29017169 -0.56077719 -0.39910871  0.55006766  0.18288245  0.122046287
##  [4,] -0.39562710  0.14951425 -0.05394657  0.06268717  0.15156046  0.301789109
##  [5,] -0.35529235  0.12244102 -0.03068457  0.02555451  0.15453341  0.219689304
##  [6,]  0.30519927 -0.22076871  0.06485829 -0.65421444  0.52724140  0.101485194
##  [7,] -0.02500655 -0.05094730  0.39255788  0.09859098 -0.01656440 -0.064229224
##  [8,]  0.57127801  0.73106446 -0.15820276  0.14920813  0.05152828  0.010950965
##  [9,]  0.21369170 -0.14261248  0.74140843  0.18292482 -0.20394264  0.043409735
## [10,]  0.10743988 -0.14169127 -0.26996331 -0.32173813 -0.74069849  0.245485819
## [11,] -0.30093007  0.08148308 -0.01649323  0.04219360  0.14651762 -0.003273051
## [12,] -0.11793498 -0.01012399 -0.10293096  0.08413534 -0.02489058 -0.779537212
##               [,7]        [,8]        [,9]         [,10]         [,11]
##  [1,]  0.094607124  0.83210905  0.29624111 -0.1430726201 -0.0358618215
##  [2,] -0.226474232 -0.26794839  0.73224556  0.0083073410  0.0408467339
##  [3,] -0.011155491 -0.03723853  0.07902480 -0.0010475826 -0.0059457573
##  [4,] -0.148322644 -0.11917497 -0.08974201  0.3945404085  0.6457446538
##  [5,] -0.081296273 -0.18090444 -0.06067394  0.2912823507 -0.7575232235
##  [6,]  0.027044462  0.17538694 -0.14949772  0.0140119259  0.0160466530
##  [7,]  0.822514681 -0.21125735  0.13111319  0.0623958551  0.0545902071
##  [8,] -0.001503039 -0.07053796  0.02828592 -0.0027754959 -0.0009649452
##  [9,] -0.471487781  0.01069135 -0.03733553  0.0102932834 -0.0111497775
## [10,]  0.081997770 -0.11066661 -0.28420493 -0.0009904231 -0.0031008347
## [11,] -0.065829705 -0.22765292 -0.20597199 -0.8334051320  0.0528189137
## [12,] -0.020094873  0.20719383 -0.43948446  0.2004600892  0.0044991981
##            [,12]
##  [1,] -0.2886751
##  [2,] -0.2886751
##  [3,] -0.2886751
##  [4,] -0.2886751
##  [5,] -0.2886751
##  [6,] -0.2886751
##  [7,] -0.2886751
##  [8,] -0.2886751
##  [9,] -0.2886751
## [10,] -0.2886751
## [11,] -0.2886751
## [12,] -0.2886751
## 
## $var.explained
##  [1] 8.807795e-01 9.105451e-02 1.435798e-02 7.640217e-03 2.641824e-03
##  [6] 1.241941e-03 9.990418e-04 6.652282e-04 4.777995e-04 1.183060e-04
## [11] 2.364965e-05 0.000000e+00
## 
## $dim.plot
## [1] 1 2
## 
## $distance.matrix.squared
##            AH0005     AH0015     AH0018     CC0003     CC0016      PM001
## AH0005 -209953787  -74109857  302722195 -384915941 -344721906  304581803
## AH0015  -74109857  -39770343   93463220 -141404472 -127634772  101374556
## AH0018  302722195   93463220 -552255450  555081231  496556338 -443907913
## CC0003 -384915941 -141404472  555081231 -720235946 -645638433  562403126
## CC0016 -344721906 -127634772  496556338 -645638433 -579165784  502918322
## PM001   304581803  101374556 -443907913  562403126  502918322 -465357532
## PM0020  -20629723   -5305188   28980904  -39173070  -36141044   30144103
## PM0027  508696487  233683516 -566303299  970341408  875817986 -709119692
## PM0028  211969924   84054493 -299635062  394963531  353350604 -307187019
## PM0032  108791238   29345089 -177532526  202955235  181674266 -164873438
## PM008  -290449691 -108641508  414715398 -544834811 -488855642  424161589
## PM017  -111980742  -45054734  148114964 -209541858 -188159935  164862096
##             PM0020      PM0027       PM0028     PM0032      PM008      PM017
## AH0005 -20629723.0   508696487  211969923.7  108791238 -290449691 -111980742
## AH0015  -5305188.2   233683516   84054493.0   29345089 -108641508  -45054734
## AH0018  28980904.4  -566303299 -299635062.2 -177532526  414715398  148114964
## CC0003 -39173070.0   970341408  394963530.9  202955235 -544834811 -209541858
## CC0016 -36141044.0   875817986  353350603.9  181674266 -488855642 -188159935
## PM001   30144102.8  -709119692 -307187019.2 -164873438  424161589  164862096
## PM0020 -19490565.4    86024217     586497.2   17440683  -31549768  -10887046
## PM0027  86024217.3 -1729403219 -495861186.8 -230029557  749394859  306758480
## PM0028    586497.2  -495861187 -259702384.0  -98200734  297235403  118425935
## PM0032  17440682.5  -230029557  -98200734.2  -79162355  153168499   56423602
## PM008  -31549767.8   749394859  297235402.5  153168499 -413954268 -160390060
## PM017  -10887045.7   306758480  118425935.2   56423602 -160390060  -68570703
## 
## $top
## [1] 500
## 
## $gene.selection
## [1] "pairwise"
## 
## $axislabel
## [1] "Leading logFC dim"
## 
## $x
##  [1] -10035.567  -3900.057  13801.598 -18817.432 -16898.968  14516.363
##  [7]  -1189.400  27172.014  10163.937   5110.223 -14313.305  -5609.407
## 
## $y
##  [1]  1619.1379  -970.4392 -8575.9413  2286.5150  1872.4852 -3376.2064
##  [7]  -779.1349 11180.1372 -2180.9665 -2166.8785  1246.1172  -154.8258
## 
## 
## [[3]]
## An object of class MDS
## $eigen.values
##  [1] 1.805114e+08 5.791190e+06 3.509207e+06 1.118331e+06 5.557315e+05
##  [6] 4.925769e+05 2.064478e+05 1.622386e+05 1.415559e+05 9.883636e+04
## [11] 4.096141e+04 1.060176e-08
## 
## $eigen.vectors
##              [,1]        [,2]        [,3]         [,4]         [,5]        [,6]
##  [1,]  0.13254933  0.06976093 -0.10138504 -0.381490607  0.563974080 -0.29041266
##  [2,] -0.03700583 -0.50538935  0.60302291  0.339417059  0.295429887  0.11731186
##  [3,]  0.17557043 -0.14552803 -0.26521723 -0.008786209 -0.399064503  0.45748267
##  [4,]  0.35079028 -0.05492723 -0.13105977 -0.011338461 -0.228623692 -0.01335748
##  [5,] -0.79519743 -0.09258790 -0.09225138 -0.348965045 -0.179198190 -0.05271741
##  [6,] -0.05359921 -0.06834056  0.12223761 -0.099552900 -0.004418229  0.41266568
##  [7,]  0.01136963  0.02590694  0.12456089  0.029514934 -0.237019731 -0.11053979
##  [8,] -0.03317872  0.81124839  0.27036020  0.264529034 -0.017123779  0.11675828
##  [9,] -0.02635616  0.13652737  0.24209348 -0.214127045 -0.040476307 -0.17120057
## [10,]  0.39319135 -0.04544415 -0.05723155 -0.388837507  0.104597440  0.03999745
## [11,]  0.06024086 -0.14977514 -0.12420381  0.367546055 -0.297738930 -0.66200783
## [12,] -0.17837453  0.01854873 -0.59092632  0.452090693  0.439661954  0.15601978
##              [,7]        [,8]        [,9]       [,10]       [,11]      [,12]
##  [1,]  0.20607293  0.15475878 -0.33015406  0.34594871  0.20226697 -0.2886751
##  [2,]  0.11195488  0.17059032  0.18449396  0.05758887  0.03174802 -0.2886751
##  [3,] -0.05421481  0.46846989 -0.10654360  0.43202108 -0.07284229 -0.2886751
##  [4,]  0.25753193 -0.01746251  0.17029887 -0.42612465  0.66605694 -0.2886751
##  [5,]  0.24165233  0.06973011  0.20672249 -0.06657784  0.01233785 -0.2886751
##  [6,]  0.03618495 -0.38372831 -0.66231241 -0.32815225 -0.13805671 -0.2886751
##  [7,] -0.19215553 -0.66419931  0.13802043  0.54765778  0.18444276 -0.2886751
##  [8,]  0.26067204  0.13379611  0.07570447  0.01642821 -0.09270142 -0.2886751
##  [9,] -0.79087853  0.27092303 -0.02233565 -0.23001230  0.09814282 -0.2886751
## [10,]  0.06796316 -0.15576480  0.46313904 -0.16104173 -0.56892680 -0.2886751
## [11,]  0.11545176  0.06927670 -0.26897963 -0.09444505 -0.33732375 -0.2886751
## [12,] -0.26023510 -0.11639002  0.15194610 -0.09329082  0.01485561 -0.2886751
## 
## $var.explained
##  [1] 9.370961e-01 3.006404e-02 1.821749e-02 5.805635e-03 2.884991e-03
##  [6] 2.557134e-03 1.071741e-03 8.422355e-04 7.348650e-04 5.130931e-04
## [11] 2.126446e-04 5.503737e-17
## 
## $dim.plot
## [1] 1 2
## 
## $distance.matrix.squared
##             AH0005      AH0015      AH0018    CC0003     CC0016       PM001
## AH0005  -7316729.3   2740971.8  -8156432.0 -16692561   37861109   2722000.1
## AH0015   2740971.8  -6398131.2   2678463.6   4988204  -10470585  -1545871.6
## AH0018  -8156432.0   2678463.6 -12363819.4 -22613043   50019981   3386185.1
## CC0003 -16692560.9   4988204.5 -22613043.5 -44747225  100466039   6864527.0
## CC0016  37861109.5 -10470585.2  50019980.8 100466039 -228797297 -15399139.4
## PM001    2722000.1  -1545871.6   3386185.1   6864527  -15399139  -1581447.2
## PM0020   -312147.4   -123707.1   -444710.7  -1323684    3375708    167660.9
## PM0027   1373046.1   2929184.4   3904495.3   4946381   -8304108   -193503.6
## PM0028   1182569.0   -356816.6   2367008.5   3691384   -7376408   -560360.7
## PM0032 -19139656.5   5470047.3 -25035285.4 -49872320  112480834   7568817.4
## PM008   -2565635.7    355830.2  -4138495.7  -7902145   17248125   1449728.8
## PM017    8303465.3   -267590.0  10395653.3  22194443  -51104259  -2878596.9
##            PM0020     PM0027     PM0028    PM0032      PM008       PM017
## AH0005  -312147.4  1373046.1  1182569.0 -19139657 -2565635.7   8303465.3
## AH0015  -123707.1  2929184.4  -356816.6   5470047   355830.2   -267590.0
## AH0018  -444710.7  3904495.3  2367008.5 -25035285 -4138495.7  10395653.3
## CC0003 -1323684.1  4946380.8  3691384.2 -49872320 -7902145.4  22194443.3
## CC0016  3375707.6 -8304108.5 -7376407.6 112480834 17248125.2 -51104258.9
## PM001    167660.9  -193503.6  -560360.7   7568817  1449728.8  -2878596.9
## PM0020  -465622.6  -306670.0  -139666.3  -1512927  -218616.1   1304383.2
## PM0027  -306670.0 -8739587.1 -1836966.0   5458527  2205667.6  -1436465.9
## PM0028  -139666.3 -1836966.0 -1304733.4   3771682  1100388.3   -538081.3
## PM0032 -1512927.3  5458526.9  3771681.9 -56314892 -8282822.1  25407995.9
## PM008   -218616.1  2205667.6  1100388.3  -8282822 -2549288.0   3297262.9
## PM017   1304383.2 -1436465.9  -538081.3  25407996  3297262.9 -14678210.8
## 
## $top
## [1] 500
## 
## $gene.selection
## [1] "pairwise"
## 
## $axislabel
## [1] "Leading logFC dim"
## 
## $x
##  [1]   1780.8603   -497.1901   2358.8684   4713.0265 -10683.8382   -720.1297
##  [7]    152.7562   -445.7712   -354.1070   5282.7042    809.3633  -2396.5427
## 
## $y
##  [1]   167.87892 -1216.21403  -350.21163  -132.18178  -222.81179  -164.46083
##  [7]    62.34477  1952.26052   328.55164  -109.36086  -360.43225    44.63732
## 
## 
## [[4]]
## An object of class MDS
## $eigen.values
##  [1]  9.439746e+08  1.384976e+07  5.807493e+06  3.337702e+06  1.330013e+06
##  [6]  5.507348e+05  2.728622e+05  1.836520e+05  1.164764e+05  5.233760e+04
## [11]  1.534511e+04 -7.806144e-09
## 
## $eigen.vectors
##              [,1]         [,2]        [,3]         [,4]        [,5]
##  [1,] -0.26472626 -0.170414555  0.26814178 -0.072268807  0.25387834
##  [2,] -0.20173024 -0.042368423  0.11450484 -0.005734423  0.02207535
##  [3,] -0.11681308  0.050090013  0.31172570 -0.040589527 -0.70549638
##  [4,]  0.53680059 -0.760521118 -0.15732652 -0.080967186 -0.10974061
##  [5,]  0.23344450  0.127603346  0.08956251  0.884534773  0.16981546
##  [6,] -0.01449653  0.309091943 -0.56178415  0.057551663 -0.34650307
##  [7,] -0.23539191 -0.106982165  0.15577710 -0.084633230  0.22285332
##  [8,] -0.13623204  0.062808885 -0.59855153 -0.164697187  0.32288500
##  [9,] -0.18844839 -0.020386399  0.11640330 -0.025837095  0.22792313
## [10,] -0.11847785  0.005327176 -0.05274178  0.036948322 -0.18564920
## [11,]  0.62796621  0.509186471  0.26007794 -0.388798397  0.18202391
## [12,] -0.12189499  0.036564826  0.05421081 -0.115508906 -0.05406525
##               [,6]        [,7]         [,8]         [,9]       [,10]
##  [1,] -0.148132804  0.07278886  0.072330582  0.185719103  0.54632772
##  [2,] -0.037747567  0.19769499  0.249965772  0.656696655 -0.56868480
##  [3,]  0.300846803 -0.32471294  0.295198785 -0.120286847  0.07551690
##  [4,] -0.066227514 -0.03606280 -0.011012285  0.004763446 -0.03027055
##  [5,]  0.130765865 -0.08005231 -0.005190058  0.016063837  0.05110008
##  [6,] -0.578668178 -0.06936489 -0.041838643  0.131352855  0.14445061
##  [7,] -0.198437665 -0.11050878  0.054726593 -0.055019425  0.28917778
##  [8,]  0.554860843 -0.16788901  0.250001311 -0.052231976  0.03391596
##  [9,] -0.328014663 -0.23261007  0.012918414 -0.587946537 -0.50623727
## [10,]  0.139789063  0.84310866 -0.072184572 -0.353889109  0.01843400
## [11,] -0.004360888  0.07223750  0.071053634  0.015600907  0.02409443
## [12,]  0.235326706 -0.16462921 -0.875969534  0.159177091 -0.07782486
##              [,11]      [,12]
##  [1,]  0.557251940 -0.2886751
##  [2,] -0.053860535 -0.2886751
##  [3,]  0.026560537 -0.2886751
##  [4,]  0.004714546 -0.2886751
##  [5,] -0.014931025 -0.2886751
##  [6,]  0.049013366 -0.2886751
##  [7,] -0.792138639 -0.2886751
##  [8,]  0.045632426 -0.2886751
##  [9,]  0.225270446 -0.2886751
## [10,] -0.053175887 -0.2886751
## [11,] -0.002643967 -0.2886751
## [12,]  0.008306792 -0.2886751
## 
## $var.explained
##  [1] 9.736806e-01 1.428560e-02 5.990249e-03 3.442737e-03 1.371867e-03
##  [6] 5.680659e-04 2.814489e-04 1.894314e-04 1.201418e-04 5.398462e-05
## [11] 1.582800e-05 0.000000e+00
## 
## $dim.plot
## [1] 1 2
## 
## $distance.matrix.squared
##            AH0005     AH0015    AH0018     CC0003     CC0016    PM001
## AH0005 -134231113 -101412618 -58604962  265214860  117329895 -3880091
## AH0015 -101412618  -77214044 -44761567  203763645   88978480 -4412794
## AH0018  -58604962  -44761567 -28487977  119798550   51483317 -2040002
## CC0003  265214860  203763645 119798550 -560411929 -233197348 20062836
## CC0016  117329895   88978480  51483317 -233197348 -108752738  5776676
## PM001    -3880091   -4412794  -2040002   20062836    5776676 -7428674
## PM0020 -118857997  -89976617 -51895909  236593397  104381895 -4403363
## PM0027  -66134129  -51012033 -27645383  138337620   61182265 -7458821
## PM0028  -94803814  -71894447 -41478903  190789830   83097805 -4195871
## PM0032  -58875003  -45057714 -26182697  120080220   52136395 -3687116
## PM008   315126660  239375638 137084788 -625367523 -276617479 14841518
## PM017   -60871686  -46375928 -27269255  124335842   54200839 -3174297
##            PM0020    PM0027    PM0028    PM0032      PM008     PM017
## AH0005 -118857997 -66134129 -94803814 -58875003  315126660 -60871686
## AH0015  -89976617 -51012033 -71894447 -45057714  239375638 -46375928
## AH0018  -51895909 -27645383 -41478903 -26182697  137084788 -27269255
## CC0003  236593397 138337620 190789830 120080220 -625367523 124335842
## CC0016  104381895  61182265  83097805  52136395 -276617479  54200839
## PM001    -4403363  -7458821  -4195871  -3687116   14841518  -3174297
## PM0020 -105468695 -59452401 -84241422 -52333894  279785339 -54130333
## PM0027  -59452401 -40145931 -47675913 -30654125  161853700 -31194848
## PM0028  -84241422 -47675913 -67614903 -41848175  223187625 -43321812
## PM0032  -52333894 -30654125 -41848175 -27075685  140704108 -27206314
## PM008   279785339 161853700 223187625 140704108 -753566229 143591855
## PM017   -54130333 -31194848 -43321812 -27206314  143591855 -28584062
## 
## $top
## [1] 500
## 
## $gene.selection
## [1] "pairwise"
## 
## $axislabel
## [1] "Leading logFC dim"
## 
## $x
##  [1] -8133.4945 -6197.9943 -3588.9850 16492.7525  7172.3884  -445.3939
##  [7] -7232.2209 -4185.6162 -5789.9205 -3640.1335 19293.7406 -3745.1225
## 
## $y
##  [1]  -634.20223  -157.67520   186.41130 -2830.29928   474.87920  1150.29377
##  [7]  -398.13693   233.74491   -75.86852    19.82523  1894.95080   136.07696
## 
## 
## [[5]]
## An object of class MDS
## $eigen.values
##  [1]  1.503535e+08  3.450944e+06  1.802376e+06  1.290274e+06  6.648787e+05
##  [6]  5.266068e+05  2.197101e+05  1.696696e+05  1.379904e+05  7.954587e+04
## [11]  5.173911e+04 -3.114308e-09
## 
## $eigen.vectors
##              [,1]        [,2]        [,3]        [,4]        [,5]        [,6]
##  [1,] -0.33642554 -0.18413004 -0.09263789  0.05915310  0.17039263  0.14877149
##  [2,] -0.21882848 -0.19882050 -0.14249871 -0.10604865  0.06082605 -0.07252855
##  [3,]  0.73404401 -0.36117409  0.18650217 -0.34300841  0.17218374  0.18601028
##  [4,] -0.20495868 -0.21431290  0.30411552  0.40811084  0.28445070  0.10746483
##  [5,]  0.07970053 -0.07319032 -0.26357421  0.17446638 -0.75367397  0.34019076
##  [6,] -0.03748048  0.04877688 -0.08369392 -0.34576872 -0.23924550 -0.22768980
##  [7,]  0.23772411  0.33267056 -0.52885006  0.33343404  0.37544129  0.29587800
##  [8,] -0.09356534  0.72523465  0.43485159 -0.25106930 -0.02971623  0.27345422
##  [9,]  0.02610530  0.18419739 -0.37641522 -0.19222193  0.13500207 -0.58682884
## [10,] -0.33589801 -0.24835178  0.01754668 -0.18781847 -0.07720061  0.08891278
## [11,]  0.24854782  0.06579653  0.33662474  0.54398754 -0.22325928 -0.49016616
## [12,] -0.09896525 -0.07669637  0.20802929 -0.09321639  0.12479910 -0.06346902
##              [,7]        [,8]        [,9]        [,10]       [,11]     [,12]
##  [1,] -0.03746002  0.59247649 -0.27881784 -0.195758173  0.48768934 0.2886751
##  [2,] -0.15561458  0.06676875  0.02615686  0.859558929 -0.14331461 0.2886751
##  [3,]  0.07385915  0.14583618  0.05898499  0.002492127  0.02248626 0.2886751
##  [4,]  0.22219896 -0.32896306  0.53097702 -0.027319243  0.19223679 0.2886751
##  [5,]  0.30919518 -0.10928185 -0.03503622  0.048615635  0.10078878 0.2886751
##  [6,] -0.60211703 -0.25887523  0.28869329 -0.192198137  0.35692537 0.2886751
##  [7,] -0.27808577 -0.11782618 -0.04191641 -0.076996088 -0.17670181 0.2886751
##  [8,]  0.11307303  0.15286233  0.06314419  0.117382095 -0.01538789 0.2886751
##  [9,]  0.56336729  0.03895518  0.11247917 -0.093495746  0.02306396 0.2886751
## [10,] -0.05768790  0.12011649  0.10827870 -0.389519789 -0.71519627 0.2886751
## [11,] -0.21539880  0.25960611 -0.12103020 -0.001732668 -0.15090378 0.2886751
## [12,]  0.06467051 -0.56167522 -0.71191357 -0.051028940  0.01831386 0.2886751
## 
## $var.explained
##  [1] 0.9471251725 0.0217386024 0.0113537450 0.0081278517 0.0041882841
##  [6] 0.0033172650 0.0013840244 0.0010688030 0.0008692456 0.0005010849
## [11] 0.0003259212 0.0000000000
## 
## $dim.plot
## [1] 1 2
## 
## $distance.matrix.squared
##           AH0005    AH0015     AH0018    CC0003      CC0016       PM001
## AH0005 -34542452 -22404357   73822820 -20949044   7993541.4 -3574676.26
## AH0015 -22404357 -14917150   47810408 -13502536   5162140.4 -2540877.70
## AH0018  73822820  47810408 -163442990  44777341 -17341688.5  8271234.24
## CC0003 -20949044 -13502536   44777341 -13972243   5116617.0 -1686054.42
## CC0016   7993541   5162140  -17341688   5116617  -3201141.8   913638.74
## PM001   -3574676  -2540878    8271234  -1686054    913638.7 -1127446.58
## PM0020  24135738  15903220  -51121363  15220036  -5876254.7  2819252.41
## PM0027  -8416084  -4996224   21887115  -4928348   2997395.9 -1292929.17
## PM0028   2846067   1718828   -5158205   2443830   -531674.5   -10833.17
## PM0032 -34251635 -22438123   73342626 -20856184   7940663.2 -3866543.36
## PM008   25327363  16726210  -54299687  14684692  -5857485.5  3158493.75
## PM017   -9987281  -6521539   21452390  -6348107   2684248.4 -1063258.49
##           PM0020     PM0027      PM0028    PM0032     PM008     PM017
## AH0005  24135738 -8416084.1  2846067.35 -34251635  25327363  -9987281
## AH0015  15903220 -4996224.3  1718827.99 -22438123  16726210  -6521539
## AH0018 -51121363 21887115.3 -5158205.38  73342626 -54299687  21452390
## CC0003  15220036 -4928348.3  2443830.42 -20856184  14684692  -6348107
## CC0016  -5876255  2997395.9  -531674.48   7940663  -5857486   2684248
## PM001    2819252 -1292929.2   -10833.17  -3866543   3158494  -1063258
## PM0020 -19375693  6019827.6 -2654818.34  24769057 -17500845   7661843
## PM0027   6019828 -7203791.3   422152.46  -8384590   6620001  -2724526
## PM0028  -2654818   422152.5 -1577046.14   2957248  -1517049   1061500
## PM0032  24769057 -8384589.9  2957248.27 -34548772  25459475 -10123221
## PM008  -17500845  6620001.4 -1517048.96  25459475 -20147471   7346303
## PM017    7661843 -2724525.7  1061499.96 -10123221   7346303  -3438351
## 
## $top
## [1] 500
## 
## $gene.selection
## [1] "pairwise"
## 
## $axislabel
## [1] "Leading logFC dim"
## 
## $x
##  [1] -4125.2073 -2683.2471  9000.7547 -2513.1774   977.2778  -459.5809
##  [7]  2914.9430 -1147.2864   320.0999 -4118.7388  3047.6619 -1213.4994
## 
## $y
##  [1] -342.05314 -369.34320 -670.94285 -398.12300 -135.96358   90.61143
##  [7]  617.99265 1347.24782  342.17826 -461.35605  122.22834 -142.47667
## 
## 
## [[6]]
## An object of class MDS
## $eigen.values
##  [1]  1.624726e+07  2.072002e+06  1.645040e+05  2.925670e+04  2.155713e+04
##  [6]  1.109050e+04  6.653763e+03  5.443311e+03  3.324356e+03  1.324308e+03
## [11]  3.782650e+02 -8.571726e-10
## 
## $eigen.vectors
##              [,1]        [,2]        [,3]        [,4]        [,5]        [,6]
##  [1,]  0.37157376  0.08677345  0.42852207 -0.12182975  0.14431841  0.20488888
##  [2,] -0.72252264 -0.18234450  0.35760872 -0.11125725  0.40137935 -0.17669701
##  [3,]  0.14393844 -0.05997944 -0.39396197 -0.01946928  0.03816285 -0.24424533
##  [4,] -0.08331470 -0.07496685 -0.58146735  0.16559247  0.31183676  0.38931016
##  [5,] -0.03924526 -0.15020547  0.16647570  0.71775492 -0.32490009 -0.11374361
##  [6,] -0.25499042 -0.16026624 -0.03687776 -0.43863959 -0.61710625  0.47189350
##  [7,] -0.05124569 -0.14233535 -0.15765020  0.10969210 -0.30090745 -0.34739067
##  [8,]  0.23101187 -0.04760474 -0.08843917 -0.46222290 -0.01431352 -0.54923326
##  [9,]  0.07983698 -0.10555497  0.08769768  0.08815619  0.10999634  0.08381449
## [10,]  0.34449873 -0.02689452  0.34177840  0.04110713 -0.02400581  0.13344227
## [11,] -0.18926594  0.92900927 -0.03661573  0.04534740 -0.07624131 -0.03052837
## [12,]  0.16972487 -0.06563064 -0.08707039 -0.01423144  0.35178071  0.17848895
##              [,7]         [,8]        [,9]       [,10]       [,11]     [,12]
##  [1,]  0.10604965  0.154172783 -0.01343462 -0.44657944 -0.52455658 0.2886751
##  [2,]  0.10392153  0.080940459 -0.09567866 -0.02211141  0.04236079 0.2886751
##  [3,]  0.27394470  0.252853155 -0.61442585  0.30084672 -0.26200247 0.2886751
##  [4,]  0.16792577 -0.185611406  0.06716224 -0.44179426  0.16562608 0.2886751
##  [5,]  0.34075567 -0.146538933  0.24546903  0.11806322 -0.13927948 0.2886751
##  [6,]  0.07102842 -0.002888953  0.02822330  0.13632533 -0.06525708 0.2886751
##  [7,] -0.56259267  0.404492838  0.04554268 -0.39832016  0.06936583 0.2886751
##  [8,]  0.22311259 -0.388765583  0.35060624 -0.05794957  0.10246768 0.2886751
##  [9,] -0.58194368 -0.622477560 -0.27214149  0.20200877 -0.15348979 0.2886751
## [10,]  0.09491110  0.106123346 -0.27764463  0.02487189  0.74993543 0.2886751
## [11,] -0.04278733 -0.020919003  0.01555772  0.06045865  0.03846264 0.2886751
## [12,] -0.19432574  0.368618857  0.52076403  0.52418026 -0.02363304 0.2886751
## 
## $var.explained
##  [1] 8.752594e-01 1.116213e-01 8.862026e-03 1.576093e-03 1.161309e-03
##  [6] 5.974585e-04 3.584462e-04 2.932377e-04 1.790871e-04 7.134206e-05
## [11] 2.037759e-05 0.000000e+00
## 
## $dim.plot
## [1] 1 2
## 
## $distance.matrix.squared
##            AH0005    AH0015     AH0018     CC0003      CC0016      PM001
## AH0005 -4581884.9   8736181 -1660697.2  1111982.1   511919.42  3140222.5
## AH0015  8736180.7 -17151906  3377708.6 -1947148.7 -1044813.98 -6093830.0
## AH0018 -1660697.2   3377709  -745107.1   298021.8   168580.27  1150843.8
## CC0003  1111982.1  -1947149   298021.8  -370560.7  -123678.62  -738709.4
## CC0016   511919.4  -1044814   168580.3  -123678.6  -189875.39  -412370.1
## PM001   3140222.5  -6093830  1150843.8  -738709.4  -412370.08 -2252425.9
## PM0020   696063.6  -1287168   184067.3  -205551.9  -151757.93  -521926.3
## PM0027 -2760044.6   5393472 -1105509.6   601596.2   285462.65  1874668.2
## PM0028  -936785.3   1784531  -385378.1   197488.5    31366.78   597285.8
## PM0032 -4198204.8   8028397 -1574511.8   988514.6   402400.82  2839742.8
## PM008   1957438.1  -3735704  1111648.8  -229828.7   335977.75  -952194.4
## PM017  -2016189.6   3940282  -819666.8   417874.9   186788.30  1368693.2
##             PM0020     PM0027     PM0028     PM0032      PM008      PM017
## AH0005   696063.62 -2760044.6 -936785.31 -4198204.8  1957438.1 -2016189.6
## AH0015 -1287168.04  5393472.3 1784530.93  8028396.9 -3735704.4  3940282.1
## AH0018   184067.34 -1105509.6 -385378.07 -1574511.8  1111648.8  -819666.8
## CC0003  -205551.94   601596.2  197488.48   988514.6  -229828.7   417874.9
## CC0016  -151757.93   285462.6   31366.78   402400.8   335977.8   186788.3
## PM001   -521926.26  1874668.2  597285.76  2839742.8  -952194.4  1368693.2
## PM0020  -191181.90   353774.2   75428.08   576291.8   229213.1   242747.8
## PM0027   353774.25 -1768426.0 -614333.47 -2577898.8  1603767.6 -1286528.7
## PM0028    75428.08  -614333.5 -266295.99  -914667.0   898141.3  -466781.6
## PM0032   576291.84 -2577898.8 -914666.97 -3899559.5  2226343.4 -1896848.5
## PM008    229213.09  1603767.6  898141.35  2226343.4 -4741396.8  1296594.1
## PM017    242747.84 -1286528.7 -466781.57 -1896848.5  1296594.1  -966965.4
## 
## $top
## [1] 500
## 
## $gene.selection
## [1] "pairwise"
## 
## $axislabel
## [1] "Leading logFC dim"
## 
## $x
##  [1]  1497.7354 -2912.3362   580.1855  -335.8240  -158.1893 -1027.8125
##  [7]  -206.5606   931.1601   321.8060  1388.6016  -762.8910   684.1251
## 
## $y
##  [1]  124.90562 -262.47490  -86.33711 -107.91066 -216.21253 -230.69445
##  [7] -204.88392  -68.52440 -151.94059  -38.71319 1337.25784  -94.47170
## 
## 
## [[7]]
## An object of class MDS
## $eigen.values
##  [1]  1.348388e+08  4.588740e+06  1.015494e+06  4.645206e+05  2.400461e+05
##  [6]  1.979490e+05  1.155072e+05  6.704047e+04  5.946306e+04  5.324812e+04
## [11]  3.721121e+04 -2.798162e-09
## 
## $eigen.vectors
##              [,1]        [,2]          [,3]        [,4]        [,5]        [,6]
##  [1,] -0.21656107  0.28974345  0.2457217173  0.03541434 -0.21228703  0.38997880
##  [2,] -0.05768345 -0.14532756  0.2934475272  0.06262777 -0.52176369 -0.03525894
##  [3,] -0.12749814 -0.06133246 -0.0661449295 -0.49275087  0.16117016 -0.68905364
##  [4,] -0.17104655  0.23725892 -0.3562205821 -0.20817516  0.04283207  0.26554714
##  [5,] -0.05757781 -0.07533413  0.0374990753  0.26832737  0.49371928  0.05064320
##  [6,]  0.20409763 -0.59718046  0.2214329018 -0.36116803  0.27120580  0.41591254
##  [7,] -0.26111394  0.36747378  0.0127718438  0.16288319  0.43104787  0.03750373
##  [8,]  0.86880857  0.39545336  0.0001832576 -0.02107581 -0.01334379 -0.05149579
##  [9,]  0.04560154 -0.26530049  0.0948432437  0.65986450  0.01046348 -0.30551608
## [10,] -0.11358371  0.02335053  0.3655950964 -0.13944649 -0.16459471 -0.10183638
## [11,]  0.02949311 -0.30346634 -0.7148007676  0.12592525 -0.24005972  0.10604319
## [12,] -0.14293618  0.13466139 -0.1343283840 -0.09242606 -0.25838972 -0.08246778
##              [,7]        [,8]         [,9]       [,10]       [,11]      [,12]
##  [1,]  0.23027389 -0.27062783  0.220659257 -0.08762147  0.58685654 -0.2886751
##  [2,] -0.30478331  0.08610737 -0.616438254 -0.21857323 -0.02504464 -0.2886751
##  [3,]  0.10581112 -0.08151260 -0.053534711 -0.11536226  0.33865202 -0.2886751
##  [4,]  0.13188425  0.67191718 -0.214630056  0.26527007  0.05690033 -0.2886751
##  [5,] -0.70419611  0.03755267  0.085055235  0.11345849  0.26558882 -0.2886751
##  [6,]  0.21263019 -0.13147723 -0.079711124  0.09415095 -0.12107642 -0.2886751
##  [7,]  0.19494529 -0.23978292 -0.251797189 -0.36977948 -0.45156190 -0.2886751
##  [8,] -0.01895670 -0.01186401 -0.006291623 -0.03852416  0.01253275 -0.2886751
##  [9,]  0.45939201  0.16757847  0.018951690  0.25138038  0.06051475 -0.2886751
## [10,] -0.10876279  0.36234254  0.626544566 -0.17274923 -0.38357678 -0.2886751
## [11,] -0.05725398 -0.13490838  0.224088512 -0.39427588 -0.02977328 -0.2886751
## [12,] -0.14098387 -0.45532526  0.047103696  0.67262582 -0.31001218 -0.2886751
## 
## $var.explained
##  [1] 0.9517270217 0.0323885098 0.0071676192 0.0032787068 0.0016943072
##  [6] 0.0013971754 0.0008152800 0.0004731889 0.0004197057 0.0003758390
## [11] 0.0002626463 0.0000000000
## 
## $dim.plot
## [1] 1 2
## 
## $distance.matrix.squared
##           AH0005     AH0015     AH0018    CC0003     CC0016     PM001    PM0020
## AH0005 -13677937 -3144038.9 -7134030.1 -10449323 -3121310.2  13364591 -16196428
## AH0015  -3144039 -1473655.2 -1963896.2  -2113421  -950377.2   2353840  -3491761
## AH0018  -7134030 -1963896.2 -4867013.3  -5817063 -1905771.8   6635379  -8720132
## CC0003 -10449323 -2113420.6 -5817062.9  -8811096 -2412418.9  10757055 -12795134
## CC0016  -3121310  -950377.2 -1905771.8  -2412419 -1256137.3   2794099  -3896009
## PM001   13364591  2353840.5  6635378.9  10757055  2794099.1 -14846680  16355897
## PM0020 -16196428 -3491760.8 -8720132.1 -12795134 -3896008.7  16355897 -19794554
## PM0027  49695339 14037083.4 30072042.2  39219087 13770477.0 -45648155  59850764
## PM0028   3328711   296734.3  1641555.0   2873330   426043.5  -3757649   4003082
## PM0032  -6856854 -1958830.5 -3903864.1  -5045379 -1715733.0   6222622  -8014117
## PM008    2831035   418801.4   849640.8   1553102   320417.0  -2902740   3136253
## PM017   -8639756 -2010480.2 -4886846.4  -6958741 -2053279.5   8671741 -10437862
##            PM0027     PM0028   PM0032      PM008     PM017
## AH0005   49695339  3328711.1 -6856854  2831034.8  -8639756
## AH0015   14037083   296734.3 -1958830   418801.4  -2010480
## AH0018   30072042  1641555.0 -3903864   849640.8  -4886846
## CC0003   39219087  2873329.9 -5045379  1553101.8  -6958741
## CC0016   13770477   426043.5 -1715733   320417.0  -2053280
## PM001   -45648155 -3757649.0  6222622 -2902740.3   8671741
## PM0020   59850764  4003081.7 -8014117  3136253.0 -10437862
## PM0027 -204997307 -9711521.6 26521933 -5807353.8  32997612
## PM0028   -9711522 -1726108.5  1465587 -1007759.2   2167995
## PM0032   26521933  1465587.5 -3871973  1481123.0  -4324515
## PM008    -5807354 -1007759.2  1481123 -2190103.0   1317585
## PM017    32997612  2167995.5 -4324515  1317584.6  -5843454
## 
## $top
## [1] 500
## 
## $gene.selection
## [1] "pairwise"
## 
## $axislabel
## [1] "Leading logFC dim"
## 
## $x
##  [1] -2514.7094  -669.8208 -1480.5098 -1986.1943  -668.5941  2369.9838
##  [7] -3032.0579 10088.6143   529.5256 -1318.9352   342.4743 -1659.7764
## 
## $y
##  [1]   620.66940  -311.31115  -131.38236   508.24048  -161.37584 -1279.24077
##  [7]   787.17820   847.11422  -568.30928    50.01998  -650.06567   288.46279
## 
## 
## [[8]]
## An object of class MDS
## $eigen.values
##  [1] 1.208371e+07 2.559413e+05 1.334695e+05 5.803010e+04 3.156240e+04
##  [6] 2.746692e+04 2.275065e+04 1.096778e+04 3.602062e+03 2.798498e+03
## [11] 1.764331e+03 3.386159e-10
## 
## $eigen.vectors
##              [,1]        [,2]         [,3]        [,4]        [,5]        [,6]
##  [1,]  0.40228618 -0.02776566 -0.037890160 -0.02865201 -0.05588595  0.12740053
##  [2,]  0.34684339 -0.04486574 -0.076054464  0.04308398  0.04480435 -0.01414464
##  [3,]  0.38035922 -0.19604628 -0.013813082 -0.05169892 -0.01389678  0.15624625
##  [4,] -0.35406178 -0.17350798 -0.181253130  0.53837284 -0.49042516  0.41674314
##  [5,] -0.01090287 -0.07326353  0.119111836  0.10582489  0.15899783 -0.23666285
##  [6,] -0.16349354  0.03499592  0.577000908  0.09253604  0.55296172  0.41748102
##  [7,]  0.32912902 -0.07036515 -0.140592531 -0.01319490 -0.03746638  0.06050215
##  [8,] -0.13393869  0.80737412 -0.191125152 -0.33850304 -0.11137747  0.24132766
##  [9,]  0.06924709  0.02615668  0.006223225 -0.10159593 -0.13119906 -0.40812729
## [10,] -0.16895123  0.09404652  0.569322420  0.02383472 -0.41854719 -0.41781498
## [11,] -0.44523937 -0.49233499 -0.186777720 -0.64634648  0.03783911  0.04455359
## [12,] -0.25127741  0.11557608 -0.444152150  0.37633881  0.46419498 -0.38750456
##              [,7]         [,8]        [,9]         [,10]       [,11]      [,12]
##  [1,]  0.19282070  0.100108844  0.12077578  8.936055e-02  0.81404983 -0.2886751
##  [2,]  0.04661615  0.001573572 -0.22563273  7.864107e-01 -0.33612178 -0.2886751
##  [3,]  0.05521746 -0.080402005  0.68478268 -2.579926e-01 -0.40135873 -0.2886751
##  [4,] -0.12352274 -0.083662532 -0.01869846  3.786864e-02  0.01646988 -0.2886751
##  [5,] -0.71153517  0.538050213  0.07157003 -2.879003e-02  0.05265575 -0.2886751
##  [6,]  0.02988155 -0.228445128 -0.11273292 -2.883938e-02  0.02326533 -0.2886751
##  [7,]  0.13098973  0.161712620 -0.64378333 -5.358289e-01 -0.18304667 -0.2886751
##  [8,] -0.08865230  0.081082481  0.07125521  3.485128e-03 -0.07479278 -0.2886751
##  [9,] -0.35768868 -0.748534010 -0.09243394 -8.316673e-02  0.11550197 -0.2886751
## [10,]  0.39940977  0.193433765  0.04473844 -1.308521e-05 -0.07670636 -0.2886751
## [11,]  0.08725042  0.066183787 -0.02834035  7.847867e-02  0.03183261 -0.2886751
## [12,]  0.33921312 -0.001101607  0.12849958 -6.097301e-02  0.01825095 -0.2886751
## 
## $var.explained
##  [1] 9.565904e-01 2.026124e-02 1.056592e-02 4.593872e-03 2.498593e-03
##  [6] 2.174380e-03 1.801023e-03 8.682493e-04 2.851522e-04 2.215392e-04
## [11] 1.396708e-04 2.680606e-17
## 
## $dim.plot
## [1] 1 2
## 
## $distance.matrix.squared
##            AH0005      AH0015      AH0018      CC0003      CC0016       PM001
## AH0005 -3917477.3 -3372739.05 -3701799.20  3436329.11  113594.817  1595475.72
## AH0015 -3372739.1 -2914603.06 -3190995.22  2959298.18   92760.356  1381185.41
## AH0018 -3701799.2 -3190995.22 -3522365.53  3236119.45   98531.886  1506048.24
## CC0003  3436329.1  2959298.18  3236119.45 -3113024.56  -93308.329 -1366432.52
## CC0016   113594.8    92760.36    98531.89   -93308.33  -44818.126   -57659.35
## PM001   1595475.7  1381185.41  1506048.24 -1366432.52  -57659.345  -766650.76
## PM0020 -3203037.5 -2762302.22 -3031566.05  2802577.01   92491.596  1323475.64
## PM0027  1309267.0  1139770.11  1307235.80 -1071779.56    5626.634  -511666.31
## PM0028  -666173.1  -578024.39  -631036.09   603399.33   13554.794   283851.28
## PM0032  1647455.6  1429788.11  1566958.48 -1412048.13  -49977.122  -732532.60
## PM008   4316485.0  3719655.15  4038648.17 -3821444.59 -119640.159 -1716851.02
## PM017   2442618.8  2096206.64  2324220.06 -2159685.39  -51157.001  -938243.75
##            PM0020       PM0027     PM0028      PM0032      PM008      PM017
## AH0005 -3203037.5  1309266.985 -666173.05  1647455.65  4316485.0  2442618.8
## AH0015 -2762302.2  1139770.107 -578024.39  1429788.11  3719655.1  2096206.6
## AH0018 -3031566.1  1307235.802 -631036.09  1566958.48  4038648.2  2324220.1
## CC0003  2802577.0 -1071779.564  603399.33 -1412048.13 -3821444.6 -2159685.4
## CC0016    92491.6     5626.634   13554.79   -49977.12  -119640.2   -51157.0
## PM001   1323475.6  -511666.307  283851.28  -732532.60 -1716851.0  -938243.8
## PM0020 -2632144.8  1086232.622 -544555.43  1366152.55  3515102.2  1987574.4
## PM0027  1086232.6  -794815.738  214121.89  -551952.22 -1272737.6  -859302.6
## PM0028  -544555.4   214121.890 -145941.35   277723.88   748223.5   424855.7
## PM0032  1366152.6  -551952.221  277723.88  -809726.14 -1763915.9  -967926.5
## PM008   3515102.2 -1272737.625  748223.46 -1763915.94 -4973466.0 -2670058.7
## PM017   1987574.4  -859302.585  424855.68  -967926.52 -2670058.7 -1629101.6
## 
## $top
## [1] 500
## 
## $gene.selection
## [1] "pairwise"
## 
## $axislabel
## [1] "Leading logFC dim"
## 
## $x
##  [1]  1398.41261  1205.68439  1322.19092 -1230.77672   -37.90017  -568.33030
##  [7]  1144.10636  -465.59282   240.71421  -587.30214 -1547.72493  -873.48141
## 
## $y
##  [1]  -14.04682  -22.69786  -99.18107  -87.77880  -37.06449   17.70466
##  [7]  -35.59818  408.45574   13.23283   47.57874 -249.07543   58.47068
## 
## 
## [[9]]
## An object of class MDS
## $eigen.values
##  [1]  2.705127e+07  1.874315e+06  8.069168e+05  1.686456e+05  6.331511e+04
##  [6]  3.321878e+04  2.150973e+04  1.587891e+04  1.035439e+04  4.486565e+03
## [11]  3.341495e+03 -1.159923e-09
## 
## $eigen.vectors
##              [,1]         [,2]         [,3]        [,4]         [,5]
##  [1,]  0.16231973  0.285341234  0.175381387  0.01090552  0.332647027
##  [2,] -0.90872962  0.217840186  0.007105863 -0.10839728  0.048366303
##  [3,]  0.16277096 -0.005801096 -0.110885781 -0.12662349 -0.107229356
##  [4,]  0.11765224  0.100122499 -0.107796661  0.14580899  0.341270474
##  [5,]  0.17821126  0.050356747 -0.031076250 -0.01547422 -0.012617805
##  [6,]  0.09049539 -0.226406478 -0.518434363 -0.68516849 -0.053589406
##  [7,]  0.07324513  0.243753384 -0.046337219  0.05770197  0.425283020
##  [8,]  0.18862836  0.106668473  0.084965086  0.17472089 -0.003963641
##  [9,] -0.04696411 -0.674541106  0.602418798 -0.09456650  0.148620700
## [10,]  0.04129980  0.100211250  0.318141810 -0.16219147 -0.410770780
## [11,] -0.13189638 -0.453679763 -0.443939411  0.61306999 -0.095487339
## [12,]  0.07296723  0.256134671  0.070456742  0.19021410 -0.612529196
##               [,6]         [,7]        [,8]         [,9]        [,10]
##  [1,] -0.073982845 -0.599370484 -0.12927635  0.136938577  0.398370764
##  [2,]  0.061080085  0.035595589 -0.04189178  0.147455161 -0.025842435
##  [3,] -0.084545147  0.078696806 -0.72359793  0.184170005  0.103640562
##  [4,]  0.506773598  0.541839553 -0.07606035 -0.289452048  0.272549911
##  [5,]  0.093165155  0.046165612 -0.21552040  0.246134078 -0.742102217
##  [6,] -0.040031881 -0.040512134  0.28529356  0.005682509  0.136993620
##  [7,] -0.549407564  0.077739507  0.21145092 -0.373160857 -0.293289925
##  [8,]  0.293347633 -0.002006901  0.51424333  0.554440249 -0.029517633
##  [9,] -0.165767581  0.164635917  0.02673382  0.047108676  0.064942654
## [10,]  0.368282447 -0.320925214  0.04546348 -0.560533134 -0.154307372
## [11,] -0.002060733 -0.306858069 -0.00779765 -0.130757289  0.001722081
## [12,] -0.406853167  0.324999817  0.11095935  0.031974073  0.266839991
##              [,11]      [,12]
##  [1,] -0.329371362 -0.2886751
##  [2,]  0.010928734 -0.2886751
##  [3,]  0.518378566 -0.2886751
##  [4,] -0.170861014 -0.2886751
##  [5,] -0.460922422 -0.2886751
##  [6,] -0.112604358 -0.2886751
##  [7,]  0.296094920 -0.2886751
##  [8,]  0.416133792 -0.2886751
##  [9,] -0.061486926 -0.2886751
## [10,]  0.173106146 -0.2886751
## [11,] -0.007904589 -0.2886751
## [12,] -0.271491487 -0.2886751
## 
## $var.explained
##  [1] 0.9001112186 0.0623664710 0.0268495675 0.0056115604 0.0021067642
##  [6] 0.0011053306 0.0007157205 0.0005283593 0.0003445349 0.0001492872
## [11] 0.0001111858 0.0000000000
## 
## $dim.plot
## [1] 1 2
## 
## $distance.matrix.squared
##            AH0005    AH0015     AH0018     CC0003     CC0016       PM001
## AH0005 -1813266.9   7744465 -1387976.2 -1109087.2 -1607810.8  -401859.48
## AH0015  7744464.7 -44860351  8003284.9  5705011.2  8718801.9  4615901.06
## AH0018 -1387976.2   8003285 -1480210.8 -1041628.5 -1577921.4  -917910.64
## CC0003 -1109087.2   5705011 -1041628.5  -859613.0 -1159334.7  -542679.15
## CC0016 -1607810.8   8718802 -1577921.4 -1159334.7 -1739180.8  -856634.09
## PM001   -401859.5   4615901  -917910.6  -542679.1  -856634.1 -1230702.11
## PM0020  -906041.0   3405494  -637592.4  -570974.6  -748007.8  -176895.53
## PM0027 -1792187.1   9189932 -1626246.4 -1239065.6 -1833696.2  -725158.62
## PM0028   960569.9  -1769083   503794.0   657000.2   610986.7   140095.19
## PM0032  -545782.9   1942313  -311211.4  -227016.6  -401631.9   109148.31
## PM008   1763386.5  -6085195  1098472.2   912985.7  1339348.6    29865.87
## PM017   -904409.4   3389426  -624853.3  -525597.6  -744919.5   -43170.83
##           PM0020     PM0027      PM0028     PM0032       PM008      PM017
## AH0005 -906041.0 -1792187.1   960569.90 -545782.94  1763386.47 -904409.37
## AH0015 3405494.3  9189932.1 -1769083.17 1942312.71 -6085194.87 3389426.05
## AH0018 -637592.4 -1626246.4   503793.97 -311211.44  1098472.25 -624853.33
## CC0003 -570974.6 -1239065.6   657000.15 -227016.63   912985.67 -525597.64
## CC0016 -748007.8 -1833696.2   610986.66 -401631.92  1339348.64 -744919.50
## PM001  -176895.5  -725158.6   140095.19  109148.31    29865.87  -43170.83
## PM0020 -566446.2  -831142.8   835224.70 -197028.38   897243.57 -503833.94
## PM0027 -831142.8 -2011251.9   674503.86 -497851.77  1553807.44 -861642.97
## PM0028  835224.7   674503.9 -2419588.77   58589.35 -1027077.66  774985.81
## PM0032 -197028.4  -497851.8    58589.35 -343936.43   715960.85 -301551.71
## PM008   897243.6  1553807.4 -1027077.66  715960.85 -2163158.67  964360.46
## PM017  -503833.9  -861643.0   774985.81 -301551.71   964360.46 -618793.04
## 
## $top
## [1] 500
## 
## $gene.selection
## [1] "pairwise"
## 
## $axislabel
## [1] "Leading logFC dim"
## 
## $x
##  [1]   844.2384 -4726.3784   846.5853   611.9191   926.8916   470.6741
##  [7]   380.9540   981.0718  -244.2643   214.8037  -686.0040   379.5087
## 
## $y
##  [1]  390.648211  298.235477   -7.942027  137.073337   68.941222 -309.963213
##  [7]  333.712103  146.035145 -923.484746  137.194842 -621.113134  350.662782

plotMDS(assays(pb)[[1]],cex=1.5,labels=patient_id,col=hiv_status+1,main="MDS red=HIV+")

Differential expression without batch correction.

rownames(pat_df) <- gsub("-","",gsub("\\+","",pat_df[,1]))

deres <- lapply(1:length(assays(pb)), function(i) {
  counts <- assays(pb)[[i]]
  cellname <- names(assays(pb))[i]
  fcounts <- counts[which(rowMeans(counts)>=5),]
  fcounts <- fcounts+1
  dds <- DESeqDataSetFromMatrix(countData = fcounts , colData = pat_df, design = ~ hiv_status)
  res <- DESeq(dds)
  z <- results(res)
  vsd <- vst(dds, blind=FALSE)
  zz<-cbind(as.data.frame(z),assay(vsd),fcounts)
  de <- as.data.frame(zz[order(zz$pvalue),])
  return(de)
})

## converting counts to integer mode

##   the design formula contains one or more numeric variables with integer values,
##   specifying a model with increasing fold change for higher values.
##   did you mean for this to be a factor? if so, first convert
##   this variable to a factor using the factor() function

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## -- replacing outliers and refitting for 21 genes
## -- DESeq argument 'minReplicatesForReplace' = 7 
## -- original counts are preserved in counts(dds)

## estimating dispersions

## fitting model and testing

## converting counts to integer mode

##   the design formula contains one or more numeric variables with integer values,
##   specifying a model with increasing fold change for higher values.
##   did you mean for this to be a factor? if so, first convert
##   this variable to a factor using the factor() function

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## -- replacing outliers and refitting for 47 genes
## -- DESeq argument 'minReplicatesForReplace' = 7 
## -- original counts are preserved in counts(dds)

## estimating dispersions

## fitting model and testing

## converting counts to integer mode

##   the design formula contains one or more numeric variables with integer values,
##   specifying a model with increasing fold change for higher values.
##   did you mean for this to be a factor? if so, first convert
##   this variable to a factor using the factor() function

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## -- replacing outliers and refitting for 12 genes
## -- DESeq argument 'minReplicatesForReplace' = 7 
## -- original counts are preserved in counts(dds)

## estimating dispersions

## fitting model and testing

## converting counts to integer mode

##   the design formula contains one or more numeric variables with integer values,
##   specifying a model with increasing fold change for higher values.
##   did you mean for this to be a factor? if so, first convert
##   this variable to a factor using the factor() function

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## -- replacing outliers and refitting for 42 genes
## -- DESeq argument 'minReplicatesForReplace' = 7 
## -- original counts are preserved in counts(dds)

## estimating dispersions

## fitting model and testing

## converting counts to integer mode

##   the design formula contains one or more numeric variables with integer values,
##   specifying a model with increasing fold change for higher values.
##   did you mean for this to be a factor? if so, first convert
##   this variable to a factor using the factor() function

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## -- replacing outliers and refitting for 22 genes
## -- DESeq argument 'minReplicatesForReplace' = 7 
## -- original counts are preserved in counts(dds)

## estimating dispersions

## fitting model and testing

## converting counts to integer mode

##   the design formula contains one or more numeric variables with integer values,
##   specifying a model with increasing fold change for higher values.
##   did you mean for this to be a factor? if so, first convert
##   this variable to a factor using the factor() function

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## -- replacing outliers and refitting for 2 genes
## -- DESeq argument 'minReplicatesForReplace' = 7 
## -- original counts are preserved in counts(dds)

## estimating dispersions

## fitting model and testing

## converting counts to integer mode

##   the design formula contains one or more numeric variables with integer values,
##   specifying a model with increasing fold change for higher values.
##   did you mean for this to be a factor? if so, first convert
##   this variable to a factor using the factor() function

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## -- replacing outliers and refitting for 63 genes
## -- DESeq argument 'minReplicatesForReplace' = 7 
## -- original counts are preserved in counts(dds)

## estimating dispersions

## fitting model and testing

## converting counts to integer mode

##   the design formula contains one or more numeric variables with integer values,
##   specifying a model with increasing fold change for higher values.
##   did you mean for this to be a factor? if so, first convert
##   this variable to a factor using the factor() function

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## -- replacing outliers and refitting for 10 genes
## -- DESeq argument 'minReplicatesForReplace' = 7 
## -- original counts are preserved in counts(dds)

## estimating dispersions

## fitting model and testing

## converting counts to integer mode

##   the design formula contains one or more numeric variables with integer values,
##   specifying a model with increasing fold change for higher values.
##   did you mean for this to be a factor? if so, first convert
##   this variable to a factor using the factor() function

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## -- replacing outliers and refitting for 30 genes
## -- DESeq argument 'minReplicatesForReplace' = 7 
## -- original counts are preserved in counts(dds)

## estimating dispersions

## fitting model and testing

names(deres) <- names(assays(pb))

nsig1 <- lapply(deres,function(x) {
  nrow(subset(x,padj<0.05))
})

nsig1

## $`NK 1 (FCGRA3A+)`
## [1] 11
## 
## $`CD14+ Mono`
## [1] 17
## 
## $`NK 2 (IL7R+)`
## [1] 3
## 
## $`CD14+ Mono (CCL4+)`
## [1] 3
## 
## $`FCGR3A+ Mono`
## [1] 1
## 
## $T
## [1] 0
## 
## $DC
## [1] 4
## 
## $Macrophage
## [1] 0
## 
## $B
## [1] 0

make_volcano <- function(dm,name) {
    sig <- subset(dm,padj<0.05)
    N_SIG=nrow(sig)
    N_UP=nrow(subset(sig,log2FoldChange>0))
    N_DN=nrow(subset(sig,log2FoldChange<0))
    HEADER=paste(N_SIG,"@5%FDR,", N_UP, "up", N_DN, "dn")
    plot(dm$log2FoldChange,-log10(dm$pval),cex=0.5,pch=19,col="darkgray",
        main=name, xlab="log FC", ylab="-log10 pval")
    mtext(HEADER)
    grid()
    points(sig$log2FoldChange,-log10(sig$pval),cex=0.5,pch=19,col="red")
}

lapply(1:length(deres),function(i) {
  x <- deres[[i]][,1:6]
  myname <- names(deres)[i]
  make_volcano(dm=x,name=myname)
})

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message("Up-regulated")

## Up-regulated

lapply(deres,function(x) {
  x <- x[,1:6]
  head(subset(x,log2FoldChange>0),10)
})

## $`NK 1 (FCGRA3A+)`
##             baseMean log2FoldChange     lfcSE     stat       pvalue       padj
## LINC02446  130.85455      1.8802099 0.3551046 5.294806 1.191431e-07 0.00174771
## MSC-AS1     34.45640      2.0193514 0.4391843 4.597959 4.266504e-06 0.01043089
## FCGR3A    1984.77882      0.8208262 0.2020907 4.061672 4.872253e-05 0.05955924
## RPTOR      363.38288      0.6232042 0.1548078 4.025663 5.681491e-05 0.06057268
## NECTIN3     11.60475      1.5748953 0.3979056 3.957962 7.559204e-05 0.06057268
## MTM1        39.80513      1.5978273 0.4062433 3.933178 8.383001e-05 0.06148512
## WNK1       670.37485      0.4087957 0.1050969 3.889702 1.003676e-04 0.06534174
## ATXN1     1212.75777      0.4439386 0.1171485 3.789538 1.509276e-04 0.07634335
## ZBTB38     465.73316      0.5250362 0.1401219 3.746996 1.789652e-04 0.08750803
## ARHGAP10   321.68658      0.9123738 0.2447249 3.728161 1.928820e-04 0.08841832
## 
## $`CD14+ Mono`
##              baseMean log2FoldChange     lfcSE     stat       pvalue
## AC239799.2  219.57657      1.8277552 0.3289869 5.555707 2.764900e-08
## TEX14      6013.21823      1.5241394 0.2751144 5.540020 3.024371e-08
## AP001160.1   54.83798      2.0976703 0.3988444 5.259369 1.445502e-07
## TMEM107     329.92437      1.3416959 0.2645624 5.071377 3.949474e-07
## L3MBTL4     257.89909      1.1985093 0.2445787 4.900302 9.568934e-07
## THSD8        17.18787      2.7849343 0.5706311 4.880446 1.058463e-06
## WDR74       894.73858      1.2207754 0.2546816 4.793339 1.640282e-06
## LINC00623   270.50423      0.6194504 0.1370612 4.519518 6.198070e-06
## AC103591.3  730.87389      0.7549738 0.1682938 4.486047 7.255663e-06
## AC253572.2 1564.63259      1.4883339 0.3319639 4.483421 7.345574e-06
##                    padj
## AC239799.2 0.0001934690
## TEX14      0.0001934690
## AP001160.1 0.0006164583
## TMEM107    0.0012632394
## L3MBTL4    0.0022569968
## THSD8      0.0022569968
## WDR74      0.0029979668
## LINC00623  0.0093979278
## AC103591.3 0.0093979278
## AC253572.2 0.0093979278
## 
## $`NK 2 (IL7R+)`
##           baseMean log2FoldChange     lfcSE     stat       pvalue        padj
## HBB      854.17620      6.4604258 1.4568707 4.434454 9.230600e-06 0.006286038
## MYO5A    269.96402      0.5097731 0.1274009 4.001331 6.298708e-05          NA
## HBA2     214.66410      5.8524453 1.4660681 3.991933 6.553694e-05          NA
## HLA-DPA1 316.83019      0.6160100 0.1576249 3.908075 9.303425e-05          NA
## PDLIM1    28.75687      1.0136619 0.2681838 3.779728 1.569995e-04          NA
## HLA-DPB1 495.40166      0.6627458 0.1759387 3.766912 1.652790e-04          NA
## FCGR3A   211.86079      1.0398103 0.2795584 3.719474 1.996382e-04          NA
## CEP152    37.25640      0.8001486 0.2184655 3.662586 2.496821e-04          NA
## FABP5    108.44360      0.8482006 0.2349648 3.609904 3.063098e-04          NA
## CDYL2     59.09071      0.6422554 0.1786973 3.594098 3.255176e-04          NA
## 
## $`CD14+ Mono (CCL4+)`
##              baseMean log2FoldChange     lfcSE     stat       pvalue      padj
## AP001160.1  26.480955      1.6122615 0.3986995 4.043801 5.259161e-05 0.1214866
## AC239799.2  97.170627      1.7160044 0.4858228 3.532161 4.121787e-04 0.6423212
## CEP152      44.307348      0.8303066 0.2352792 3.529026 4.170917e-04 0.6423212
## ASTL       141.510950      1.5458773 0.4470673 3.457818 5.445690e-04 0.7547726
## CXCL9        6.076817      2.8413820 0.8391106 3.386183 7.087213e-04 0.7690969
## GCNT2      126.163599      1.1418177 0.3387022 3.371155 7.485374e-04 0.7690969
## AL035446.1   5.602586      1.9533227 0.5811889 3.360908 7.768656e-04 0.7690969
## NAA25      167.565629      0.5081391 0.1533547 3.313488 9.214012e-04 0.8018431
## TMEM107    139.182578      1.1795972 0.3614019 3.263949 1.098711e-03 0.8313600
## WDR74      377.811964      1.1301621 0.3471447 3.255593 1.131557e-03 0.8313600
## 
## $`FCGR3A+ Mono`
##              baseMean log2FoldChange     lfcSE     stat       pvalue      padj
## ASTL        27.033978      2.2631404 0.5863014 3.860029 0.0001133737 0.2497641
## AC018755.4  50.426490      0.8807728 0.2282901 3.858129 0.0001142582 0.2497641
## LINC01366    8.145255      1.8845263 0.5108783 3.688797 0.0002253171 0.4109784
## LINC01004  104.658034      0.5404469 0.1490028 3.627091 0.0002866322 0.4574650
## ANK3        13.763878      1.9490374 0.5448397 3.577267 0.0003472051 0.4925683
## CXCL10      26.077754      2.7758707 0.7872721 3.525935 0.0004219901 0.5387969
## DGKH        13.472268      1.3451211 0.3916618 3.434394 0.0005938795 0.5456725
## NLRC5      185.855270      0.5215871 0.1519611 3.432373 0.0005983251 0.5456725
## AC245014.3 103.033028      1.2574038 0.3801056 3.308038 0.0009395214 0.5999629
## NHEJ1       36.013002      0.7815564 0.2392068 3.267284 0.0010858486 0.5999629
## 
## $T
##            baseMean log2FoldChange     lfcSE     stat      pvalue    padj
## TMEM131    4.548092      1.6733488 0.5552052 3.013929 0.002578885 0.99875
## YES1       7.043204      1.3803585 0.4989873 2.766320 0.005669294 0.99875
## HBA1     205.640292      4.3108808 1.5613958 2.760915 0.005763971 0.99875
## HBA2     603.820281      4.3008982 1.6432064 2.617382 0.008860724 0.99875
## HBB     2814.201545      4.5858501 1.7787880 2.578076 0.009935221 0.99875
## NCOR1     10.841539      0.8809492 0.3725593 2.364588 0.018050135 0.99875
## NAP1L4     5.135122      1.0879024 0.4995827 2.177622 0.029434177 0.99875
## MYL9       7.648986      2.0836212 0.9610446 2.168080 0.030152630 0.99875
## SEM1       5.619102      1.0334194 0.4966629 2.080726 0.037459001 0.99875
## BOD1L1     6.914654      0.9253759 0.4540496 2.038050 0.041544896 0.99875
## 
## $DC
##             baseMean log2FoldChange     lfcSE     stat       pvalue       padj
## AC253572.2 220.28201      1.3173819 0.3048641 4.321210 1.551761e-05 0.04877858
## TMEM107     91.13860      0.9318133 0.2206358 4.223310 2.407407e-05 0.05158641
## AL035446.2  13.04327      2.5905329 0.6285129 4.121686 3.761097e-05 0.06536248
## AC239799.2  51.00850      1.2393312 0.3148959 3.935685 8.295960e-05 0.12615045
## TRIM14      88.44710      0.5999574 0.1619304 3.705033 2.113631e-04 0.21738206
## AC245014.3 108.39832      0.9174370 0.2492345 3.681020 2.323031e-04 0.21738206
## TEX14      894.05488      0.9107811 0.2509955 3.628675 2.848792e-04 0.24097488
## SLC12A2     47.96101      0.8005622 0.2212851 3.617787 2.971330e-04 0.24097488
## AC007952.4 217.50458      0.8142990 0.2314026 3.518971 4.332238e-04 0.30600822
## AC144652.1  13.96332      1.4626216 0.4193092 3.488169 4.863399e-04 0.30600822
## 
## $Macrophage
##              baseMean log2FoldChange     lfcSE     stat       pvalue      padj
## TCL1A       93.867915      1.0902743 0.2768075 3.938745 8.190880e-05 0.3375802
## KCNQ5        6.500180      2.1783939 0.5580180 3.903806 9.469175e-05 0.3375802
## NR4A3       45.271498      1.5191660 0.3928868 3.866676 1.103288e-04 0.3375802
## HLA-C      548.900354      0.8209086 0.2192877 3.743522 1.814584e-04 0.3375802
## PLEKHG2     17.590340      1.3389923 0.3596670 3.722866 1.969738e-04 0.3375802
## PKD2        34.867400      0.8070777 0.2314593 3.486909 4.886375e-04 0.6280824
## AL355075.4  34.568710      1.1212111 0.3405454 3.292399 9.933670e-04 0.8736046
## AC239799.2  16.968088      1.0861180 0.3373227 3.219819 1.282716e-03 0.9421552
## XACT         9.112779      2.1174527 0.6745234 3.139183 1.694193e-03 0.9995856
## SP4         31.731192      0.7298792 0.2352729 3.102266 1.920456e-03 0.9995856
## 
## $B
##             baseMean log2FoldChange     lfcSE     stat       pvalue      padj
## PRG2       19.494272      3.8372680 0.8942474 4.291058 1.778235e-05 0.1601123
## TPSAB1     16.225058      3.3887905 1.0126806 3.346357 8.188094e-04 0.9986398
## BX284613.2 10.353411      1.9679315 0.5995178 3.282524 1.028823e-03 0.9986398
## SLC24A3    55.632919      1.7235976 0.5353291 3.219697 1.283262e-03 0.9986398
## MS4A2      15.534285      3.1782027 0.9887487 3.214368 1.307318e-03 0.9986398
## DCAF6      33.153706      0.8652989 0.2737851 3.160504 1.574962e-03 0.9986398
## RAD51B     85.942680      0.6396572 0.2065131 3.097417 1.952148e-03 0.9986398
## DTL        11.769723      1.4877069 0.4886389 3.044594 2.329949e-03 0.9986398
## APOE        4.905372      2.7528424 0.9067484 3.035950 2.397794e-03 0.9986398
## APOC1      13.934261      1.9130840 0.6312286 3.030731 2.439627e-03 0.9986398

message("Down-regulated")

## Down-regulated

lapply(deres,function(x) {
  x <- x[,1:6]
  head(subset(x,log2FoldChange<0),10)
})

## $`NK 1 (FCGRA3A+)`
##                baseMean log2FoldChange     lfcSE      stat       pvalue
## BEX4          203.78365     -0.7457956 0.1504036 -4.958630 7.099216e-07
## AC120193.1     44.98166     -0.9418390 0.1947843 -4.835292 1.329504e-06
## PIWIL3         11.50857     -3.6677527 0.7801865 -4.701123 2.587347e-06
## SCRN1          64.16911     -1.3644860 0.2944586 -4.633881 3.588737e-06
## AC233976.1     27.14550     -1.5961076 0.3565531 -4.476493 7.587902e-06
## MIR4458HG      15.14060     -1.4397897 0.3274941 -4.396384 1.100694e-05
## ZDBF2          60.08055     -1.1772282 0.2808669 -4.191409 2.772276e-05
## ZNF442         14.05453     -1.3466068 0.3214443 -4.189238 2.798926e-05
## RNF217         22.12022     -1.5743284 0.3787806 -4.156307 3.234335e-05
## CCDC144NL-AS1  13.14685     -1.4412699 0.3594410 -4.009754 6.078207e-05
##                      padj
## BEX4          0.005206920
## AC120193.1    0.006500831
## PIWIL3        0.009488447
## SCRN1         0.010430890
## AC233976.1    0.015900990
## MIR4458HG     0.020182595
## ZDBF2         0.041057444
## ZNF442        0.041057444
## RNF217        0.043131324
## CCDC144NL-AS1 0.060572677
## 
## $`CD14+ Mono`
##             baseMean log2FoldChange     lfcSE      stat       pvalue       padj
## SULT1A1    483.86190     -0.5670462 0.1313671 -4.316503 1.585207e-05 0.01448653
## AC009974.2  14.12940     -1.3969767 0.3440152 -4.060800 4.890490e-05 0.03680525
## TNNI2      109.55689     -0.8742316 0.2248029 -3.888881 1.007075e-04 0.06611554
## SLC9A3R1   681.92293     -0.7262460 0.1873961 -3.875459 1.064241e-04 0.06611554
## FAM157C    157.93069     -0.8418565 0.2174944 -3.870704 1.085217e-04 0.06611554
## ZEB1        20.74069     -2.4279480 0.6405255 -3.790557 1.503101e-04 0.08271126
## ZNF697     116.55545     -0.9031815 0.2387684 -3.782668 1.551563e-04 0.08271126
## TBP        137.49069     -0.5924773 0.1655371 -3.579121 3.447519e-04 0.13365928
## AL032821.1  13.63233     -1.3708945 0.3933250 -3.485399 4.914041e-04 0.17463956
## AL133297.2  35.56103     -3.0297488 0.8921313 -3.396079 6.835855e-04 0.22027929
## 
## $`NK 2 (IL7R+)`
##              baseMean log2FoldChange     lfcSE      stat       pvalue padj
## AC009041.2  13.758496      -1.680409 0.3117613 -5.390049 7.043860e-08   NA
## USP44       10.777769      -1.830992 0.3633235 -5.039562 4.665981e-07   NA
## CTNNA3      13.364414      -2.041420 0.4366013 -4.675709 2.929403e-06   NA
## AP000787.1 151.177374      -1.340340 0.3036941 -4.413454 1.017344e-05   NA
## LINC00511   13.901981      -1.328786 0.3072483 -4.324794 1.526742e-05   NA
## ZNF10      126.951708      -0.712087 0.1658735 -4.292953 1.763121e-05   NA
## SLC22A23    17.461782      -1.296894 0.3082939 -4.206680 2.591496e-05   NA
## FXYD7       68.749330      -1.073189 0.2560751 -4.190914 2.778324e-05   NA
## MACROD2      9.987173      -1.671438 0.3996142 -4.182629 2.881574e-05   NA
## LRRN1        7.699828      -1.615234 0.3892157 -4.149971 3.325175e-05   NA
## 
## $`CD14+ Mono (CCL4+)`
##           baseMean log2FoldChange     lfcSE      stat       pvalue       padj
## PPARG   113.271484     -2.6220999 0.5732726 -4.573915 4.786948e-06 0.03591472
## ARRDC3  273.716433     -1.0751425 0.2390590 -4.497394 6.879153e-06 0.03591472
## GSG1     15.388658     -1.3041880 0.2916784 -4.471322 7.773749e-06 0.03591472
## PLPP3    21.386384     -3.0358517 0.7149270 -4.246380 2.172521e-05 0.06453717
## RUBCNL   44.498469     -1.4686478 0.3471290 -4.230842 2.328181e-05 0.06453717
## GPAT3   138.095157     -0.8225436 0.2235564 -3.679356 2.338240e-04 0.46297150
## SELENOK 277.429668     -0.4819702 0.1422976 -3.387058 7.064654e-04 0.76909693
## ZSCAN18  12.387733     -1.2428788 0.3752425 -3.312201 9.256485e-04 0.80184306
## DLEC1    17.000186     -0.9059846 0.2797947 -3.238034 1.203566e-03 0.83407148
## HORMAD2   7.251243     -1.8448525 0.6037576 -3.055618 2.245973e-03 0.99997103
## 
## $`FCGR3A+ Mono`
##              baseMean log2FoldChange     lfcSE      stat       pvalue
## PTGES       80.905477     -2.0005062 0.4054784 -4.933694 8.068881e-07
## TNNI2      110.306026     -0.8233175 0.2037307 -4.041205 5.317731e-05
## WDR86        6.112729     -2.3075881 0.5743551 -4.017702 5.876833e-05
## PUS10       58.468056     -0.5960076 0.1547447 -3.851555 1.173704e-04
## AF127577.2  17.771134     -1.1453710 0.3311580 -3.458684 5.428217e-04
## TWF2       181.379711     -0.4112220 0.1190295 -3.454791 5.507199e-04
## ARRDC3     258.761311     -0.8112621 0.2415165 -3.359034 7.821554e-04
## AC021752.1  18.581098     -0.9091475 0.2730268 -3.329883 8.688242e-04
## ARG2        10.353223     -1.1346791 0.3412402 -3.325162 8.836703e-04
## NAPB        46.012153     -0.7575819 0.2294716 -3.301419 9.619696e-04
##                  padj
## PTGES      0.01030235
## TNNI2      0.24976414
## WDR86      0.24976414
## PUS10      0.24976414
## AF127577.2 0.54567246
## TWF2       0.54567246
## ARRDC3     0.59996295
## AC021752.1 0.59996295
## ARG2       0.59996295
## NAPB       0.59996295
## 
## $T
##          baseMean log2FoldChange     lfcSE      stat     pvalue    padj
## BCL6     5.278050     -1.0019682 0.4167210 -2.404410 0.01619859 0.99875
## YPEL3    8.045802     -0.8840448 0.3780896 -2.338188 0.01937748 0.99875
## CYBB    14.296499     -0.8015324 0.3518583 -2.277998 0.02272671 0.99875
## ATP5F1C  6.239390     -0.9863001 0.4401401 -2.240878 0.02503401 0.99875
## REL      9.739690     -0.7762767 0.3536059 -2.195316 0.02814096 0.99875
## USP34   10.886515     -0.7929194 0.3714329 -2.134758 0.03278080 0.99875
## SFT2D1   6.453705     -0.9300804 0.4366068 -2.130247 0.03315120 0.99875
## IGHM    10.772156     -1.4059945 0.6728276 -2.089680 0.03664653 0.99875
## KCNAB2   6.145091     -0.9089676 0.4483915 -2.027174 0.04264463 0.99875
## LNPEP    5.636824     -0.8554812 0.4237925 -2.018632 0.04352545 0.99875
## 
## $DC
##            baseMean log2FoldChange     lfcSE      stat       pvalue       padj
## FAM118A  205.012845     -0.6563038 0.1425586 -4.603747 4.149570e-06 0.04777184
## LILRA5   122.388767     -0.9295040 0.2079834 -4.469126 7.853981e-06 0.04777184
## C5AR1     68.797820     -1.2797005 0.2966449 -4.313913 1.603899e-05 0.04877858
## APOBEC3A  38.319594     -1.4648867 0.3478854 -4.210831 2.544336e-05 0.05158641
## ERG       14.169042     -1.5294657 0.3993192 -3.830183 1.280479e-04 0.15587043
## CHST15    85.193328     -1.1620252 0.3033989 -3.830025 1.281302e-04 0.15587043
## TNFSF9    39.573427     -1.2374361 0.3355322 -3.687980 2.260412e-04 0.21738206
## GK5       48.861849     -0.7707061 0.2169967 -3.551695 3.827582e-04 0.29101584
## EXTL3     42.582870     -0.9016178 0.2585327 -3.487442 4.876646e-04 0.30600822
## TBC1D30    9.180131     -1.4077049 0.4046173 -3.479102 5.030961e-04 0.30600822
## 
## $Macrophage
##              baseMean log2FoldChange     lfcSE      stat       pvalue      padj
## ARRDC3      38.373223     -1.0111753 0.2690872 -3.757797 0.0001714155 0.3375802
## TFPI        15.112197     -1.1872938 0.3265052 -3.636370 0.0002765073 0.4061892
## VEGFA        7.101706     -1.7629414 0.5148229 -3.424365 0.0006162379 0.7040860
## EXT1       229.095337     -0.6489528 0.1919741 -3.380419 0.0007237551 0.7442374
## AC025159.1  33.863651     -0.9010538 0.2752763 -3.273271 0.0010631048 0.8736046
## LINC02076    4.914336     -2.1352349 0.6544827 -3.262477 0.0011044306 0.8736046
## TIGAR       43.810748     -0.5699251 0.1781626 -3.198905 0.0013795083 0.9456990
## ST3GAL1     49.433197     -0.9621846 0.3056546 -3.147948 0.0016442107 0.9995856
## AC138649.1   8.549846     -1.1672439 0.4077142 -2.862897 0.0041978686 0.9995856
## LINC01765    6.269495     -1.2807672 0.4476461 -2.861116 0.0042215281 0.9995856
## 
## $B
##              baseMean log2FoldChange     lfcSE      stat       pvalue      padj
## ID1         10.275158     -2.4210039 0.6511455 -3.718069 0.0002007513 0.9037823
## ID2         24.157156     -1.0047805 0.2894791 -3.470994 0.0005185347 0.9986398
## IGHG4       17.950157     -3.5236269 1.0165537 -3.466248 0.0005277767 0.9986398
## ZRANB2-AS2   6.245355     -1.6698395 0.5002739 -3.337850 0.0008442922 0.9986398
## HIST1H2BF   10.249874     -1.7313542 0.5526680 -3.132720 0.0017319449 0.9986398
## H3F3B      903.748686     -1.2445832 0.4032188 -3.086620 0.0020244614 0.9986398
## H1FX       194.847009     -1.3846936 0.4516522 -3.065841 0.0021705890 0.9986398
## SLC25A29    11.322208     -1.1278562 0.3693570 -3.053566 0.0022613871 0.9986398
## MYADM       26.590013     -0.9077363 0.3013125 -3.012608 0.0025901362 0.9986398
## ATP10A      19.934320     -1.2811993 0.4320608 -2.965322 0.0030236632 0.9986398

if ( ! dir.exists("de_analysis") ) {
  dir.create("de_analysis")
}

lapply(1:length(assays(pb)),function(i) {
  cellname=names(assays(pb))[[i]]
  myres <- deres[[i]]
  filename <- paste(cellname,".tsv",sep="")
  filename <- gsub("\\)","",gsub("\\(","",gsub(" ","_",filename)))
  filename <- paste("de_analysis/",filename,sep="")
  write.table(x=myres,file=filename,sep="\t",quote=FALSE)
})

## [[1]]
## NULL
## 
## [[2]]
## NULL
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## NULL
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## [[4]]
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## NULL
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## [[6]]
## NULL
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## [[7]]
## NULL
## 
## [[8]]
## NULL
## 
## [[9]]
## NULL

Differential expression with batch correction.

deres <- lapply(1:length(assays(pb)), function(i) {
  counts <- assays(pb)[[i]]
  cellname <- names(assays(pb))[i]
  fcounts <- counts[which(rowMeans(counts)>=5),]
  fcounts <- fcounts+1
  dds <- DESeqDataSetFromMatrix(countData = fcounts , colData = pat_df, design = ~ batch + hiv_status)
  res <- DESeq(dds)
  z <- results(res)
  vsd <- vst(dds, blind=FALSE)
  zz<-cbind(as.data.frame(z),assay(vsd),fcounts)
  de <- as.data.frame(zz[order(zz$pvalue),])
  return(de)
})

## converting counts to integer mode

##   the design formula contains one or more numeric variables with integer values,
##   specifying a model with increasing fold change for higher values.
##   did you mean for this to be a factor? if so, first convert
##   this variable to a factor using the factor() function

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## converting counts to integer mode

##   the design formula contains one or more numeric variables with integer values,
##   specifying a model with increasing fold change for higher values.
##   did you mean for this to be a factor? if so, first convert
##   this variable to a factor using the factor() function

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## converting counts to integer mode

##   the design formula contains one or more numeric variables with integer values,
##   specifying a model with increasing fold change for higher values.
##   did you mean for this to be a factor? if so, first convert
##   this variable to a factor using the factor() function

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## converting counts to integer mode

##   the design formula contains one or more numeric variables with integer values,
##   specifying a model with increasing fold change for higher values.
##   did you mean for this to be a factor? if so, first convert
##   this variable to a factor using the factor() function

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## converting counts to integer mode

##   the design formula contains one or more numeric variables with integer values,
##   specifying a model with increasing fold change for higher values.
##   did you mean for this to be a factor? if so, first convert
##   this variable to a factor using the factor() function

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## converting counts to integer mode

##   the design formula contains one or more numeric variables with integer values,
##   specifying a model with increasing fold change for higher values.
##   did you mean for this to be a factor? if so, first convert
##   this variable to a factor using the factor() function

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## converting counts to integer mode

##   the design formula contains one or more numeric variables with integer values,
##   specifying a model with increasing fold change for higher values.
##   did you mean for this to be a factor? if so, first convert
##   this variable to a factor using the factor() function

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## converting counts to integer mode

##   the design formula contains one or more numeric variables with integer values,
##   specifying a model with increasing fold change for higher values.
##   did you mean for this to be a factor? if so, first convert
##   this variable to a factor using the factor() function

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## converting counts to integer mode

##   the design formula contains one or more numeric variables with integer values,
##   specifying a model with increasing fold change for higher values.
##   did you mean for this to be a factor? if so, first convert
##   this variable to a factor using the factor() function

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

names(deres) <- names(assays(pb))

nsig2 <- lapply(deres,function(x) {
  nrow(subset(x,padj<0.05))
})

nsig2

## $`NK 1 (FCGRA3A+)`
## [1] 27
## 
## $`CD14+ Mono`
## [1] 12
## 
## $`NK 2 (IL7R+)`
## [1] 20
## 
## $`CD14+ Mono (CCL4+)`
## [1] 3
## 
## $`FCGR3A+ Mono`
## [1] 1
## 
## $T
## [1] 0
## 
## $DC
## [1] 4
## 
## $Macrophage
## [1] 0
## 
## $B
## [1] 0

bc <- t(rbind(as.data.frame(nsig1),as.data.frame(nsig2)))
colnames(bc) <- c("uncorrected","corrected")

lapply(1:length(deres),function(i) {
  x <- deres[[i]][,1:6]
  myname <- names(deres)[i]
  make_volcano(dm=x,name=myname)
})

## [[1]]
## NULL
## 
## [[2]]
## NULL
## 
## [[3]]
## NULL
## 
## [[4]]
## NULL
## 
## [[5]]
## NULL
## 
## [[6]]
## NULL
## 
## [[7]]
## NULL
## 
## [[8]]
## NULL
## 
## [[9]]
## NULL

message("Up-regulated")

## Up-regulated

lapply(deres,function(x) {
  x <- x[,1:6]
  head(subset(x,log2FoldChange>0),10)
})

## $`NK 1 (FCGRA3A+)`
##            baseMean log2FoldChange      lfcSE     stat       pvalue
## LINC02446 130.85455      2.1286856 0.36093761 5.897655 3.687032e-09
## MSC-AS1    34.45640      2.1828503 0.47394921 4.605663 4.111538e-06
## MYO6      270.60487      0.8594049 0.19091451 4.501517 6.747026e-06
## IFI27L2   399.41849      0.3413644 0.07735526 4.412944 1.019746e-05
## PTTG1      48.34220      0.9210436 0.21805042 4.223994 2.400108e-05
## NECTIN3    11.60475      1.5859468 0.37948522 4.179206 2.925290e-05
## HLA-DPB1  803.55713      0.7887473 0.18968936 4.158100 3.209056e-05
## CDYL2      37.08550      1.4068589 0.35278034 3.987917 6.665584e-05
## RPTOR     363.38288      0.5663943 0.14266502 3.970099 7.184267e-05
## MTM1       39.80513      1.5043277 0.39188416 3.838705 1.236849e-04
##                   padj
## LINC02446 4.992241e-05
## MSC-AS1   9.278371e-03
## MYO6      1.305068e-02
## IFI27L2   1.725919e-02
## PTTG1     2.413923e-02
## NECTIN3   2.413923e-02
## HLA-DPB1  2.413923e-02
## CDYL2     3.610080e-02
## RPTOR     3.741345e-02
## MTM1      5.315696e-02
## 
## $`CD14+ Mono`
##              baseMean log2FoldChange     lfcSE     stat       pvalue
## AC239799.2  219.57657      1.7930081 0.3648274 4.914675 8.892974e-07
## AP001160.1   54.83798      2.2304076 0.4569053 4.881553 1.052535e-06
## TEX14      6013.21823      1.5007373 0.3097678 4.844716 1.267926e-06
## AC079753.2   68.93448      2.9246800 0.6251852 4.678102 2.895424e-06
## TMEM107     329.92437      1.3325466 0.3012027 4.424086 9.685136e-06
## THSD8        17.18787      2.7940047 0.6351446 4.399006 1.087478e-05
## LINC00623   270.50423      0.6009981 0.1374083 4.373813 1.220950e-05
## L3MBTL4     257.89909      1.1966444 0.2756084 4.341828 1.413021e-05
## AC253572.2 1564.63259      1.4227830 0.3399987 4.184672 2.855777e-05
## WDR74       894.73858      1.1893822 0.2847557 4.176851 2.955722e-05
##                   padj
## AC239799.2 0.005803720
## AP001160.1 0.005803720
## TEX14      0.005803720
## AC079753.2 0.007951994
## TMEM107    0.020957602
## THSD8      0.020957602
## LINC00623  0.020957602
## L3MBTL4    0.021559560
## AC253572.2 0.036898162
## WDR74      0.036898162
## 
## $`NK 2 (IL7R+)`
##             baseMean log2FoldChange     lfcSE     stat       pvalue
## CCL5      4304.09113      0.6725337 0.1099411 6.117218 9.522328e-10
## HLA-DPA1   316.83019      0.6997432 0.1243654 5.626509 1.838933e-08
## HLA-DPB1   495.40166      0.7422984 0.1461530 5.078913 3.795998e-07
## FABP5      108.44360      0.9488743 0.1939154 4.893238 9.919049e-07
## LINC02384  119.51675      1.3255419 0.2874366 4.611597 3.995871e-06
## CDYL2       59.09071      0.6990216 0.1870673 3.736739 1.864226e-04
## PDLIM1      28.75687      1.0227229 0.2799873 3.652747 2.594494e-04
## MT1E        90.94295      1.1979482 0.3314307 3.614475 3.009568e-04
## IRF4        50.84447      0.9102567 0.2520683 3.611151 3.048406e-04
## HLA-DRB1   391.68535      0.6263100 0.1758522 3.561570 3.686438e-04
##                   padj
## CCL5      6.848101e-06
## HLA-DPA1  8.365305e-05
## HLA-DPB1  8.633998e-04
## FABP5     1.692066e-03
## LINC02384 4.544305e-03
## CDYL2     9.086103e-02
## PDLIM1    1.011630e-01
## MT1E      1.124367e-01
## IRF4      1.124367e-01
## HLA-DRB1  1.257720e-01
## 
## $`CD14+ Mono (CCL4+)`
##              baseMean log2FoldChange     lfcSE     stat       pvalue      padj
## AP001160.1  26.480955      1.6854217 0.4069666 4.141425 3.451549e-05 0.1107013
## AC239799.2  97.170627      1.8810784 0.4578595 4.108418 3.983782e-05 0.1107013
## CEP152      44.307348      0.8916622 0.2256789 3.951021 7.781843e-05 0.1608326
## GCNT2      126.163599      1.0815122 0.2903733 3.724559 1.956573e-04 0.2827138
## CEP57L1     23.654725      0.9794998 0.2636851 3.714658 2.034791e-04 0.2827138
## SPTLC2     598.402198      0.6196831 0.1725033 3.592298 3.277753e-04 0.3276574
## STMN1       25.025196      0.9859338 0.2763163 3.568134 3.595322e-04 0.3276574
## SLC27A1     59.323226      0.7244196 0.2032486 3.564204 3.649618e-04 0.3276574
## CXCL9        6.076817      2.9344859 0.8267323 3.549499 3.859642e-04 0.3276574
## NHSL1      154.860422      0.8993505 0.2611959 3.443202 5.748690e-04 0.3953403
## 
## $`FCGR3A+ Mono`
##              baseMean log2FoldChange     lfcSE     stat       pvalue      padj
## AC253572.2 220.559222      1.3870540 0.3787469 3.662219 0.0002500401 0.6223815
## C1QB       110.990994      1.0835604 0.3079490 3.518636 0.0004337719 0.6223815
## ASTL        27.033978      1.7700031 0.5061190 3.497207 0.0004701562 0.6223815
## LINC01004  104.658034      0.5378581 0.1542118 3.487788 0.0004870346 0.6223815
## AC018755.4  50.426490      0.7671552 0.2329744 3.292874 0.0009916898 0.7825801
## NBEA        13.638682      1.5048965 0.4649165 3.236917 0.0012082835 0.7825801
## TMEM254     19.713148      0.9636004 0.2989950 3.222798 0.0012694508 0.7825801
## NLRC5      185.855270      0.5015769 0.1600344 3.134183 0.0017233351 0.7825801
## DLGAP2       7.615759      2.7779664 0.8904268 3.119815 0.0018096493 0.7825801
## TMEM14C    167.682450      0.4795074 0.1537004 3.119754 0.0018100209 0.7825801
## 
## $T
##            baseMean log2FoldChange     lfcSE     stat       pvalue      padj
## HBB     2814.201545      6.3474978 1.6888234 3.758533 0.0001709127 0.3202905
## HBA2     603.820281      5.6657502 1.6205505 3.496189 0.0004719551 0.4422220
## HBA1     205.640292      4.7273107 1.4999612 3.151622 0.0016236629 0.9983424
## TMEM131    4.548092      1.6631390 0.6006673 2.768819 0.0056259877 0.9983424
## YES1       7.043204      1.4687778 0.5364013 2.738207 0.0061775209 0.9983424
## IGKC      50.052168      1.1274159 0.5114053 2.204545 0.0274860373 0.9983424
## NCOR1     10.841539      0.8735788 0.3972474 2.199080 0.0278722434 0.9983424
## OGA       10.861904      0.8482265 0.4037803 2.100713 0.0356661716 0.9983424
## SEM1       5.619102      1.0688826 0.5363421 1.992912 0.0462711077 0.9983424
## NAP1L4     5.135122      1.0345238 0.5465827 1.892712 0.0583961564 0.9983424
## 
## $DC
##             baseMean log2FoldChange     lfcSE     stat       pvalue       padj
## TMEM107     91.13860      1.0367159 0.2144978 4.833224 1.343395e-06 0.00820747
## AC007952.4 217.50458      0.9029977 0.2173708 4.154181 3.264552e-05 0.07641274
## AC253572.2 220.28201      1.2886481 0.3126092 4.122234 3.752160e-05 0.07641274
## AL035446.2  13.04327      2.3432527 0.6228719 3.762014 1.685505e-04 0.22883546
## AC245014.3 108.39832      0.9304942 0.2629809 3.538257 4.027775e-04 0.38098156
## AC239799.2  51.00850      1.2221272 0.3455668 3.536587 4.053327e-04 0.38098156
## CCDC26     203.71458      0.9401338 0.2709768 3.469426 5.215719e-04 0.45522054
## AC144652.1  13.96332      1.5396345 0.4476805 3.439137 5.835719e-04 0.47537771
## TRIM14      88.44710      0.5855962 0.1761214 3.324958 8.843193e-04 0.56512644
## TEX14      894.05488      0.9106514 0.2741285 3.321988 8.937867e-04 0.56512644
## 
## $Macrophage
##               baseMean log2FoldChange     lfcSE     stat       pvalue      padj
## HLA-C       548.900354      0.8963731 0.2236572 4.007800 6.128706e-05 0.4275123
## TCL1A        93.867915      1.1527714 0.2929327 3.935277 8.310085e-05 0.4275123
## KCNQ5         6.500180      2.1601742 0.5823952 3.709121 2.079802e-04 0.4279816
## XACT          9.112779      2.4965598 0.6985797 3.573765 3.518844e-04 0.4566354
## PTK2        105.007602      0.5813482 0.1627778 3.571423 3.550474e-04 0.4566354
## PCNT         30.631747      0.9139271 0.2637367 3.465302 5.296365e-04 0.6033991
## HLA-B      1104.947793      0.4452650 0.1295201 3.437805 5.864507e-04 0.6033991
## NR4A3        45.271498      1.4504572 0.4387491 3.305892 9.467444e-04 0.7532456
## AC118549.1   75.847610      0.4990188 0.1510153 3.304425 9.517147e-04 0.7532456
## PKD2         34.867400      0.7914444 0.2412539 3.280545 1.036068e-03 0.7614360
## 
## $B
##             baseMean log2FoldChange     lfcSE     stat       pvalue      padj
## PRG2       19.494272      4.1189844 0.9465727 4.351472 1.352267e-05 0.1218122
## DTL        11.769723      1.8877447 0.4900108 3.852455 1.169392e-04 0.2916381
## BX284613.2 10.353411      2.0672414 0.5993348 3.449226 5.621951e-04 0.8829858
## HPGD       14.647625      1.9288985 0.5612111 3.437028 5.881344e-04 0.8829858
## APOC1      13.934261      2.1692484 0.6467004 3.354333 7.955658e-04 0.9476717
## ORC6        5.943184      2.0943681 0.6817683 3.071965 2.126549e-03 0.9999354
## TPSAB1     16.225058      3.3075888 1.1090810 2.982279 2.861112e-03 0.9999354
## DCAF6      33.153706      0.8318079 0.2884822 2.883394 3.934148e-03 0.9999354
## CDC42BPA   41.013362      0.8583532 0.2991827 2.868994 4.117796e-03 0.9999354
## ANTKMT     15.220412      1.0233334 0.3704153 2.762665 5.733161e-03 0.9999354

message("Down-regulated")

## Down-regulated

lapply(deres,function(x) {
  x <- x[,1:6]
  head(subset(x,log2FoldChange<0),10)
})

## $`NK 1 (FCGRA3A+)`
##              baseMean log2FoldChange     lfcSE      stat       pvalue
## AC120193.1  44.981664     -0.9622082 0.1769880 -5.436574 5.431493e-08
## BEX4       203.783646     -0.7063573 0.1425121 -4.956471 7.178508e-07
## TEX14      410.364373     -0.8321431 0.1695461 -4.908063 9.198032e-07
## MIR4458HG   15.140604     -1.4840070 0.3089691 -4.803092 1.562341e-06
## DHRS3      203.271030     -1.0367044 0.2385539 -4.345787 1.387775e-05
## CASC8       29.770529     -1.3671683 0.3158507 -4.328527 1.501100e-05
## ATP9A       95.576152     -1.9225314 0.4514093 -4.258954 2.053861e-05
## SCRN1       64.169106     -1.3544800 0.3213921 -4.214417 2.504247e-05
## AC051619.5   9.036788     -1.6490063 0.3923756 -4.202622 2.638407e-05
## TTTY14     123.218048     -0.7335551 0.1763472 -4.159721 3.186366e-05
##                    padj
## AC120193.1 0.0003677121
## BEX4       0.0031135340
## TEX14      0.0031135340
## MIR4458HG  0.0042308202
## DHRS3      0.0203248894
## CASC8      0.0203248894
## ATP9A      0.0241392319
## SCRN1      0.0241392319
## AC051619.5 0.0241392319
## TTTY14     0.0241392319
## 
## $`CD14+ Mono`
##             baseMean log2FoldChange     lfcSE      stat       pvalue
## SLC9A3R1   681.92293     -0.8002175 0.1695943 -4.718422 2.376806e-06
## SULT1A1    483.86190     -0.5668832 0.1365613 -4.151125 3.308447e-05
## TBP        137.49069     -0.6748776 0.1711221 -3.943837 8.018817e-05
## AC009974.2  14.12940     -1.3162658 0.3620560 -3.635531 2.774091e-04
## AL032821.1  13.63233     -1.5261433 0.4228696 -3.609017 3.073598e-04
## TNNI2      109.55689     -0.8454082 0.2372711 -3.563047 3.665749e-04
## AL592183.1 154.62901     -1.7208623 0.5099573 -3.374522 7.394389e-04
## AC011603.2  98.71458     -0.7843998 0.2419760 -3.241643 1.188428e-03
## ZEB1        20.74069     -2.1129987 0.6518860 -3.241362 1.189600e-03
## SNX18      750.67309     -0.8042320 0.2498108 -3.219365 1.284750e-03
##                   padj
## SLC9A3R1   0.007951994
## SULT1A1    0.037859665
## TBP        0.073409598
## AC009974.2 0.173153733
## AL032821.1 0.183507147
## TNNI2      0.201352251
## AL592183.1 0.312292690
## AC011603.2 0.398428859
## ZEB1       0.398428859
## SNX18      0.410283373
## 
## $`NK 2 (IL7R+)`
##             baseMean log2FoldChange     lfcSE      stat       pvalue
## AP000787.1 151.17737     -1.4275746 0.2336901 -6.108836 1.003605e-09
## CTNNA3      13.36441     -2.1364248 0.4033586 -5.296589 1.179856e-07
## SESN3      297.49087     -1.3118149 0.2544716 -5.155054 2.535582e-07
## AC009041.2  13.75850     -1.7104642 0.3413227 -5.011282 5.406851e-07
## ADAM23      22.69418     -2.5515787 0.5249462 -4.860648 1.170020e-06
## USP44       10.77777     -1.8143459 0.3868689 -4.689821 2.734442e-06
## AL589693.1 153.25046     -1.3866088 0.2977091 -4.657597 3.199221e-06
## KANK1       39.15477     -1.3029381 0.2884380 -4.517221 6.265644e-06
## FXYD7       68.74933     -1.1449563 0.2542699 -4.502918 6.702687e-06
## CDKN1B     389.46428     -0.5010516 0.1137194 -4.406034 1.052806e-05
##                    padj
## AP000787.1 6.848101e-06
## CTNNA3     4.025372e-04
## SESN3      6.920618e-04
## AC009041.2 1.054104e-03
## ADAM23     1.774140e-03
## USP44      3.731693e-03
## AL589693.1 3.969070e-03
## KANK1      6.533684e-03
## FXYD7      6.533684e-03
## CDKN1B     9.578429e-03
## 
## $`CD14+ Mono (CCL4+)`
##               baseMean log2FoldChange     lfcSE      stat       pvalue
## GPAT3       138.095157     -0.9168031 0.1714880 -5.346166 8.983703e-08
## RUBCNL       44.498469     -1.6507620 0.3503579 -4.711645 2.457254e-06
## ARRDC3      273.716433     -1.0777376 0.2371544 -4.544456 5.507726e-06
## GSG1         15.388658     -1.1738141 0.2978216 -3.941334 8.102983e-05
## PLPP3        21.386384     -2.5109896 0.6641582 -3.780710 1.563819e-04
## PPARG       113.271484     -2.0127696 0.5597499 -3.595838 3.233494e-04
## DNAJC25      72.558825     -0.8098307 0.2285502 -3.543339 3.950952e-04
## GRAMD2B      25.088827     -1.2215689 0.3451259 -3.539487 4.009051e-04
## AL669970.3    4.845053     -1.9350225 0.5563098 -3.478318 5.045710e-04
## H3F3B      4088.262249     -0.5395627 0.1581471 -3.411778 6.454073e-04
##                   padj
## GPAT3      0.001248196
## RUBCNL     0.017070545
## ARRDC3     0.025508116
## GSG1       0.160832643
## PLPP3      0.271596185
## PPARG      0.327657388
## DNAJC25    0.327657388
## GRAMD2B    0.327657388
## AL669970.3 0.389472734
## H3F3B      0.395340255
## 
## $`FCGR3A+ Mono`
##             baseMean log2FoldChange     lfcSE      stat       pvalue
## PTGES       80.90548     -2.0745952 0.3943210 -5.261183 1.431314e-07
## TNNI2      110.30603     -0.8419628 0.1999974 -4.209869 2.555193e-05
## AF127577.2  17.77113     -1.1946666 0.3237292 -3.690327 2.239655e-04
## CKB        211.14827     -0.5983243 0.1664447 -3.594732 3.247254e-04
## PUS10       58.46806     -0.6224566 0.1756193 -3.544353 3.935785e-04
## OASL        46.74276     -1.1408812 0.3237139 -3.524350 4.245227e-04
## ODF2L      101.39500     -0.5062969 0.1529856 -3.309441 9.348233e-04
## PRR7        25.89491     -1.2972399 0.3922860 -3.306872 9.434388e-04
## HSBP1      577.49415     -0.4346097 0.1320913 -3.290222 1.001082e-03
## GNS        470.88475     -0.4022688 0.1230384 -3.269457 1.077542e-03
##                   padj
## PTGES      0.001829077
## TNNI2      0.163264045
## AF127577.2 0.622381461
## CKB        0.622381461
## PUS10      0.622381461
## OASL       0.622381461
## ODF2L      0.782580136
## PRR7       0.782580136
## HSBP1      0.782580136
## GNS        0.782580136
## 
## $T
##              baseMean log2FoldChange     lfcSE      stat     pvalue      padj
## CYBB        14.296499     -0.8872705 0.3608141 -2.459079 0.01392939 0.9983424
## ATP5F1C      6.239390     -1.1671048 0.4815303 -2.423741 0.01536155 0.9983424
## SFT2D1       6.453705     -1.1138868 0.4747278 -2.346369 0.01895731 0.9983424
## BCL6         5.278050     -1.0570873 0.4613071 -2.291504 0.02193425 0.9983424
## LYZ        231.429719     -0.4427538 0.1958985 -2.260119 0.02381386 0.9983424
## MTRNR2L1    38.828218     -2.5782990 1.1461494 -2.249531 0.02447871 0.9983424
## REL          9.739690     -0.8005290 0.3745836 -2.137117 0.03258847 0.9983424
## YPEL3        8.045802     -0.8760953 0.4119422 -2.126744 0.03344139 0.9983424
## VMP1        14.801753     -0.6772379 0.3392266 -1.996418 0.04588847 0.9983424
## ATP2B1-AS1   5.738598     -0.9390538 0.4770244 -1.968565 0.04900301 0.9983424
## 
## $DC
##            baseMean log2FoldChange     lfcSE      stat       pvalue
## SLC9A3R1  157.19622     -0.8008697 0.1434666 -5.582275 2.373928e-08
## LILRA5    122.38877     -0.9867012 0.2221278 -4.442043 8.910869e-06
## FAM118A   205.01285     -0.6973272 0.1574683 -4.428367 9.494935e-06
## C5AR1      68.79782     -1.0543492 0.2669514 -3.949592 7.828454e-05
## APOBEC3A   38.31959     -1.2569278 0.3320899 -3.784903 1.537688e-04
## GK5        48.86185     -0.7305331 0.2057571 -3.550463 3.845542e-04
## CHST15     85.19333     -0.9306613 0.2625800 -3.544297 3.936617e-04
## FCN1     1472.89421     -0.6878119 0.2057424 -3.343073 8.285611e-04
## ERG        14.16904     -1.5063235 0.4540741 -3.317352 9.087517e-04
## BTG3       72.47695     -1.1699251 0.3541385 -3.303581 9.545844e-04
##                  padj
## SLC9A3R1 0.0002900703
## LILRA5   0.0290046522
## FAM118A  0.0290046522
## C5AR1    0.1366512476
## APOBEC3A 0.2288354613
## GK5      0.3809815635
## CHST15   0.3809815635
## FCN1     0.5651264359
## ERG      0.5651264359
## BTG3     0.5651264359
## 
## $Macrophage
##           baseMean log2FoldChange     lfcSE      stat       pvalue      padj
## EXT1    229.095337     -0.7140498 0.1894806 -3.768459 0.0001642587 0.4279816
## ARRDC3   38.373223     -0.7884409 0.2118195 -3.722230 0.0001974714 0.4279816
## TFPI     15.112197     -1.1593352 0.3235935 -3.582690 0.0003400746 0.4566354
## CCDC82   36.672770     -0.7019713 0.2115315 -3.318519 0.0009049618 0.7532456
## VEGFA     7.101706     -1.7449049 0.5491279 -3.177593 0.0014850321 0.8987938
## PJVK     20.376723     -1.6909953 0.5365021 -3.151889 0.0016221779 0.9272549
## ST3GAL1  49.433197     -1.0374753 0.3357297 -3.090210 0.0020001490 0.9990035
## PDE8A    31.649752     -1.0191106 0.3323116 -3.066731 0.0021641320 0.9990035
## UBE2Q2   55.306975     -0.6194901 0.2028154 -3.054454 0.0022547079 0.9990035
## TIGAR    43.810748     -0.5880053 0.1951981 -3.012351 0.0025923278 0.9990035
## 
## $B
##             baseMean log2FoldChange     lfcSE      stat       pvalue      padj
## IGHV3-15   10.498845     -3.4397435 0.8479845 -4.056376 0.0000498401 0.2244798
## CRHBP      31.060840     -1.2709966 0.3320780 -3.827404 0.0001295018 0.2916381
## ID1        10.275158     -2.2715472 0.6803629 -3.338729 0.0008416267 0.9476717
## JAM3       13.399800     -1.3601758 0.4156905 -3.272088 0.0010675633 0.9999354
## PRR11      18.250469     -3.0332235 0.9533345 -3.181699 0.0014641390 0.9999354
## CHMP1B     14.060580     -1.1177545 0.3553718 -3.145310 0.0016591075 0.9999354
## SLC25A29   11.322208     -1.1486275 0.3658656 -3.139480 0.0016924807 0.9999354
## RBPMS      23.075623     -0.9647895 0.3137937 -3.074598 0.0021078672 0.9999354
## ZRANB2-AS2  6.245355     -1.5962932 0.5246160 -3.042784 0.0023440043 0.9999354
## SNED1      18.301063     -1.0725222 0.3528598 -3.039514 0.0023696021 0.9999354

if ( ! dir.exists("de_analysis") ) {
  dir.create("de_analysis")
}

lapply(1:length(assays(pb)),function(i) {
  cellname=names(assays(pb))[[i]]
  myres <- deres[[i]]
  filename <- paste(cellname,".tsv",sep="")
  filename <- gsub("\\)","",gsub("\\(","",gsub(" ","_",filename)))
  filename <- paste("de_analysis/",filename,sep="")
  write.table(x=myres,file=filename,sep="\t",quote=FALSE)
})

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Pathway analysis

Using mitch package with gene ontology terms.

go <- gmt_import("c5.go.v2023.2.Hs.symbols.gmt")
names(go) <- gsub("_"," ",names(go))

str(deres)

## List of 9
##  $ NK 1 (FCGRA3A+)   :'data.frame':  14682 obs. of  30 variables:
##   ..$ baseMean      : num [1:14682] 130.9 45 203.8 410.4 15.1 ...
##   ..$ log2FoldChange: num [1:14682] 2.129 -0.962 -0.706 -0.832 -1.484 ...
##   ..$ lfcSE         : num [1:14682] 0.361 0.177 0.143 0.17 0.309 ...
##   ..$ stat          : num [1:14682] 5.9 -5.44 -4.96 -4.91 -4.8 ...
##   ..$ pvalue        : num [1:14682] 3.69e-09 5.43e-08 7.18e-07 9.20e-07 1.56e-06 ...
##   ..$ padj          : num [1:14682] 4.99e-05 3.68e-04 3.11e-03 3.11e-03 4.23e-03 ...
##   ..$ AH0005        : num [1:14682] 7.41 6.2 7.6 8.18 5.23 ...
##   ..$ AH0015        : num [1:14682] 7.73 6.16 7.67 8.6 5.19 ...
##   ..$ AH0018        : num [1:14682] 7.77 5.72 7.47 8.83 5.57 ...
##   ..$ CC0003        : num [1:14682] 5.51 6.78 8.1 9.7 5.84 ...
##   ..$ CC0016        : num [1:14682] 8.61 6.06 7.7 8.26 5.14 ...
##   ..$ PM001         : num [1:14682] 7.62 5.95 7.58 8.09 5.22 ...
##   ..$ PM0020        : num [1:14682] 6.42 6.68 8.23 8.64 5.54 ...
##   ..$ PM0027        : num [1:14682] 6.41 6.25 8.88 8.93 5.6 ...
##   ..$ PM0028        : num [1:14682] 6.88 6.75 7.97 9.52 6.03 ...
##   ..$ PM0032        : num [1:14682] 6.7 6.59 8.08 8.68 6.07 ...
##   ..$ PM008         : num [1:14682] 7.94 6.15 7.39 8.75 5.43 ...
##   ..$ PM017         : num [1:14682] 7.21 6.24 7.67 8.65 5.24 ...
##   ..$ AH0005        : num [1:14682] 254 74 298 482 15 122 689 934 184 11 ...
##   ..$ AH0015        : num [1:14682] 268 56 255 539 11 60 421 758 234 28 ...
##   ..$ AH0018        : num [1:14682] 82 9 63 189 7 31 99 135 95 8 ...
##   ..$ CC0003        : num [1:14682] 35 186 600 2010 60 5 332 672 854 162 ...
##   ..$ CC0016        : num [1:14682] 559 51 270 426 10 70 594 805 203 25 ...
##   ..$ PM001         : num [1:14682] 126 22 121 186 6 33 287 417 86 17 ...
##   ..$ PM0020        : num [1:14682] 78 103 411 563 23 23 441 671 314 56 ...
##   ..$ PM0027        : num [1:14682] 6 5 53 55 2 2 29 45 61 3 ...
##   ..$ PM0028        : num [1:14682] 55 48 144 472 21 16 131 267 112 25 ...
##   ..$ PM0032        : num [1:14682] 27 24 93 149 13 4 53 143 95 23 ...
##   ..$ PM008         : num [1:14682] 345 60 217 654 20 112 520 581 277 56 ...
##   ..$ PM017         : num [1:14682] 330 121 493 1078 24 ...
##  $ CD14+ Mono        :'data.frame':  15548 obs. of  30 variables:
##   ..$ baseMean      : num [1:15548] 219.6 54.8 6013.2 681.9 68.9 ...
##   ..$ log2FoldChange: num [1:15548] 1.79 2.23 1.5 -0.8 2.92 ...
##   ..$ lfcSE         : num [1:15548] 0.365 0.457 0.31 0.17 0.625 ...
##   ..$ stat          : num [1:15548] 4.91 4.88 4.84 -4.72 4.68 ...
##   ..$ pvalue        : num [1:15548] 8.89e-07 1.05e-06 1.27e-06 2.38e-06 2.90e-06 ...
##   ..$ padj          : num [1:15548] 0.0058 0.0058 0.0058 0.00795 0.00795 ...
##   ..$ AH0005        : num [1:15548] 7.09 6.06 12.13 9.04 6.31 ...
##   ..$ AH0015        : num [1:15548] 8.92 7.26 13.11 8.72 6.97 ...
##   ..$ AH0018        : num [1:15548] 8.89 7.21 13.49 8.92 5.97 ...
##   ..$ CC0003        : num [1:15548] 7.23 5.62 11.85 10.25 5.96 ...
##   ..$ CC0016        : num [1:15548] 8.44 7.09 13.03 8.84 8.66 ...
##   ..$ PM001         : num [1:15548] 8.53 6.79 13.65 9.39 6.51 ...
##   ..$ PM0020        : num [1:15548] 7.32 5.03 11.27 9.8 4.86 ...
##   ..$ PM0027        : num [1:15548] 6.55 5.3 10.91 9.94 4.81 ...
##   ..$ PM0028        : num [1:15548] 6.75 5.29 11.11 9.44 5.08 ...
##   ..$ PM0032        : num [1:15548] 6.35 5.83 12.04 9.69 5.5 ...
##   ..$ PM008         : num [1:15548] 8.4 6.11 12.56 9.28 6.3 ...
##   ..$ PM017         : num [1:15548] 7.82 5.98 12.53 9.51 6.51 ...
##   ..$ AH0005        : num [1:15548] 74 28 3041 341 36 ...
##   ..$ AH0015        : num [1:15548] 649 179 12503 557 139 ...
##   ..$ AH0018        : num [1:15548] 1317 356 33816 1339 109 ...
##   ..$ CC0003        : num [1:15548] 10 2 300 97 3 12 1 11 20 126 ...
##   ..$ CC0016        : num [1:15548] 70 24 1836 95 83 ...
##   ..$ PM001         : num [1:15548] 904 220 33942 1698 170 ...
##   ..$ PM0020        : num [1:15548] 183 14 3397 1201 10 ...
##   ..$ PM0027        : num [1:15548] 153 36 4348 2181 15 ...
##   ..$ PM0028        : num [1:15548] 176 34 4776 1455 24 ...
##   ..$ PM0032        : num [1:15548] 117 67 8836 1689 44 ...
##   ..$ PM008         : num [1:15548] 136 19 2643 262 23 ...
##   ..$ PM017         : num [1:15548] 243 46 7224 862 79 ...
##  $ NK 2 (IL7R+)      :'data.frame':  13653 obs. of  30 variables:
##   ..$ baseMean      : num [1:13653] 4304.1 151.2 316.8 13.4 297.5 ...
##   ..$ log2FoldChange: num [1:13653] 0.673 -1.428 0.7 -2.136 -1.312 ...
##   ..$ lfcSE         : num [1:13653] 0.11 0.234 0.124 0.403 0.254 ...
##   ..$ stat          : num [1:13653] 6.12 -6.11 5.63 -5.3 -5.16 ...
##   ..$ pvalue        : num [1:13653] 9.52e-10 1.00e-09 1.84e-08 1.18e-07 2.54e-07 ...
##   ..$ padj          : num [1:13653] 6.85e-06 6.85e-06 8.37e-05 4.03e-04 6.92e-04 ...
##   ..$ AH0005        : num [1:13653] 12.63 6.57 8.78 5.06 7.69 ...
##   ..$ AH0015        : num [1:13653] 12.46 6.87 8.45 4.93 7.63 ...
##   ..$ AH0018        : num [1:13653] 11.92 7.16 8.48 4.86 8.06 ...
##   ..$ CC0003        : num [1:13653] 11.14 9.05 7.77 6.48 10.08 ...
##   ..$ CC0016        : num [1:13653] 12.46 6.69 9.02 4.76 7.25 ...
##   ..$ PM001         : num [1:13653] 12.33 6.9 8.87 4.7 7.67 ...
##   ..$ PM0020        : num [1:13653] 12.04 7.16 8.11 5.26 8.04 ...
##   ..$ PM0027        : num [1:13653] 11.64 8.08 8.27 5.28 8.96 ...
##   ..$ PM0028        : num [1:13653] 11.79 7.65 8.23 5.48 8.3 ...
##   ..$ PM0032        : num [1:13653] 11.96 7.51 8.03 5.48 7.98 ...
##   ..$ PM008         : num [1:13653] 11.91 7.08 8.43 4.89 8.11 ...
##   ..$ PM017         : num [1:13653] 12.09 7.2 8.43 4.96 7.89 ...
##   ..$ AH0005        : num [1:13653] 5254 54 337 7 145 ...
##   ..$ AH0015        : num [1:13653] 7855 121 444 9 233 ...
##   ..$ AH0018        : num [1:13653] 3557 104 301 5 218 ...
##   ..$ CC0003        : num [1:13653] 1277 286 107 34 603 ...
##   ..$ CC0016        : num [1:13653] 12298 160 1070 9 265 ...
##   ..$ PM001         : num [1:13653] 6014 104 514 4 202 ...
##   ..$ PM0020        : num [1:13653] 4363 117 256 13 241 ...
##   ..$ PM0027        : num [1:13653] 2687 204 236 11 397 ...
##   ..$ PM0028        : num [1:13653] 3568 172 272 18 287 ...
##   ..$ PM0032        : num [1:13653] 1792 68 104 8 100 ...
##   ..$ PM008         : num [1:13653] 3963 108 325 6 254 ...
##   ..$ PM017         : num [1:13653] 6991 187 503 11 332 ...
##  $ CD14+ Mono (CCL4+):'data.frame':  13897 obs. of  30 variables:
##   ..$ baseMean      : num [1:13897] 138.1 44.5 273.7 26.5 97.2 ...
##   ..$ log2FoldChange: num [1:13897] -0.917 -1.651 -1.078 1.685 1.881 ...
##   ..$ lfcSE         : num [1:13897] 0.171 0.35 0.237 0.407 0.458 ...
##   ..$ stat          : num [1:13897] -5.35 -4.71 -4.54 4.14 4.11 ...
##   ..$ pvalue        : num [1:13897] 8.98e-08 2.46e-06 5.51e-06 3.45e-05 3.98e-05 ...
##   ..$ padj          : num [1:13897] 0.00125 0.01707 0.02551 0.1107 0.1107 ...
##   ..$ AH0005        : num [1:13897] 6.45 5.14 7.98 5.14 6.77 ...
##   ..$ AH0015        : num [1:13897] 6.7 5.05 7.6 6.54 7.59 ...
##   ..$ AH0018        : num [1:13897] 7.28 6.01 7.83 6.24 7.68 ...
##   ..$ CC0003        : num [1:13897] 7.79 7.11 9.08 5.18 7.23 ...
##   ..$ CC0016        : num [1:13897] 6.92 5.98 7.93 5.99 7.43 ...
##   ..$ PM001         : num [1:13897] 7.25 5.46 7.61 5.67 7.34 ...
##   ..$ PM0020        : num [1:13897] 8.08 5.78 8.4 4.91 5.08 ...
##   ..$ PM0027        : num [1:13897] 8 6.79 9.32 4.66 5.54 ...
##   ..$ PM0028        : num [1:13897] 7.14 6.31 8.23 4.93 5.58 ...
##   ..$ PM0032        : num [1:13897] 7.45 6.57 8.1 5.33 5.11 ...
##   ..$ PM008         : num [1:13897] 6.91 5.64 7.68 5.94 7.59 ...
##   ..$ PM017         : num [1:13897] 7.22 5.34 7.47 5.27 6.69 ...
##   ..$ AH0005        : num [1:13897] 9 2 32 2 12 6 2 1 16 4 ...
##   ..$ AH0015        : num [1:13897] 45 7 96 39 95 43 7 2 131 26 ...
##   ..$ AH0018        : num [1:13897] 146 46 227 58 202 79 11 4 296 36 ...
##   ..$ CC0003        : num [1:13897] 683 395 1796 52 437 ...
##   ..$ CC0016        : num [1:13897] 264 109 597 110 404 127 25 14 390 67 ...
##   ..$ PM001         : num [1:13897] 216 37 288 48 232 74 13 9 160 41 ...
##   ..$ PM0020        : num [1:13897] 76 10 97 3 4 8 6 10 16 4 ...
##   ..$ PM0027        : num [1:13897] 191 71 505 5 20 20 19 130 36 11 ...
##   ..$ PM0028        : num [1:13897] 69 33 162 6 15 20 14 2 32 9 ...
##   ..$ PM0032        : num [1:13897] 154 73 256 19 14 38 30 7 73 19 ...
##   ..$ PM008         : num [1:13897] 463 132 874 184 808 219 46 40 718 121 ...
##   ..$ PM017         : num [1:13897] 132 20 162 18 84 60 8 5 164 30 ...
##  $ FCGR3A+ Mono      :'data.frame':  12783 obs. of  30 variables:
##   ..$ baseMean      : num [1:12783] 80.9 110.3 17.8 220.6 211.1 ...
##   ..$ log2FoldChange: num [1:12783] -2.075 -0.842 -1.195 1.387 -0.598 ...
##   ..$ lfcSE         : num [1:12783] 0.394 0.2 0.324 0.379 0.166 ...
##   ..$ stat          : num [1:12783] -5.26 -4.21 -3.69 3.66 -3.59 ...
##   ..$ pvalue        : num [1:12783] 1.43e-07 2.56e-05 2.24e-04 2.50e-04 3.25e-04 ...
##   ..$ padj          : num [1:12783] 0.00183 0.16326 0.62238 0.62238 0.62238 ...
##   ..$ AH0005        : num [1:12783] 5.98 6.42 5.01 6.53 8.35 ...
##   ..$ AH0015        : num [1:12783] 5.58 6.49 5.18 7.91 8.02 ...
##   ..$ AH0018        : num [1:12783] 6.17 6.9 5.24 9.52 7.23 ...
##   ..$ CC0003        : num [1:12783] 7.7 7.16 5.83 8.17 8.01 ...
##   ..$ CC0016        : num [1:12783] 6.26 7.24 5.4 8.23 7.74 ...
##   ..$ PM001         : num [1:12783] 5.84 6.83 5.36 8.7 7.5 ...
##   ..$ PM0020        : num [1:12783] 6.6 7.51 5.79 6.55 8.47 ...
##   ..$ PM0027        : num [1:12783] 8.43 7.82 6.33 6.42 8.04 ...
##   ..$ PM0028        : num [1:12783] 6.08 7.33 5.4 6.57 8.32 ...
##   ..$ PM0032        : num [1:12783] 7.41 7.66 5.5 6.83 8.33 ...
##   ..$ PM008         : num [1:12783] 6.22 6.84 5.36 8.09 7.26 ...
##   ..$ PM017         : num [1:12783] 6.2 7.07 5.43 8.32 7.17 ...
##   ..$ AH0005        : num [1:12783] 15 25 3 28 135 26 15 94 1 52 ...
##   ..$ AH0015        : num [1:12783] 14 43 7 154 167 35 16 166 14 85 ...
##   ..$ AH0018        : num [1:12783] 113 239 29 1939 323 ...
##   ..$ CC0003        : num [1:12783] 123 77 19 179 158 62 72 38 12 68 ...
##   ..$ CC0016        : num [1:12783] 61 160 19 361 244 60 26 69 43 208 ...
##   ..$ PM001         : num [1:12783] 29 86 14 406 157 54 39 94 39 121 ...
##   ..$ PM0020        : num [1:12783] 94 216 37 89 465 103 44 41 7 126 ...
##   ..$ PM0027        : num [1:12783] 253 157 40 44 187 58 91 31 5 75 ...
##   ..$ PM0028        : num [1:12783] 39 136 15 67 305 79 49 144 7 70 ...
##   ..$ PM0032        : num [1:12783] 74 91 9 44 156 32 22 50 5 41 ...
##   ..$ PM008         : num [1:12783] 69 130 21 379 191 74 62 218 81 187 ...
##   ..$ PM017         : num [1:12783] 43 103 15 289 112 44 45 230 24 110 ...
##  $ T                 :'data.frame':  1874 obs. of  30 variables:
##   ..$ baseMean      : num [1:1874] 2814.2 603.82 205.64 4.55 7.04 ...
##   ..$ log2FoldChange: num [1:1874] 6.35 5.67 4.73 1.66 1.47 ...
##   ..$ lfcSE         : num [1:1874] 1.689 1.621 1.5 0.601 0.536 ...
##   ..$ stat          : num [1:1874] 3.76 3.5 3.15 2.77 2.74 ...
##   ..$ pvalue        : num [1:1874] 0.000171 0.000472 0.001624 0.005626 0.006178 ...
##   ..$ padj          : num [1:1874] 0.32 0.442 0.998 0.998 0.998 ...
##   ..$ AH0005        : num [1:1874] 13.36 11.1 8.62 2.91 4.12 ...
##   ..$ AH0015        : num [1:1874] 11.45 9.83 8.78 3.7 3.8 ...
##   ..$ AH0018        : num [1:1874] 4.03 2.39 2.39 3.05 3.46 ...
##   ..$ CC0003        : num [1:1874] 9.9 7.93 6.35 2.56 2.72 ...
##   ..$ CC0016        : num [1:1874] 3.13 2.44 2.15 3.56 4.26 ...
##   ..$ PM001         : num [1:1874] 10.18 8.9 6.37 3.47 3.05 ...
##   ..$ PM0020        : num [1:1874] 2.96 2.53 2.53 2.53 2.76 ...
##   ..$ PM0027        : num [1:1874] 3.07 2.79 2.4 2.4 2.4 ...
##   ..$ PM0028        : num [1:1874] 2.77 3.04 2.77 3.04 3.62 ...
##   ..$ PM0032        : num [1:1874] 4.27 3.38 2.85 2.85 3.38 ...
##   ..$ PM008         : num [1:1874] 14.16 11.74 10.53 3.73 3.73 ...
##   ..$ PM017         : num [1:1874] 2.88 3.04 2.7 3.41 3.94 ...
##   ..$ AH0005        : num [1:1874] 3354 698 124 1 4 ...
##   ..$ AH0015        : num [1:1874] 6458 2090 1004 19 21 ...
##   ..$ AH0018        : num [1:1874] 9 1 1 3 5 12 3 4 4 171 ...
##   ..$ CC0003        : num [1:1874] 1617 406 130 3 4 ...
##   ..$ CC0016        : num [1:1874] 6 2 1 10 20 12 1 3 4 323 ...
##   ..$ PM001         : num [1:1874] 1806 738 121 10 6 ...
##   ..$ PM0020        : num [1:1874] 4 2 2 2 3 17 8 11 8 424 ...
##   ..$ PM0027        : num [1:1874] 3 2 1 1 1 8 10 7 4 219 ...
##   ..$ PM0028        : num [1:1874] 2 3 2 3 6 12 6 5 7 187 ...
##   ..$ PM0032        : num [1:1874] 5 2 1 1 2 12 2 5 2 58 ...
##   ..$ PM008         : num [1:1874] 34563 6458 2789 16 16 ...
##   ..$ PM017         : num [1:1874] 4 5 3 8 14 12 9 7 6 226 ...
##  $ DC                :'data.frame':  12221 obs. of  30 variables:
##   ..$ baseMean      : num [1:12221] 157.2 91.1 122.4 205 217.5 ...
##   ..$ log2FoldChange: num [1:12221] -0.801 1.037 -0.987 -0.697 0.903 ...
##   ..$ lfcSE         : num [1:12221] 0.143 0.214 0.222 0.157 0.217 ...
##   ..$ stat          : num [1:12221] -5.58 4.83 -4.44 -4.43 4.15 ...
##   ..$ pvalue        : num [1:12221] 2.37e-08 1.34e-06 8.91e-06 9.49e-06 3.26e-05 ...
##   ..$ padj          : num [1:12221] 0.00029 0.00821 0.029 0.029 0.07641 ...
##   ..$ AH0005        : num [1:12221] 7.16 7.35 7.04 7.37 8.39 ...
##   ..$ AH0015        : num [1:12221] 6.85 7.27 6.33 7.49 8.21 ...
##   ..$ AH0018        : num [1:12221] 7.42 7.31 7.24 7.93 8.21 ...
##   ..$ CC0003        : num [1:12221] 8.32 6.3 7.81 8.05 7.29 ...
##   ..$ CC0016        : num [1:12221] 6.96 7.28 7.01 7.64 8.15 ...
##   ..$ PM001         : num [1:12221] 7.34 7.23 6.75 7.44 8.75 ...
##   ..$ PM0020        : num [1:12221] 7.84 6.24 7.56 8.01 7.75 ...
##   ..$ PM0027        : num [1:12221] 7.81 6.24 7.58 8.27 7.42 ...
##   ..$ PM0028        : num [1:12221] 7.56 6.37 7.17 8.08 7.09 ...
##   ..$ PM0032        : num [1:12221] 7.58 6.92 7.91 8.43 7.79 ...
##   ..$ PM008         : num [1:12221] 7.4 6.74 6.75 7.56 7.44 ...
##   ..$ PM017         : num [1:12221] 7.6 6.72 6.82 7.71 7.91 ...
##   ..$ AH0005        : num [1:12221] 61 72 55 73 163 153 33 9 5 16 ...
##   ..$ AH0015        : num [1:12221] 90 130 55 156 276 307 46 28 10 41 ...
##   ..$ AH0018        : num [1:12221] 148 135 127 223 277 413 44 30 52 28 ...
##   ..$ CC0003        : num [1:12221] 213 38 144 173 94 164 38 21 3 46 ...
##   ..$ CC0016        : num [1:12221] 104 137 108 183 274 293 53 20 21 62 ...
##   ..$ PM001         : num [1:12221] 227 206 135 245 681 538 73 39 32 64 ...
##   ..$ PM0020        : num [1:12221] 99 24 79 113 92 32 51 46 6 38 ...
##   ..$ PM0027        : num [1:12221] 417 104 348 599 306 259 427 230 1 168 ...
##   ..$ PM0028        : num [1:12221] 195 68 141 296 132 84 96 47 3 63 ...
##   ..$ PM0032        : num [1:12221] 151 86 197 293 178 103 81 35 1 50 ...
##   ..$ PM008         : num [1:12221] 192 108 109 220 199 177 49 19 19 44 ...
##   ..$ PM017         : num [1:12221] 144 68 74 158 185 309 25 23 13 34 ...
##  $ Macrophage        :'data.frame':  10290 obs. of  30 variables:
##   ..$ baseMean      : num [1:10290] 548.9 93.9 229.1 38.4 6.5 ...
##   ..$ log2FoldChange: num [1:10290] 0.896 1.153 -0.714 -0.788 2.16 ...
##   ..$ lfcSE         : num [1:10290] 0.224 0.293 0.189 0.212 0.582 ...
##   ..$ stat          : num [1:10290] 4.01 3.94 -3.77 -3.72 3.71 ...
##   ..$ pvalue        : num [1:10290] 6.13e-05 8.31e-05 1.64e-04 1.97e-04 2.08e-04 ...
##   ..$ padj          : num [1:10290] 0.428 0.428 0.428 0.428 0.428 ...
##   ..$ AH0005        : num [1:10290] 9.18 6.9 7.34 5.6 4.44 ...
##   ..$ AH0015        : num [1:10290] 9.84 7.75 7.66 5.88 4.67 ...
##   ..$ AH0018        : num [1:10290] 9.57 7.18 7.56 5.25 4.28 ...
##   ..$ CC0003        : num [1:10290] 8.59 6.65 8.83 5.84 4.14 ...
##   ..$ CC0016        : num [1:10290] 9.11 7.52 7.98 5.84 5.24 ...
##   ..$ PM001         : num [1:10290] 9.77 6.92 7.57 5.86 5.12 ...
##   ..$ PM0020        : num [1:10290] 9.09 5.85 8.32 6.09 4.24 ...
##   ..$ PM0027        : num [1:10290] 9 5.82 7.84 6.28 4.18 ...
##   ..$ PM0028        : num [1:10290] 8.14 6.04 8.03 6.68 4.55 ...
##   ..$ PM0032        : num [1:10290] 8.24 6.81 8.31 6.33 4.21 ...
##   ..$ PM008         : num [1:10290] 9.47 6.86 7.79 5.32 5.13 ...
##   ..$ PM017         : num [1:10290] 8.86 6.86 7.93 5.36 4.78 ...
##   ..$ AH0005        : num [1:10290] 146 25 36 7 1 2 6 35 11 268 ...
##   ..$ AH0015        : num [1:10290] 415 89 83 17 3 3 12 71 26 616 ...
##   ..$ AH0018        : num [1:10290] 305 50 68 7 1 5 1 35 10 568 ...
##   ..$ CC0003        : num [1:10290] 764 163 914 74 3 ...
##   ..$ CC0016        : num [1:10290] 723 219 313 48 23 ...
##   ..$ PM001         : num [1:10290] 1327 152 259 56 22 ...
##   ..$ PM0020        : num [1:10290] 253 17 143 22 1 5 3 37 10 501 ...
##   ..$ PM0027        : num [1:10290] 569 40 240 64 2 ...
##   ..$ PM0028        : num [1:10290] 306 51 282 94 6 38 6 97 33 969 ...
##   ..$ PM0032        : num [1:10290] 444 142 469 92 3 ...
##   ..$ PM008         : num [1:10290] 1763 238 505 48 37 ...
##   ..$ PM017         : num [1:10290] 824 173 410 37 15 ...
##  $ B                 :'data.frame':  9017 obs. of  30 variables:
##   ..$ baseMean      : num [1:9017] 19.5 10.5 11.8 31.1 10.4 ...
##   ..$ log2FoldChange: num [1:9017] 4.12 -3.44 1.89 -1.27 2.07 ...
##   ..$ lfcSE         : num [1:9017] 0.947 0.848 0.49 0.332 0.599 ...
##   ..$ stat          : num [1:9017] 4.35 -4.06 3.85 -3.83 3.45 ...
##   ..$ pvalue        : num [1:9017] 1.35e-05 4.98e-05 1.17e-04 1.30e-04 5.62e-04 ...
##   ..$ padj          : num [1:9017] 0.122 0.224 0.292 0.292 0.883 ...
##   ..$ AH0005        : num [1:9017] 4.24 3.95 5.22 4.9 5.22 ...
##   ..$ AH0015        : num [1:9017] 6.62 3.52 5.23 4.58 5.68 ...
##   ..$ AH0018        : num [1:9017] 3.9 3.71 4.52 5.6 4.44 ...
##   ..$ CC0003        : num [1:9017] 3.75 3.75 3.75 6.49 4.1 ...
##   ..$ CC0016        : num [1:9017] 6.19 3.8 4.97 4.82 4.89 ...
##   ..$ PM001         : num [1:9017] 5.49 3.9 5.32 5.37 4.62 ...
##   ..$ PM0020        : num [1:9017] 3.76 6.92 4.13 5.75 3.76 ...
##   ..$ PM0027        : num [1:9017] 3.93 3.93 3.93 6.2 4.84 ...
##   ..$ PM0028        : num [1:9017] 4.26 4.06 4.81 5.12 4.06 ...
##   ..$ PM0032        : num [1:9017] 3.76 4.57 4.48 5.72 3.76 ...
##   ..$ PM008         : num [1:9017] 5.34 3.57 5.15 5.07 4.81 ...
##   ..$ PM017         : num [1:9017] 5.57 3.74 4.52 6.01 4.33 ...
##   ..$ AH0005        : num [1:9017] 2 1 9 6 9 18 1 5 3 2 ...
##   ..$ AH0015        : num [1:9017] 240 1 63 26 102 41 72 7 8 12 ...
##   ..$ AH0018        : num [1:9017] 2 1 8 32 7 1 36 2 13 37 ...
##   ..$ CC0003        : num [1:9017] 1 1 1 63 3 3 1 23 25 20 ...
##   ..$ CC0016        : num [1:9017] 40 1 11 9 10 16 15 3 8 1 ...
##   ..$ PM001         : num [1:9017] 42 3 35 37 14 48 42 7 12 1 ...
##   ..$ PM0020        : num [1:9017] 1 84 3 30 1 4 14 7 14 2 ...
##   ..$ PM0027        : num [1:9017] 1 1 1 26 6 3 2 23 20 37 ...
##   ..$ PM0028        : num [1:9017] 8 5 20 30 5 14 6 5 15 26 ...
##   ..$ PM0032        : num [1:9017] 1 7 6 29 1 6 1 7 2 29 ...
##   ..$ PM008         : num [1:9017] 52 1 42 38 27 21 56 2 16 12 ...
##   ..$ PM017         : num [1:9017] 27 1 7 42 5 23 4 2 11 10 ...

if ( ! dir.exists("enrichment_analysis") ) {
  dir.create("enrichment_analysis")
}

# mitch multi
m0 <- mitch_import(deres,DEtype="deseq2",joinType="full")

## Note: Mean no. genes in input = 11551.6666666667

## Note: no. genes in output = 17578

## Note: estimated proportion of input genes in output = 1.52

summary(m0)

##  NK 1 (FCGRA3A+)     CD14+ Mono       NK 2 (IL7R+)    CD14+ Mono (CCL4+)
##  Min.   :-5.4366   Min.   :-4.7184   Min.   :-6.109   Min.   :-5.346    
##  1st Qu.:-0.6267   1st Qu.:-0.5812   1st Qu.:-0.697   1st Qu.:-0.642    
##  Median : 0.0271   Median :-0.0019   Median :-0.012   Median :-0.037    
##  Mean   : 0.0001   Mean   : 0.0111   Mean   :-0.045   Mean   :-0.046    
##  3rd Qu.: 0.6493   3rd Qu.: 0.5927   3rd Qu.: 0.649   3rd Qu.: 0.551    
##  Max.   : 5.8977   Max.   : 4.9147   Max.   : 6.117   Max.   : 4.141    
##  NA's   :2896      NA's   :2030      NA's   :3925     NA's   :3681      
##   FCGR3A+ Mono          T                DC           Macrophage    
##  Min.   :-5.261   Min.   :-2.459   Min.   :-5.582   Min.   :-3.768  
##  1st Qu.:-0.625   1st Qu.:-0.550   1st Qu.:-0.680   1st Qu.:-0.599  
##  Median :-0.034   Median :-0.055   Median :-0.045   Median :-0.009  
##  Mean   :-0.036   Mean   :-0.043   Mean   :-0.048   Mean   :-0.001  
##  3rd Qu.: 0.544   3rd Qu.: 0.462   3rd Qu.: 0.587   3rd Qu.: 0.587  
##  Max.   : 3.662   Max.   : 3.759   Max.   : 4.833   Max.   : 4.008  
##  NA's   :4795     NA's   :15704    NA's   :5357     NA's   :7288    
##        B         
##  Min.   :-4.056  
##  1st Qu.:-0.578  
##  Median : 0.033  
##  Mean   : 0.012  
##  3rd Qu.: 0.611  
##  Max.   : 4.351  
##  NA's   :8561

dim(m0)

## [1] 17578     9

# remove genes with 7/9 or more NA values
m0 <- m0[which(apply(m0,1,function(x) { length(which(is.na(x)))<7  } )),]
dim(m0)

## [1] 14197     9

mres0 <- mitch_calc(m0,genesets=go,minsetsize=5,cores=16,priority="effect")

## Warning in mitch_rank(input_profile): Warning: >60% of genes have the same score. This isn't
##             optimal for rank based enrichment analysis.

## Note: Enrichments with large effect sizes may not be 
##             statistically significant.

head(mres0$enrichment_result,20) |> kbl(caption="top enrichments") |> kable_paper("hover", full_width = F)

top enrichments
	set	setSize	pMANOVA	s.NK.1._FCGRA3A_	s.CD14_.Mono	s.NK.2._IL7R__	s.CD14_.Mono._CC	s.FCGR3A_.Mono	s.T	s.DC	s.Macrophage	s.B	p.NK.1._FCGRA3A_	p.CD14_.Mono	p.NK.2._IL7R__	p.CD14_.Mono._CC	p.FCGR3A_.Mono	p.T	p.DC	p.Macrophage	p.B	s.dist	SD	p.adjustMANOVA
2982	GOBP OLFACTORY BULB INTERNEURON DIFFERENTIATION	5	NA	-0.0468749	0.8043468	0.1758761	0.5312906	0.8282821	NaN	0.5369625	0.6497791	-0.5030506	NA	NA	NA	NA	NA	NA	NA	NA	NA	1.616176	NA	NA
4807	GOBP REGULATION OF MAST CELL CHEMOTAXIS	5	0.1785184	-0.4478405	-0.6542047	-0.4143465	-0.7841459	-0.6071991	0.0491473	-0.6102114	-0.4354625	0.3217278	0.2912088	0.1732911	0.9214028	0.0681412	0.3123475	0.3616198	0.4227267	0.8620933	0.0398392	1.565795	0.3580592	0.5616839
3718	GOBP POSITIVE REGULATION OF PLASMINOGEN ACTIVATION	5	0.0119994	0.5291795	-0.3319079	0.8061593	-0.6852716	-0.6504084	-0.0309716	-0.2954518	0.2476856	0.2270875	0.0289418	0.2340076	0.0153699	0.0783854	0.0273005	0.4774223	0.6498655	0.0778675	0.3172920	1.460782	0.5160093	0.1478370
3051	GOBP OXYGEN TRANSPORT	5	NA	0.5259709	-0.3531877	0.7299719	-0.4734019	0.4563189	0.1319997	-0.3753044	0.3643384	0.5918118	NA	NA	NA	NA	NA	NA	NA	NA	NA	1.416984	0.4642098	NA
1305	GOBP GAS TRANSPORT	6	NA	0.5259709	-0.2559393	0.7299719	-0.3472933	0.5273332	0.1319997	-0.3498009	0.3643384	0.5918118	NA	NA	NA	NA	NA	NA	NA	NA	NA	1.377124	0.4312128	NA
4004	GOBP PROTECTION FROM NATURAL KILLER CELL MEDIATED CYTOTOXICITY	5	0.0629256	0.6667297	0.5702412	0.6011061	0.1216200	0.5122965	0.0515253	0.1058671	0.6463433	0.1903025	0.1717262	0.0697088	0.0374350	0.3339561	0.0278640	0.2414559	0.0599051	0.0008635	0.0494734	1.369711	0.2601175	0.3538561
8353	GOMF TAP BINDING	7	0.0012174	0.4829519	0.5249227	0.6958258	0.2327653	0.5372819	-0.0145913	0.4519257	0.4479907	0.3543498	0.0683936	0.0047148	0.0082078	0.1991973	0.0061077	0.6638393	0.0082569	0.0001158	0.0012943	1.366207	0.2044362	0.0319599
6658	GOCC MHC CLASS I PROTEIN COMPLEX	9	0.0000753	0.5665346	0.3851823	0.6270524	0.2250644	0.5407100	0.0189379	0.2158746	0.6686127	0.2793852	0.0244583	0.0031301	0.0029526	0.3246875	0.0048458	0.5453798	0.0005602	0.0000035	0.0012697	1.333268	0.2222422	0.0036524
6204	GOBP VOCALIZATION BEHAVIOR	5	0.1605311	-0.4090008	-0.2720991	-0.6703139	0.2295531	0.0620398	0.1112801	-0.4039157	-0.7112066	-0.5858199	0.0564491	0.8819581	0.1110161	0.6186341	0.2823321	0.1431443	0.3630028	0.1101123	0.1700284	1.331058	0.3520904	0.5390386
4393	GOBP REGULATION OF CARDIAC MUSCLE CELL MEMBRANE POTENTIAL	6	NA	-0.2046661	-0.1940215	-0.2175527	-0.7199074	-0.5816907	NaN	-0.5878752	0.0963401	0.6073098	NA	NA	NA	NA	NA	NA	NA	NA	NA	1.306597	NA	NA
1981	GOBP METANEPHRIC TUBULE MORPHOGENESIS	5	NA	-0.6310979	0.3847268	0.8024147	0.4596475	-0.1365636	NaN	0.4913820	0.0400124	NaN	NA	NA	NA	NA	NA	NA	NA	NA	NA	1.289632	NA	NA
8435	GOMF T CELL RECEPTOR BINDING	10	0.0112284	0.6777235	0.2409427	0.7388547	0.2245939	0.3339541	-0.0144500	0.1962596	0.5710660	0.1390028	0.0129919	0.0123559	0.0012610	0.1335496	0.0464063	0.6668601	0.0208861	0.0006015	0.1340639	1.268622	0.2588778	0.1432891
1992	GOBP MHC CLASS I BIOSYNTHETIC PROCESS	5	0.5116188	0.7508423	0.4405615	0.3022701	0.1866155	0.5251212	0.0713912	0.5839697	0.1379711	0.2781425	0.1673626	0.0759881	0.1602780	0.0383058	0.0536662	0.3480692	0.0325116	0.1149339	0.1136828	1.265828	0.2261669	0.7842342
1233	GOBP FAT PAD DEVELOPMENT	7	0.0959703	-0.1373445	-0.6942792	-0.1368530	-0.6334599	-0.3220268	-0.0618352	-0.5453595	-0.2047893	-0.4547057	0.0688706	0.0092969	0.2542712	0.0382769	0.0324928	0.1024039	0.0788183	0.8997600	0.0402398	1.254801	0.2354210	0.4356463
5568	GOBP RESPONSE TO ZINC ION STARVATION	6	0.0658774	-0.2349679	0.5042035	-0.3900053	0.5176162	0.6481535	-0.0061313	0.4867260	0.4173652	-0.0478135	0.0604735	0.0975426	0.1030263	0.0375966	0.1765629	0.9337724	0.2367782	0.0957241	0.1280322	1.250194	0.3814315	0.3612959
7032	GOCC TRANSCRIPTION FACTOR AP 1 COMPLEX	5	0.0000387	-0.6595987	0.6557830	-0.4343584	0.4994734	0.1212707	0.0453881	0.2488312	0.0114729	-0.4251616	0.0006091	0.0057771	0.0069127	0.0051467	0.1936047	0.2338071	0.2540453	0.2643002	0.0048440	1.250136	0.4419273	0.0022199
7175	GOMF ALPHA GLUCOSIDASE ACTIVITY	5	NA	-0.0911521	0.6768655	-0.1006862	0.3561507	0.3129819	NaN	0.6524657	0.5384256	0.3661407	NA	NA	NA	NA	NA	NA	NA	NA	NA	1.245418	NA	NA
6442	GOCC CYTOSOLIC SMALL RIBOSOMAL SUBUNIT	38	0.0000000	0.1201205	-0.2315719	-0.7901520	-0.7030446	-0.4385926	-0.0260576	-0.3605943	-0.1198536	-0.1093560	0.9334185	0.0129775	0.0000000	0.0000000	0.0000086	0.0397258	0.0182402	0.4839131	0.3457008	1.239379	0.3062706	0.0000000
7881	GOMF NEUROPEPTIDE RECEPTOR ACTIVITY	5	NA	0.3120706	0.1721393	0.1936572	-0.2844895	0.3692500	NaN	0.8247493	-0.6272369	NaN	NA	NA	NA	NA	NA	NA	NA	NA	NA	1.206416	NA	NA
3799	GOBP POSITIVE REGULATION OF RESPONSE TO TYPE II INTERFERON	5	0.5116188	0.0382196	0.2286658	0.4385018	0.4528001	0.7681868	0.0713912	0.3258101	0.3534541	0.4144303	0.1673626	0.0759881	0.1602780	0.0383058	0.0536662	0.3480692	0.0325116	0.1149339	0.1136828	1.203787	0.2200000	0.7842342

head(subset(mres0$enrichment_result,p.adjustMANOVA<0.01),20) |> kbl(caption="top enrichments") |> kable_paper("hover", full_width = F)

top enrichments
	set	setSize	pMANOVA	s.NK.1._FCGRA3A_	s.CD14_.Mono	s.NK.2._IL7R__	s.CD14_.Mono._CC	s.FCGR3A_.Mono	s.T	s.DC	s.Macrophage	s.B	p.NK.1._FCGRA3A_	p.CD14_.Mono	p.NK.2._IL7R__	p.CD14_.Mono._CC	p.FCGR3A_.Mono	p.T	p.DC	p.Macrophage	p.B	s.dist	SD	p.adjustMANOVA
6658	GOCC MHC CLASS I PROTEIN COMPLEX	9	0.0000753	0.5665346	0.3851823	0.6270524	0.2250644	0.5407100	0.0189379	0.2158746	0.6686127	0.2793852	0.0244583	0.0031301	0.0029526	0.3246875	0.0048458	0.5453798	0.0005602	0.0000035	0.0012697	1.3332676	0.2222422	0.0036524
7032	GOCC TRANSCRIPTION FACTOR AP 1 COMPLEX	5	0.0000387	-0.6595987	0.6557830	-0.4343584	0.4994734	0.1212707	0.0453881	0.2488312	0.0114729	-0.4251616	0.0006091	0.0057771	0.0069127	0.0051467	0.1936047	0.2338071	0.2540453	0.2643002	0.0048440	1.2501362	0.4419273	0.0022199
6442	GOCC CYTOSOLIC SMALL RIBOSOMAL SUBUNIT	38	0.0000000	0.1201205	-0.2315719	-0.7901520	-0.7030446	-0.4385926	-0.0260576	-0.3605943	-0.1198536	-0.1093560	0.9334185	0.0129775	0.0000000	0.0000000	0.0000086	0.0397258	0.0182402	0.4839131	0.3457008	1.2393792	0.3062706	0.0000000
6439	GOCC CYTOSOLIC LARGE RIBOSOMAL SUBUNIT	55	0.0000000	0.0961184	-0.1961419	-0.7620206	-0.6334597	-0.4608439	-0.0534745	-0.3453907	-0.1050807	-0.1091125	0.6202175	0.0036791	0.0000000	0.0000000	0.0000000	0.0000009	0.0009514	0.4279397	0.3125547	1.1777673	0.2858315	0.0000000
5404	GOBP RESPONSE TO CORTICOSTERONE	5	0.0000328	-0.6013291	0.4675347	-0.5289375	0.0438001	0.1831392	-0.0741955	0.2963820	0.3174463	-0.5341676	0.0181473	0.0295909	0.0479713	0.0242020	0.6951540	0.0897141	0.0163049	0.0149522	0.0253029	1.1725498	0.4114452	0.0019258
6840	GOCC POLYSOMAL RIBOSOME	29	0.0000000	0.1547316	-0.2034595	-0.6811214	-0.5806937	-0.3924367	-0.0474010	-0.2155780	-0.0408350	-0.1359172	0.7686282	0.0240706	0.0000000	0.0000021	0.0002819	0.0014712	0.1785882	0.9332046	0.1306343	1.0437133	0.2690732	0.0000000
174	GOBP ANTIGEN PROCESSING AND PRESENTATION OF PEPTIDE ANTIGEN VIA MHC CLASS IB	11	0.0000753	0.2937478	0.1916904	0.4493864	0.2911307	0.4443277	0.0189379	0.3137475	0.4946513	0.2929602	0.0244583	0.0031301	0.0029526	0.3246875	0.0048458	0.5453798	0.0005602	0.0000035	0.0012697	1.0180702	0.1462906	0.0036524
6441	GOCC CYTOSOLIC RIBOSOME	109	0.0000000	0.1161091	-0.1764938	-0.6725459	-0.5333003	-0.3614297	-0.0410408	-0.2913931	-0.0980106	-0.0993839	0.6696618	0.0000852	0.0000000	0.0000000	0.0000000	0.0000006	0.0000219	0.2090952	0.1027037	1.0089934	0.2502180	0.0000000
176	GOBP ANTIGEN PROCESSING AND PRESENTATION VIA MHC CLASS IB	15	0.0000466	0.3608409	0.1693360	0.4305613	0.2903743	0.3626802	0.0349164	0.2956653	0.4141032	0.2896287	0.0128205	0.0128543	0.0009335	0.1918178	0.0050656	0.2265488	0.0002358	0.0000017	0.0007852	0.9508529	0.1249792	0.0025152
6659	GOCC MHC PROTEIN COMPLEX	23	0.0000003	0.5681298	0.3962081	0.5247028	0.0960257	0.1828663	-0.0184568	0.1158107	0.2689914	0.0560049	0.0000323	0.0001241	0.0000033	0.4580556	0.0385879	0.3433688	0.0012273	0.0002209	0.0201574	0.9417919	0.2103373	0.0000312
167	GOBP ANTIGEN PROCESSING AND PRESENTATION OF ENDOGENOUS PEPTIDE ANTIGEN	18	0.0000073	0.3142297	0.3132263	0.4080091	0.2888417	0.3575872	-0.0107346	0.2545613	0.3878129	0.2545520	0.0090751	0.0006357	0.0002508	0.3427793	0.0062512	0.6669084	0.0015929	0.0000014	0.0022661	0.9245176	0.1234645	0.0005054
166	GOBP ANTIGEN PROCESSING AND PRESENTATION OF ENDOGENOUS ANTIGEN	22	0.0000015	0.3688583	0.3207871	0.4387995	0.2625187	0.3080397	-0.0213437	0.2178141	0.3582927	0.2535950	0.0035693	0.0001433	0.0000729	0.3086087	0.0043460	0.3709845	0.0015246	0.0000038	0.0054468	0.9142718	0.1310309	0.0001295
2955	GOBP NUCLEOTIDE BINDING OLIGOMERIZATION DOMAIN CONTAINING 1 SIGNALING PATHWAY	8	0.0000351	-0.5972616	0.3439327	-0.2572630	0.1071635	-0.0473110	0.0453168	-0.3612653	-0.1750879	-0.2594565	0.0082670	0.0138654	0.0405996	0.0941705	0.6667040	0.3031575	0.0505378	0.1979762	0.0974319	0.8862604	0.2795373	0.0020469
814	GOBP CYTOPLASMIC TRANSLATION	145	0.0000000	0.1048847	-0.1507584	-0.5622229	-0.4459800	-0.3124125	-0.0296235	-0.2950924	-0.0958619	-0.0806266	0.8203869	0.0006220	0.0000000	0.0000000	0.0000000	0.0000750	0.0000073	0.6924361	0.1227815	0.8660083	0.2128332	0.0000000
6656	GOCC MHC CLASS II PROTEIN COMPLEX	15	0.0000309	0.5750827	0.4151557	0.4664390	-0.0349946	-0.0356225	-0.0381978	0.0940893	0.0783623	-0.0341459	0.0002842	0.0061125	0.0002527	0.8617487	0.6274633	0.1105671	0.0738715	0.1472738	0.4761403	0.8606693	0.2488325	0.0018363
6909	GOCC RESPIRATORY CHAIN COMPLEX IV	19	0.0000775	0.2793538	-0.0407092	0.2859999	-0.5105143	-0.4052640	-0.0140489	-0.3117625	-0.0923801	0.2077131	0.1230326	0.9368514	0.0601170	0.0050836	0.0031573	0.4551236	0.2000152	0.9801178	0.0011654	0.8575707	0.2947907	0.0037114
4038	GOBP PROTEIN FOLDING IN ENDOPLASMIC RETICULUM	11	0.0000002	-0.1304684	-0.3853033	0.1047656	-0.5590238	0.0797084	-0.0321894	-0.3242935	-0.0867069	0.2833130	0.2647324	0.0446577	0.1969039	0.0067013	0.1189970	0.2598919	0.1584910	0.4305702	0.0012820	0.8302435	0.2661668	0.0000159
6636	GOCC LUMENAL SIDE OF ENDOPLASMIC RETICULUM MEMBRANE	25	0.0000268	0.4181203	0.2401054	0.5218601	-0.0357808	0.1371478	-0.0154094	0.0862629	0.1816953	0.1158959	0.0031850	0.0024039	0.0000044	0.4047116	0.0196980	0.3856381	0.0169014	0.0025228	0.0019700	0.7609380	0.1859315	0.0016190
1411	GOBP GRANZYME MEDIATED PROGRAMMED CELL DEATH SIGNALING PATHWAY	8	0.0000523	-0.0092681	0.1976203	0.5969394	0.0243153	-0.3052574	0.0144625	-0.2367026	0.0905486	-0.0643403	0.5638743	0.2407642	0.0202236	0.4940534	0.0495646	0.8355094	0.0654979	0.4176601	0.3923827	0.7468773	0.2615491	0.0027412
7796	GOMF MHC CLASS II PROTEIN COMPLEX BINDING	25	0.0001173	0.4876772	0.3763988	0.3719490	-0.0033635	-0.0359561	-0.0123617	0.0959476	0.0988246	0.0831951	0.0004898	0.0003410	0.0004746	0.4961881	0.4003801	0.5103417	0.0095958	0.0158837	0.0737933	0.7383778	0.1960889	0.0052071

sig <- head(subset(mres0$enrichment_result,p.adjustMANOVA<0.01),30)

sig <- sig[,c(1,4:12)]
rownames(sig) <- sig[,1]
sig[,1]=NULL

colfunc <- colorRampPalette(c("blue", "white", "red"))

heatmap.2(as.matrix(sig),trace="none",scale="none",
  margin=c(10,22),col=colfunc(25),cexCol=0.8, cexRow=0.7)

pdf("multimitch_heatmap.pdf")
heatmap.2(as.matrix(sig),trace="none",scale="none",
  margin=c(10,22),col=colfunc(25),cexCol=0.8, cexRow=0.7)
dev.off()

## png 
##   2

mitch single

par(mfrow=c(1,1))
par(mar=c(5,27,3,1))

message("NK 1 (FCGRA3A+)")

## NK 1 (FCGRA3A+)

m1 <- mitch_import(deres[[1]],DEtype="deseq2",joinType="full")

## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.

## Note: Mean no. genes in input = 14682

## Note: no. genes in output = 14682

## Note: estimated proportion of input genes in output = 1

mres1 <- mitch_calc(m1,genesets=go,minsetsize=5,cores=16,priority="effect")

## Note: Enrichments with large effect sizes may not be
##             statistically significant.

res <- mres1$enrichment_result
res <- subset(res,p.adjustANOVA<0.05)
resup <- subset(res,s.dist>0)
resdn <- subset(res,s.dist<0)
head(resup,10)

##                                                                              set
## 607  GOBP CELL MOTILITY INVOLVED IN CEREBRAL CORTEX RADIAL GLIA GUIDED MIGRATION
## 6134                                     GOCC ALPHA BETA T CELL RECEPTOR COMPLEX
## 3570                  GOBP POSITIVE REGULATION OF MYELOID CELL APOPTOTIC PROCESS
## 8306                                                GOMF T CELL RECEPTOR BINDING
## 6764                          GOCC PROTEASOME CORE COMPLEX ALPHA SUBUNIT COMPLEX
## 1807                                           GOBP MACROPHAGE APOPTOTIC PROCESS
## 6545                                           GOCC MHC CLASS II PROTEIN COMPLEX
## 6548                                                    GOCC MHC PROTEIN COMPLEX
## 6149                                                 GOCC ARP2 3 PROTEIN COMPLEX
## 3004             GOBP PEPTIDE ANTIGEN ASSEMBLY WITH MHC CLASS II PROTEIN COMPLEX
##      setSize       pANOVA    s.dist p.adjustANOVA
## 607        6 1.336535e-03 0.7562233  0.0450649390
## 6134       8 5.093048e-04 0.7096054  0.0230205753
## 3570       8 9.939413e-04 0.6721412  0.0379513123
## 8306      11 1.283127e-04 0.6668009  0.0075559906
## 6764       8 1.251880e-03 0.6587842  0.0435129920
## 1807      10 5.862996e-04 0.6278081  0.0255354350
## 6545      13 9.366443e-05 0.6256994  0.0058449403
## 6548      20 1.609046e-06 0.6195471  0.0002038613
## 6149       9 1.403971e-03 0.6147876  0.0461341417
## 3004      12 3.393236e-04 0.5973756  0.0163070330

head(resdn,10)

##                                                           set setSize
## 3431    GOBP POSITIVE REGULATION OF HORMONE METABOLIC PROCESS       9
## 3589   GOBP POSITIVE REGULATION OF NEUROINFLAMMATORY RESPONSE       9
## 3532      GOBP POSITIVE REGULATION OF MIRNA METABOLIC PROCESS      37
## 7305           GOMF DNA BINDING TRANSCRIPTION FACTOR ACTIVITY     862
## 1163       GOBP EXTERNAL ENCAPSULATING STRUCTURE ORGANIZATION     132
## 7220 GOMF CIS REGULATORY REGION SEQUENCE SPECIFIC DNA BINDING     730
## 7307        GOMF DNA BINDING TRANSCRIPTION REPRESSOR ACTIVITY     220
## 8130                       GOMF SEQUENCE SPECIFIC DNA BINDING    1064
## 7304        GOMF DNA BINDING TRANSCRIPTION ACTIVATOR ACTIVITY     263
## 8257                    GOMF TRANSCRIPTION REGULATOR ACTIVITY    1283
##            pANOVA     s.dist p.adjustANOVA
## 3431 1.767636e-04 -0.7218171  9.987144e-03
## 3589 1.216441e-03 -0.6227084  4.310118e-02
## 3532 8.642574e-04 -0.3165641  3.457857e-02
## 7305 8.818343e-23 -0.1988221  3.686949e-19
## 1163 2.522602e-04 -0.1847256  1.302098e-02
## 7220 1.257236e-14 -0.1688533  1.340581e-11
## 7307 1.715056e-05 -0.1685709  1.419930e-03
## 8130 1.218288e-11 -0.1244816  7.276658e-09
## 7304 1.403581e-03 -0.1147285  4.613414e-02
## 8257 6.428257e-10 -0.1042064  2.150123e-07

s <- c(head(resup$s.dist,10), head(resdn$s.dist,10))
names(s) <- c(head(resup$set,10),head(resdn$set,10))
s <- s[order(s)]
cols <- gsub("1","red",gsub("-1","blue",as.character(sign(s))))
if( length(s) > 1 ) {
  barplot(abs(s),las=1,horiz=TRUE,col=cols,xlab="ES",cex.names=0.8,main="NK 1 (FCGRA3A+)")
  if (! file.exists("enrichment_analysis/NK1.html") ) {
    mitch_report(mres1,outfile="enrichment_analysis/NK1.html")
  }
}

message("CD14+ Mono")

## CD14+ Mono

m2 <- mitch_import(deres[[2]],DEtype="deseq2",joinType="full")

## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.

## Note: Mean no. genes in input = 15548

## Note: no. genes in output = 15548

## Note: estimated proportion of input genes in output = 1

mres2 <- mitch_calc(m2,genesets=go,minsetsize=5,cores=16,priority="effect")

## Note: Enrichments with large effect sizes may not be
##             statistically significant.

res <- mres2$enrichment_result
res <- subset(res,p.adjustANOVA<0.05)
resup <- subset(res,s.dist>0)
resdn <- subset(res,s.dist<0)
head(resup,10)

##                                 set setSize       pANOVA     s.dist
## 5746    GOBP RRNA METABOLIC PROCESS     245 2.167667e-05 0.15786447
## 2274   GOBP NCRNA METABOLIC PROCESS     542 2.285731e-05 0.10688222
## 7284 GOMF ADENYL NUCLEOTIDE BINDING    1263 9.192851e-07 0.08316133
## 6809             GOCC MITOCHONDRION    1395 1.287532e-05 0.07066010
##      p.adjustANOVA
## 5746   0.049354640
## 2274   0.049354640
## 7284   0.007939866
## 6809   0.049354640

head(resdn,10)

## [1] set           setSize       pANOVA        s.dist        p.adjustANOVA
## <0 rows> (or 0-length row.names)

s <- c(head(resup$s.dist,10), head(resdn$s.dist,10))
names(s) <- c(head(resup$set,10),head(resdn$set,10))
s <- s[order(s)]
cols <- gsub("1","red",gsub("-1","blue",as.character(sign(s))))
if( length(s) > 1 ) {
  barplot(abs(s),las=1,horiz=TRUE,col=cols,xlab="ES",cex.names=0.8,main="CD14+ Mono")
  if (! file.exists("enrichment_analysis/CD14mono.html") ) {
    mitch_report(mres2,outfile="enrichment_analysis/CD14mono.html")
  }
}

message("NK 2 (IL7R+)")

## NK 2 (IL7R+)

m3 <- mitch_import(deres[[3]],DEtype="deseq2",joinType="full")

## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.

## Note: Mean no. genes in input = 13653

## Note: no. genes in output = 13653

## Note: estimated proportion of input genes in output = 1

mres3 <- mitch_calc(m3,genesets=go,minsetsize=5,cores=16,priority="effect")

## Note: Enrichments with large effect sizes may not be
##             statistically significant.

res <- mres3$enrichment_result
res <- subset(res,p.adjustANOVA<0.05)
resup <- subset(res,s.dist>0)
resdn <- subset(res,s.dist<0)
head(resup,10)

##                                                           set setSize
## 1958                            GOBP MITOCHONDRIAL DNA REPAIR       5
## 8213                             GOMF T CELL RECEPTOR BINDING      10
## 1261                                       GOBP GAS TRANSPORT       5
## 2953                                    GOBP OXYGEN TRANSPORT       5
## 6075                GOCC ALPHA DNA POLYMERASE PRIMASE COMPLEX       5
## 6467                                         GOCC MCM COMPLEX       8
## 6414                              GOCC IKAPPAB KINASE COMPLEX       5
## 8133                                         GOMF TAP BINDING       7
## 3996 GOBP PROTEIN LOCALIZATION TO SITE OF DOUBLE STRAND BREAK       6
## 7681                       GOMF NITRIC OXIDE SYNTHASE BINDING       6
##            pANOVA    s.dist p.adjustANOVA
## 1958 1.192887e-03 0.8367819  0.0252953136
## 8213 8.148082e-06 0.8145862  0.0003743591
## 1261 1.817635e-03 0.8052755  0.0356204693
## 2953 1.817635e-03 0.8052755  0.0356204693
## 6075 2.134640e-03 0.7929660  0.0395817850
## 6467 1.085471e-04 0.7901796  0.0036196940
## 6414 2.269908e-03 0.7882181  0.0413717646
## 8133 4.348307e-04 0.7677812  0.0114635145
## 3996 1.382433e-03 0.7539386  0.0286817885
## 7681 1.536415e-03 0.7467331  0.0309182405

head(resdn,10)

##                                                                                          set
## 6265                                                  GOCC CYTOSOLIC SMALL RIBOSOMAL SUBUNIT
## 6262                                                  GOCC CYTOSOLIC LARGE RIBOSOMAL SUBUNIT
## 2407                                    GOBP NEGATIVE REGULATION OF INTERLEUKIN 4 PRODUCTION
## 6660                                                                 GOCC POLYSOMAL RIBOSOME
## 6264                                                                 GOCC CYTOSOLIC RIBOSOME
## 8226                                                GOMF UBIQUITIN LIGASE INHIBITOR ACTIVITY
## 3426 GOBP POSITIVE REGULATION OF INTRINSIC APOPTOTIC SIGNALING PATHWAY BY P53 CLASS MEDIATOR
## 6192                                                                    GOCC CILIARY ROOTLET
## 787                                                             GOBP CYTOPLASMIC TRANSLATION
## 7965                                    GOMF PROTEIN TYROSINE THREONINE PHOSPHATASE ACTIVITY
##      setSize       pANOVA     s.dist p.adjustANOVA
## 6265      38 2.644742e-20 -0.8646346  3.645336e-17
## 6262      55 9.476974e-27 -0.8335183  2.612486e-23
## 2407       6 1.030183e-03 -0.7736987  2.230265e-02
## 6660      29 4.119946e-12 -0.7435003  9.577164e-10
## 6264     109 4.297979e-40 -0.7335778  3.554429e-36
## 8226       8 3.861486e-04 -0.7246244  1.047032e-02
## 3426       6 2.908866e-03 -0.7018392  4.919493e-02
## 6192      10 7.254935e-04 -0.6172103  1.731043e-02
## 787      145 3.406841e-37 -0.6124881  1.408729e-33
## 7965       9 2.431660e-03 -0.5836021  4.352776e-02

s <- c(head(resup$s.dist,10), head(resdn$s.dist,10))
names(s) <- c(head(resup$set,10),head(resdn$set,10))
s <- s[order(s)]
cols <- gsub("1","red",gsub("-1","blue",as.character(sign(s))))
if( length(s) > 1 ) {
  barplot(abs(s),las=1,horiz=TRUE,col=cols,xlab="ES",cex.names=0.8,main="NK 2 (IL7R+)")
  if (! file.exists("enrichment_analysis/NK2.html") ) {
    mitch_report(mres3,outfile="enrichment_analysis/NK2.html")
  }
}

message("CD14+ Mono (CCL4+)")

## CD14+ Mono (CCL4+)

m4 <- mitch_import(deres[[4]],DEtype="deseq2",joinType="full")

## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.

## Note: Mean no. genes in input = 13897

## Note: no. genes in output = 13897

## Note: estimated proportion of input genes in output = 1

mres4 <- mitch_calc(m4,genesets=go,minsetsize=5,cores=16,priority="effect")

## Note: Enrichments with large effect sizes may not be
##             statistically significant.

res <- mres4$enrichment_result
res <- subset(res,p.adjustANOVA<0.05)
resup <- subset(res,s.dist>0)
resdn <- subset(res,s.dist<0)
head(resup,10)

##                                                   set setSize       pANOVA
## 2012               GOBP MITOCHONDRIAL DNA REPLICATION      13 2.553776e-04
## 6735                    GOCC NUCLEAR REPLICATION FORK      30 2.557647e-05
## 1978                       GOBP MICROTUBULE ANCHORING      20 6.003216e-04
## 7682 GOMF LIGASE ACTIVITY FORMING CARBON SULFUR BONDS      30 8.749009e-05
## 4422         GOBP REGULATION OF CELL CYCLE CHECKPOINT      39 2.595989e-04
## 8185              GOMF RNA METHYLTRANSFERASE ACTIVITY      59 4.383457e-04
## 7195        GOMF ATP DEPENDENT ACTIVITY ACTING ON DNA      99 3.338083e-05
## 2942                  GOBP NUCLEOTIDE EXCISION REPAIR      80 3.617883e-04
## 959                GOBP DNA TEMPLATED DNA REPLICATION     139 4.980879e-06
## 6007                           GOBP TRNA MODIFICATION      89 2.864340e-04
##         s.dist p.adjustANOVA
## 2012 0.5857434   0.027665907
## 6735 0.4441143   0.005271248
## 1978 0.4432658   0.046969604
## 7682 0.4138795   0.013441659
## 4422 0.3381070   0.027767229
## 8185 0.2647953   0.039828186
## 7195 0.2415633   0.006131914
## 2942 0.2308497   0.033967903
## 959  0.2246405   0.001503158
## 6007 0.2226669   0.028813899

head(resdn,10)

##                                                         set setSize
## 6416                 GOCC CYTOSOLIC SMALL RIBOSOMAL SUBUNIT      38
## 8405               GOMF UBIQUITIN LIGASE INHIBITOR ACTIVITY       8
## 6413                 GOCC CYTOSOLIC LARGE RIBOSOMAL SUBUNIT      55
## 6814                                GOCC POLYSOMAL RIBOSOME      29
## 7503                   GOMF GLUTATHIONE PEROXIDASE ACTIVITY      14
## 6415                                GOCC CYTOSOLIC RIBOSOME     109
## 6883                      GOCC RESPIRATORY CHAIN COMPLEX IV      19
## 3753 GOBP POSITIVE REGULATION OF PROTEIN POLYUBIQUITINATION      15
## 807                            GOBP CYTOPLASMIC TRANSLATION     145
## 2685               GOBP NEGATIVE REGULATION OF RNA SPLICING      23
##            pANOVA     s.dist p.adjustANOVA
## 6416 3.695736e-15 -0.7368649  7.807242e-12
## 8405 4.847777e-04 -0.7123083  4.267054e-02
## 6413 1.624688e-17 -0.6636272  4.576205e-14
## 6814 1.448120e-08 -0.6079687  1.019718e-05
## 7503 1.812481e-04 -0.5778804  2.243126e-02
## 6415 7.168268e-24 -0.5583448  6.057187e-20
## 6883 5.546334e-05 -0.5342192  9.012793e-03
## 3753 4.320260e-04 -0.5249004  3.968065e-02
## 807  3.262761e-22 -0.4663898  1.378517e-18
## 2685 5.328409e-04 -0.4172710  4.446468e-02

s <- c(head(resup$s.dist,10), head(resdn$s.dist,10))
names(s) <- c(head(resup$set,10),head(resdn$set,10))
s <- s[order(s)]
cols <- gsub("1","red",gsub("-1","blue",as.character(sign(s))))
if( length(s) > 1 ) {
  barplot(abs(s),las=1,horiz=TRUE,col=cols,xlab="ES",cex.names=0.8,main="CD14+ Mono (CCL4+)")
  if (! file.exists("enrichment_analysis/CD14monoCCL4.html") ) {
    mitch_report(mres4,outfile="enrichment_analysis/CD14monoCCL4.html")
  }
}

message("FCGR3A+ Mono")

## FCGR3A+ Mono

m5 <- mitch_import(deres[[5]],DEtype="deseq2",joinType="full")

## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.

## Note: Mean no. genes in input = 12783

## Note: no. genes in output = 12783

## Note: estimated proportion of input genes in output = 1

mres5 <- mitch_calc(m5,genesets=go,minsetsize=5,cores=16,priority="effect")

## Note: Enrichments with large effect sizes may not be
##             statistically significant.

res <- mres5$enrichment_result
res <- subset(res,p.adjustANOVA<0.05)
resup <- subset(res,s.dist>0)
resdn <- subset(res,s.dist<0)
head(resup,10)

##               set setSize       pANOVA    s.dist p.adjustANOVA
## 6107 GOCC CAVEOLA      45 3.268335e-05 0.3580573    0.01672774

head(resdn,10)

##                                          set setSize       pANOVA     s.dist
## 6220  GOCC CYTOSOLIC LARGE RIBOSOMAL SUBUNIT      55 3.685613e-11 -0.5157905
## 6223  GOCC CYTOSOLIC SMALL RIBOSOMAL SUBUNIT      38 1.654984e-07 -0.4907642
## 6608                 GOCC POLYSOMAL RIBOSOME      29 4.273153e-05 -0.4391266
## 6222                 GOCC CYTOSOLIC RIBOSOME     109 3.176493e-13 -0.4043038
## 779             GOBP CYTOPLASMIC TRANSLATION     144 4.931039e-13 -0.3493706
## 8030 GOMF STRUCTURAL CONSTITUENT OF RIBOSOME     156 2.585689e-10 -0.2938230
## 6397            GOCC LARGE RIBOSOMAL SUBUNIT     112 1.540361e-07 -0.2874195
## 6609                           GOCC POLYSOME      61 1.203706e-04 -0.2848841
## 6683                  GOCC RIBOSOMAL SUBUNIT     180 1.319753e-09 -0.2626888
## 2928          GOBP OXIDATIVE PHOSPHORYLATION     127 4.399774e-07 -0.2599508
##      p.adjustANOVA
## 6220  1.006049e-07
## 6223  1.936095e-04
## 6608  1.944047e-02
## 6222  2.019014e-09
## 779   2.019014e-09
## 8030  5.293552e-07
## 6397  1.936095e-04
## 6609  4.928576e-02
## 6683  2.161491e-06
## 2928  4.503718e-04

s <- c(head(resup$s.dist,10), head(resdn$s.dist,10))
names(s) <- c(head(resup$set,10),head(resdn$set,10))
s <- s[order(s)]
cols <- gsub("1","red",gsub("-1","blue",as.character(sign(s))))
if( length(s) > 1 ) {
  barplot(abs(s),las=1,horiz=TRUE,col=cols,xlab="ES",cex.names=0.8,main="FCGR3A+ Mono")
  if (! file.exists("enrichment_analysis/FCGR3Amono.html") ) {
    mitch_report(mres5,outfile="enrichment_analysis/FCGR3Amono.html")
  }
}

message("T")

## T

m6 <- mitch_import(deres[[6]],DEtype="deseq2",joinType="full")

## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.

## Note: Mean no. genes in input = 1874

## Note: no. genes in output = 1874

## Note: estimated proportion of input genes in output = 1

mres6 <- mitch_calc(m6,genesets=go,minsetsize=5,cores=16,priority="effect")

## Note: Enrichments with large effect sizes may not be
##             statistically significant.

res <- mres6$enrichment_result
res <- subset(res,p.adjustANOVA<0.05)
resup <- subset(res,s.dist>0)
resdn <- subset(res,s.dist<0)
head(resup,10)

## [1] set           setSize       pANOVA        s.dist        p.adjustANOVA
## <0 rows> (or 0-length row.names)

head(resdn,10)

##                                          set setSize       pANOVA     s.dist
## 3033  GOCC CYTOSOLIC LARGE RIBOSOMAL SUBUNIT      49 1.179107e-06 -0.4051104
## 3111            GOCC LARGE RIBOSOMAL SUBUNIT      51 5.443373e-06 -0.3719252
## 3754 GOMF STRUCTURAL CONSTITUENT OF RIBOSOME      86 7.829888e-07 -0.3140185
## 3035                 GOCC CYTOSOLIC RIBOSOME      88 7.631607e-07 -0.3109157
## 3252                  GOCC RIBOSOMAL SUBUNIT      87 2.416460e-06 -0.2981881
## 3253                           GOCC RIBOSOME     100 4.696085e-07 -0.2981623
##      p.adjustANOVA
## 3033  0.0011242785
## 3111  0.0034601708
## 3754  0.0009954398
## 3035  0.0009954398
## 3252  0.0018432758
## 3253  0.0009954398

s <- c(head(resup$s.dist,10), head(resdn$s.dist,10))
names(s) <- c(head(resup$set,10),head(resdn$set,10))
s <- s[order(s)]
cols <- gsub("1","red",gsub("-1","blue",as.character(sign(s))))
if( length(s) > 1 ) {
  barplot(abs(s),las=1,horiz=TRUE,col=cols,xlab="ES",cex.names=0.8,main="T")
  if (! file.exists("enrichment_analysis/T.html") ) {
    mitch_report(mres6,outfile="enrichment_analysis/T.html")
  }
}

message("DC")

## DC

m7 <- mitch_import(deres[[7]],DEtype="deseq2",joinType="full")

## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.

## Note: Mean no. genes in input = 12221

## Note: no. genes in output = 12221

## Note: estimated proportion of input genes in output = 1

mres7 <- mitch_calc(m7,genesets=go,minsetsize=5,cores=16,priority="effect")

## Note: Enrichments with large effect sizes may not be
##             statistically significant.

res <- mres7$enrichment_result
res <- subset(res,p.adjustANOVA<0.05)
resup <- subset(res,s.dist>0)
resdn <- subset(res,s.dist<0)
head(resup,10)

##                                                         set setSize
## 1955                    GOBP MITOCHONDRIAL RNA MODIFICATION      10
## 824           GOBP DEOXYRIBONUCLEOTIDE BIOSYNTHETIC PROCESS      12
## 1962                     GOBP MITOCHONDRIAL TRNA PROCESSING      14
## 5789 GOBP TRNA THREONYLCARBAMOYLADENOSINE METABOLIC PROCESS      17
## 7007               GOMF CATALYTIC ACTIVITY ACTING ON A RRNA      21
## 1932               GOBP MITOCHONDRIAL DNA METABOLIC PROCESS      20
## 4341               GOBP REGULATION OF DNA DAMAGE CHECKPOINT      24
## 5784                                  GOBP TRNA METHYLATION      40
## 7866                    GOMF RNA METHYLTRANSFERASE ACTIVITY      58
## 2772                        GOBP NON MOTILE CILIUM ASSEMBLY      52
##            pANOVA    s.dist p.adjustANOVA
## 1955 6.217545e-04 0.6249283   0.034116677
## 824  6.367725e-04 0.5694433   0.034474862
## 1962 2.820269e-04 0.5605449   0.022022501
## 5789 8.070495e-05 0.5522972   0.009747546
## 7007 1.543346e-04 0.4770570   0.015473470
## 1932 5.379888e-04 0.4471273   0.031431703
## 4341 4.323557e-04 0.4151226   0.028089285
## 5784 1.587838e-04 0.3452836   0.015559997
## 7866 1.854729e-04 0.2840015   0.017514251
## 2772 7.958755e-04 0.2690917   0.040395656

head(resdn,10)

##                                                         set setSize
## 7844                             GOMF RAGE RECEPTOR BINDING       9
## 2437    GOBP NEGATIVE REGULATION OF MIRNA METABOLIC PROCESS      18
## 7177                      GOMF EXTRACELLULAR MATRIX BINDING      17
## 451  GOBP CELLULAR RESPONSE TO ARSENIC CONTAINING SUBSTANCE      17
## 5755        GOBP TRANSFORMING GROWTH FACTOR BETA PRODUCTION      21
## 2243       GOBP NEGATIVE REGULATION OF CELL MATRIX ADHESION      20
## 6179                 GOCC CYTOSOLIC SMALL RIBOSOMAL SUBUNIT      38
## 6176                 GOCC CYTOSOLIC LARGE RIBOSOMAL SUBUNIT      55
## 1833                     GOBP MEGAKARYOCYTE DIFFERENTIATION      36
## 780                            GOBP CYTOPLASMIC TRANSLATION     144
##            pANOVA     s.dist p.adjustANOVA
## 7844 6.307889e-04 -0.6579503  3.438011e-02
## 2437 2.482072e-04 -0.4989028  2.037190e-02
## 7177 7.101974e-04 -0.4743479  3.720976e-02
## 451  1.118235e-03 -0.4566102  4.962395e-02
## 5755 4.046300e-04 -0.4459407  2.663725e-02
## 2243 8.829575e-04 -0.4296041  4.242898e-02
## 6179 6.389808e-06 -0.4232127  1.921912e-03
## 6176 1.995367e-07 -0.4055326  1.620438e-04
## 1833 1.256902e-04 -0.3694707  1.360676e-02
## 780  7.664105e-13 -0.3465449  6.224019e-09

s <- c(head(resup$s.dist,10), head(resdn$s.dist,10))
names(s) <- c(head(resup$set,10),head(resdn$set,10))
s <- s[order(s)]
cols <- gsub("1","red",gsub("-1","blue",as.character(sign(s))))
if( length(s) > 1 ) {
  barplot(abs(s),las=1,horiz=TRUE,col=cols,xlab="ES",cex.names=0.8,main="DC")
  if (! file.exists("enrichment_analysis/DC.html") ) {
    mitch_report(mres7,outfile="enrichment_analysis/DC.html")
  }
}

message("Macrophage")

## Macrophage

m8 <- mitch_import(deres[[8]],DEtype="deseq2",joinType="full")

## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.

## Note: Mean no. genes in input = 10290

## Note: no. genes in output = 10290

## Note: estimated proportion of input genes in output = 1

mres8 <- mitch_calc(m8,genesets=go,minsetsize=5,cores=16,priority="effect")

## Note: Enrichments with large effect sizes may not be
##             statistically significant.

res <- mres8$enrichment_result
res <- subset(res,p.adjustANOVA<0.05)
resup <- subset(res,s.dist>0)
resdn <- subset(res,s.dist<0)
head(resup,10)

## [1] set           setSize       pANOVA        s.dist        p.adjustANOVA
## <0 rows> (or 0-length row.names)

head(resdn,10)

## [1] set           setSize       pANOVA        s.dist        p.adjustANOVA
## <0 rows> (or 0-length row.names)

s <- c(head(resup$s.dist,10), head(resdn$s.dist,10))
names(s) <- c(head(resup$set,10),head(resdn$set,10))
s <- s[order(s)]
cols <- gsub("1","red",gsub("-1","blue",as.character(sign(s))))
if( length(s) > 1 ) {
  barplot(abs(s),las=1,horiz=TRUE,col=cols,xlab="ES",cex.names=0.8,main="Macrophage")
  if (! file.exists("enrichment_analysis/macrophage.html") ) {
    mitch_report(mres8,outfile="enrichment_analysis/macrophage.html")
  }
}

message("B")

## B

m9 <- mitch_import(deres[[9]],DEtype="deseq2",joinType="full")

## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.

## Note: Mean no. genes in input = 9017

## Note: no. genes in output = 9017

## Note: estimated proportion of input genes in output = 1

mres9 <- mitch_calc(m9,genesets=go,minsetsize=5,cores=16,priority="effect")

## Note: Enrichments with large effect sizes may not be
##             statistically significant.

res <- mres9$enrichment_result
res <- subset(res,p.adjustANOVA<0.05)
resup <- subset(res,s.dist>0)
resdn <- subset(res,s.dist<0)
head(resup,10)

##                                                                set setSize
## 5626                        GOCC HIGH DENSITY LIPOPROTEIN PARTICLE       5
## 5372                                          GOCC BRCA1 C COMPLEX       6
## 5891            GOCC PROTEASOME CORE COMPLEX ALPHA SUBUNIT COMPLEX       7
## 7130                    GOMF THREONINE TYPE ENDOPEPTIDASE ACTIVITY       7
## 5449                                              GOCC CMG COMPLEX       7
## 823  GOBP DOUBLE STRAND BREAK REPAIR VIA BREAK INDUCED REPLICATION       9
## 5801                        GOCC OLIGOSACCHARYLTRANSFERASE COMPLEX      12
## 6365                           GOMF DNA REPLICATION ORIGIN BINDING      12
## 6063                                                 GOCC U4 SNRNP      10
## 5890                                  GOCC PROTEASOME CORE COMPLEX      18
##            pANOVA    s.dist p.adjustANOVA
## 5626 5.925268e-04 0.8869951   0.024073897
## 5372 1.407879e-03 0.7527466   0.043143139
## 5891 8.123685e-04 0.7308705   0.030128456
## 7130 1.213907e-03 0.7062312   0.039818305
## 5449 1.220103e-03 0.7059141   0.039818305
## 823  7.239726e-04 0.6507549   0.027889882
## 5801 1.051229e-04 0.6466593   0.006497852
## 6365 1.787836e-04 0.6248010   0.010022970
## 6063 7.389734e-04 0.6163650   0.028127663
## 5890 2.062864e-05 0.5798052   0.001676251

head(resdn,10)

##                                                             set setSize
## 1450 GOBP LEUKOCYTE MIGRATION INVOLVED IN INFLAMMATORY RESPONSE       8
## 3980          GOBP REGULATION OF HETEROTYPIC CELL CELL ADHESION      10
## 5635                            GOCC IGG IMMUNOGLOBULIN COMPLEX      10
## 5194                      GOBP T HELPER 17 CELL DIFFERENTIATION      13
## 5                        GOBP 3 UTR MEDIATED MRNA STABILIZATION      16
## 4683                                    GOBP RESPONSE TO LECTIN      13
## 868                            GOBP ENDOCRINE HORMONE SECRETION      13
## 6728                             GOMF NUCLEAR RECEPTOR ACTIVITY      20
## 1224                        GOBP HETEROTYPIC CELL CELL ADHESION      25
## 3047            GOBP POSITIVE REGULATION OF MIRNA TRANSCRIPTION      25
##            pANOVA     s.dist p.adjustANOVA
## 1450 0.0006736342 -0.6942224    0.02662143
## 3980 0.0005996965 -0.6267792    0.02422536
## 5635 0.0013626676 -0.5849228    0.04284701
## 5194 0.0010280319 -0.5259543    0.03574388
## 5    0.0003753240 -0.5137346    0.01753697
## 4683 0.0013558676 -0.5133274    0.04284701
## 868  0.0015978700 -0.5057069    0.04678460
## 6728 0.0004311410 -0.4548738    0.01936653
## 1224 0.0003731442 -0.4113701    0.01753697
## 3047 0.0004073432 -0.4087011    0.01852771

s <- c(head(resup$s.dist,10), head(resdn$s.dist,10))
names(s) <- c(head(resup$set,10),head(resdn$set,10))
s <- s[order(s)]
cols <- gsub("1","red",gsub("-1","blue",as.character(sign(s))))
if( length(s) > 1 ) {
  barplot(abs(s),las=1,horiz=TRUE,col=cols,xlab="ES",cex.names=0.8,main="B")
  if (! file.exists("enrichment_analysis/B.html") ) {
    mitch_report(mres9,outfile="enrichment_analysis/B.html")
  }
}

Heatmap of combined results

pw <- unique(c(head(subset(mres1$enrichment_result,p.adjustANOVA<0.01 & s.dist>0.3),5)$set,
  head(subset(mres2$enrichment_result,p.adjustANOVA<0.01 & s.dist>0.3),6)$set,
  head(subset(mres3$enrichment_result,p.adjustANOVA<0.01 & s.dist>0.3),6)$set,
  head(subset(mres4$enrichment_result,p.adjustANOVA<0.01 & s.dist>0.3),6)$set,
  head(subset(mres5$enrichment_result,p.adjustANOVA<0.01 & s.dist>0.3),6)$set,
  head(subset(mres6$enrichment_result,p.adjustANOVA<0.01 & s.dist>0.3),6)$set,
  head(subset(mres7$enrichment_result,p.adjustANOVA<0.01 & s.dist>0.3),6)$set,
  head(subset(mres8$enrichment_result,p.adjustANOVA<0.01 & s.dist>0.3),6)$set,
  head(subset(mres9$enrichment_result,p.adjustANOVA<0.01 & s.dist>0.3),6)$set,
  head(subset(mres1$enrichment_result,p.adjustANOVA<0.01 & s.dist< -0.3),6)$set,
  head(subset(mres2$enrichment_result,p.adjustANOVA<0.01 & s.dist< -0.3),6)$set,
  head(subset(mres3$enrichment_result,p.adjustANOVA<0.01 & s.dist< -0.3),6)$set,
  head(subset(mres4$enrichment_result,p.adjustANOVA<0.01 & s.dist< -0.3),6)$set,
  head(subset(mres5$enrichment_result,p.adjustANOVA<0.01 & s.dist< -0.3),6)$set,
  head(subset(mres6$enrichment_result,p.adjustANOVA<0.01 & s.dist< -0.3),6)$set,
  head(subset(mres7$enrichment_result,p.adjustANOVA<0.01 & s.dist< -0.3),6)$set,
  head(subset(mres8$enrichment_result,p.adjustANOVA<0.01 & s.dist< -0.3),6)$set,
  head(subset(mres9$enrichment_result,p.adjustANOVA<0.01 & s.dist< -0.3),6)$set))

x1 <- mres1$enrichment_result[which(mres1$enrichment_result$set %in% pw),c("set","s.dist")]
x2 <- mres2$enrichment_result[which(mres2$enrichment_result$set %in% pw),c("set","s.dist")]
x3 <- mres3$enrichment_result[which(mres3$enrichment_result$set %in% pw),c("set","s.dist")]
x4 <- mres4$enrichment_result[which(mres4$enrichment_result$set %in% pw),c("set","s.dist")]
x5 <- mres5$enrichment_result[which(mres5$enrichment_result$set %in% pw),c("set","s.dist")]
x6 <- mres6$enrichment_result[which(mres6$enrichment_result$set %in% pw),c("set","s.dist")]
x7 <- mres7$enrichment_result[which(mres7$enrichment_result$set %in% pw),c("set","s.dist")]
x8 <- mres8$enrichment_result[which(mres8$enrichment_result$set %in% pw),c("set","s.dist")]
x9 <- mres9$enrichment_result[which(mres9$enrichment_result$set %in% pw),c("set","s.dist")]

jlist <- list(x1,x2,x3,x4,x5,x6,x7,x8,x9)

jj <- join_all(jlist,by="set")
rownames(jj) <- jj$set
jj$set=NULL
colnames(jj) <- names(deres)
colfunc <- colorRampPalette(c("blue", "white", "red"))

heatmap.2(as.matrix(jj),trace="none",scale="none",
  margin=c(10,20),col=colfunc(25),cexCol=0.8)

Differential expression2 KIR3DL2 positive cells

We are going to use muscat for pseudobulk analysis. First need to convert seurat obj to singlecellexperiment object. Then summarise counts to pseudobulk level.

Cells to look at:

DCs
NK2
Macrophage

sc <- assay(sce)
table(sc[which(rownames(sc)=="KIR3DL2"),])

## 
##     0     1     2     3     4     5     6 
## 34093  1144   281    64    14     6     1

KIR3DL2_2 <- sc[,sc[which(rownames(sc)=="KIR3DL2"),]>1]
KIR3DL2_0 <- sc[,sc[which(rownames(sc)=="KIR3DL2"),]<1]
summary(colSums(KIR3DL2_2))

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    3430    5991    7220   10079    9602   77471

summary(colSums(KIR3DL2_0))

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     507    6003    9366   11375   15524  106134

par(mfrow=c(2,1))
hist(colSums(KIR3DL2_2),breaks=50,xlim=c(0,1e5),main="KIR3DL2+")
hist(colSums(KIR3DL2_0),breaks=50,xlim=c(0,1e5),main="KIR3DL2-")

par(mfrow=c(1,1))

low <- as.numeric(sc[which(rownames(sc)=="KIR3DL2"),]==0) * 1
med <- as.numeric(sc[which(rownames(sc)=="KIR3DL2"),]==1) * 2
high <- as.numeric(sc[which(rownames(sc)=="KIR3DL2"),]>1) * 3
KIR3DL2 <- data.frame(low,med,high)
KIR3DL2$sum <- rowSums(KIR3DL2)
head(KIR3DL2)

##   low med high sum
## 1   1   0    0   1
## 2   1   0    0   1
## 3   1   0    0   1
## 4   1   0    0   1
## 5   1   0    0   1
## 6   1   0    0   1

table(KIR3DL2$sum)

## 
##     1     2     3 
## 34093  1144   366

colData(sce)$KIR3DL2 <- KIR3DL2$sum
# change cluster id names to incorporate KIR3DL2 counts
colData(sce)$cluster_id <- paste(colData(sce)$cluster_id,colData(sce)$KIR3DL2)

sc_low <- sc[,KIR3DL2$sum==1]
sc_med <- sc[,KIR3DL2$sum==2]
sc_hi <- sc[,KIR3DL2$sum==3]

summary(colSums(sc_low))

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     507    6003    9366   11375   15524  106134

summary(colSums(sc_med))

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     661    5570    6750    9066    9322   48659

summary(colSums(sc_hi))

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    3430    5991    7220   10079    9602   77471

dim(sc_low)

## [1] 36603 34093

dim(sc_med)

## [1] 36603  1144

dim(sc_hi)

## [1] 36603   366

colDat_hi <- subset(colData(sce),KIR3DL2==3)
colDat_med <- subset(colData(sce),KIR3DL2==2)
colDat_low <- subset(colData(sce),KIR3DL2==1)

table(colDat_low$ident)/nrow(colDat_low)*100

## 
##    NK 1 (FCGRA3A+)         CD14+ Mono       NK 2 (IL7R+) CD14+ Mono (CCL4+) 
##          27.225530          26.184261          14.844690          10.773473 
##       FCGR3A+ Mono                  T                 DC         Macrophage 
##           8.189364           5.666852           4.185610           1.883085 
##                  B 
##           1.047136

table(colDat_med$ident)/nrow(colDat_med)*100

## 
##    NK 1 (FCGRA3A+)         CD14+ Mono       NK 2 (IL7R+) CD14+ Mono (CCL4+) 
##        71.59090909         7.77972028        11.62587413         5.85664336 
##       FCGR3A+ Mono                  T                 DC         Macrophage 
##         1.83566434         0.26223776         0.87412587         0.08741259 
##                  B 
##         0.08741259

table(colDat_hi$ident)/nrow(colDat_hi)*100

## 
##    NK 1 (FCGRA3A+)         CD14+ Mono       NK 2 (IL7R+) CD14+ Mono (CCL4+) 
##          75.956284           1.366120          13.661202           5.737705 
##       FCGR3A+ Mono                  T                 DC         Macrophage 
##           1.639344           0.000000           1.092896           0.273224 
##                  B 
##           0.273224

message("Focus on NK 1 (FCGRA3A+) now")

## Focus on NK 1 (FCGRA3A+) now

sc_low <- sc[,colData(sce)$KIR3DL2==1 & colData(sce)$ident=="NK 1 (FCGRA3A+)"]
sc_med <- sc[,colData(sce)$KIR3DL2==2 & colData(sce)$ident=="NK 1 (FCGRA3A+)"]
sc_hi <- sc[,colData(sce)$KIR3DL2==3 & colData(sce)$ident=="NK 1 (FCGRA3A+)"]

colDat_hi <- subset(colData(sce),KIR3DL2==3 & ident=="NK 1 (FCGRA3A+)")
sc_hi <- SingleCellExperiment(list(counts=sc_hi), colData=colDat_hi)

colDat_med <- subset(colData(sce),KIR3DL2==2 & ident=="NK 1 (FCGRA3A+)")
sc_med <- SingleCellExperiment(list(counts=sc_med), colData=colDat_med)

colDat_low <- subset(colData(sce),KIR3DL2==1 & ident=="NK 1 (FCGRA3A+)")
sc_low <- SingleCellExperiment(list(counts=sc_low), colData=colDat_low)

#muscat aggregation of NK 1 (FCGRA3A+) cells
pbhi <- aggregateData(sc_hi, assay="counts", fun = "sum", by = c("sample_id"))
pbmed <- aggregateData(sc_med, assay="counts", fun = "sum", by = c("sample_id"))
pblow <- aggregateData(sc_low, assay="counts", fun = "sum", by = c("sample_id"))

colnames(pbhi) <- paste(colnames(pbhi),"hi")
colnames(pbmed) <- paste(colnames(pbmed),"med")
colnames(pblow) <- paste(colnames(pblow),"low")

kx <- cbind(assay(pbhi),assay(pbmed),assay(pblow))

dfhi <- pat_df
dfhi$KIR3DL2 <- 3
dfmed <- pat_df
dfmed$KIR3DL2 <- 2
dflow <- pat_df
dflow$KIR3DL2 <- 1

kdf <- rbind(dfhi,dfmed,dflow)
rownames(kdf) <- colnames(kx)

## focus on HIV negative
kdf1 <- subset(kdf,hiv_status==0 & KIR3DL2!=2)
kx1 <- kx[,which(colnames(kx) %in% rownames(kdf1))]
colSums(kx1)

##  CC0003 hi  PM0020 hi  PM0027 hi  PM0028 hi  PM0032 hi CC0003 low PM0020 low 
##     442207     324598      12808      21053      35073    8850484    5276990 
## PM0027 low PM0028 low PM0032 low 
##     424172    2643361    1440032

plotMDS(kx1)

kx1f <- kx1[which(rowMeans(kx1)>=5),]
kx1f <- kx1f+1
dds <- DESeqDataSetFromMatrix(countData = kx1f , colData = kdf1, design = ~ batch + KIR3DL2)

## converting counts to integer mode

##   the design formula contains one or more numeric variables with integer values,
##   specifying a model with increasing fold change for higher values.
##   did you mean for this to be a factor? if so, first convert
##   this variable to a factor using the factor() function

res <- DESeq(dds)

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

z <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd),kx1f)
kx1de <- as.data.frame(zz[order(zz$pvalue),])

kx1de[1:50,1:6] |> kbl(caption="KIR3DL2 associated genes in HIV- cells") |> kable_paper("hover", full_width = F)

KIR3DL2 associated genes in HIV- cells
	baseMean	log2FoldChange	lfcSE	stat	pvalue	padj
KIR3DL2	52.106641	4.7020539	0.3426424	13.722917	0.0000000	0.0000000
C4orf45	5.850221	1.1112152	0.2736121	4.061279	0.0000488	NA
C20orf96	5.007717	1.0546415	0.2758804	3.822822	0.0001319	NA
AC004083.1	4.882308	1.1073538	0.2899995	3.818468	0.0001343	NA
AC008083.3	8.027470	0.8413549	0.2209043	3.808685	0.0001397	NA
EIF1	819.805875	-0.5729001	0.1508867	-3.796890	0.0001465	0.0692332
SPTLC3	5.425260	1.1176408	0.2947932	3.791270	0.0001499	NA
KLRC1	45.991607	-0.6270414	0.1654733	-3.789382	0.0001510	NA
AC007114.2	4.876752	1.0176296	0.2725145	3.734221	0.0001883	NA
CBLB	267.617835	-0.4797514	0.1297975	-3.696154	0.0002189	0.0692332
TUBG2	4.334305	1.0777302	0.2938131	3.668081	0.0002444	NA
AC069277.1	6.823230	1.0633761	0.2908060	3.656652	0.0002555	NA
CMC1	173.725959	-0.5767568	0.1602494	-3.599120	0.0003193	0.0692332
ETV2	4.584710	1.0131687	0.2845123	3.561071	0.0003693	NA
ADCY4	4.276397	1.0637218	0.2989170	3.558586	0.0003729	NA
ZFP69	5.351868	0.9859325	0.2781532	3.544566	0.0003933	NA
ZBED6CL	4.215899	1.0807571	0.3057829	3.534394	0.0004087	NA
AL136418.1	5.235093	0.9877834	0.2808625	3.516964	0.0004365	NA
OLFM2	4.889633	1.0227135	0.2931494	3.488711	0.0004854	NA
PRKCA-AS1	5.192120	0.9837238	0.2822064	3.485831	0.0004906	NA
SSBP2	19.028518	-0.8406370	0.2430361	-3.458898	0.0005424	NA
HIC1	5.380199	0.9674838	0.2801605	3.453320	0.0005537	NA
LRRFIP2	41.575811	-0.5307578	0.1550092	-3.424040	0.0006170	NA
MAP3K8	208.334170	-0.5161858	0.1509093	-3.420504	0.0006251	0.0692332
HNRNPC	195.069124	-0.5223392	0.1532174	-3.409137	0.0006517	0.0692332
AC007066.2	4.271557	1.0181100	0.2991027	3.403881	0.0006644	NA
AZIN2	4.654714	0.9791401	0.2876622	3.403785	0.0006646	NA
PNRC1	214.430451	-0.4924397	0.1448863	-3.398800	0.0006768	0.0692332
ZNF283	4.460224	0.9622871	0.2868157	3.355071	0.0007934	NA
OCA2	4.423250	1.0753846	0.3231032	3.328301	0.0008738	NA
PIDD1	6.488632	0.8367719	0.2515700	3.326199	0.0008804	NA
RPL10A	516.382035	-0.4308291	0.1297381	-3.320760	0.0008977	0.0692332
YME1L1	81.255256	-0.5217900	0.1579361	-3.303805	0.0009538	0.0692332
FBXL22	4.846118	0.8899058	0.2703309	3.291913	0.0009951	NA
PET100	71.462458	-0.4927422	0.1496916	-3.291716	0.0009958	0.0692332
MCTP2	283.086344	-0.6205048	0.1885512	-3.290909	0.0009986	0.0692332
PODN	4.267074	0.9929022	0.3019045	3.288796	0.0010062	NA
HINT1	145.591336	-0.4852409	0.1478355	-3.282303	0.0010296	0.0692332
S100A10	125.090792	-0.4272536	0.1301768	-3.282102	0.0010304	0.0692332
ACSF2	4.319117	0.9902032	0.3032651	3.265141	0.0010941	NA
PSMD6-AS1	4.234843	1.0591708	0.3254203	3.254778	0.0011348	NA
EEF1A1	1392.010484	-0.4845508	0.1493585	-3.244213	0.0011778	0.0692332
KCTD3	4.642128	1.0073970	0.3118112	3.230791	0.0012345	NA
RSL1D1	28.281129	-0.5555478	0.1720146	-3.229655	0.0012394	NA
RPS10	269.903342	-0.4826707	0.1498178	-3.221719	0.0012742	0.0692332
RPS25	607.763430	-0.5028058	0.1571536	-3.199454	0.0013769	0.0692332
KLRB1	324.068178	-0.5903132	0.1846262	-3.197343	0.0013870	0.0692332
SNHG29	83.136073	-0.4475459	0.1403459	-3.188877	0.0014283	0.0692332
RAB3A	4.529164	1.0857122	0.3405551	3.188066	0.0014323	NA
AC109454.2	5.305082	0.9331262	0.2928082	3.186818	0.0014385	NA

write.table(x=kx1de,file="KIR3DL2_de_hivneg.tsv",sep="\t",quote=FALSE)

## select HIV positive
kdf2 <- subset(kdf,hiv_status==1 & KIR3DL2!=2)
kx2 <- kx[,which(colnames(kx) %in% rownames(kdf2))]
colSums(kx2)

##  AH0005 hi  AH0015 hi  AH0018 hi  CC0016 hi   PM001 hi   PM008 hi   PM017 hi 
##     217038     297540      39159     250893      61946     147293     265861 
## AH0005 low AH0015 low AH0018 low CC0016 low  PM001 low  PM008 low  PM017 low 
##    7345282    5384408    1506339    5759154    3078125    6244346   10425498

plotMDS(kx2)

kx2f <- kx2[which(rowMeans(kx2)>=5),]
kx2f <- kx2f+1
dds <- DESeqDataSetFromMatrix(countData = kx2f , colData = kdf2, design = ~ batch + KIR3DL2)

## converting counts to integer mode

##   the design formula contains one or more numeric variables with integer values,
##   specifying a model with increasing fold change for higher values.
##   did you mean for this to be a factor? if so, first convert
##   this variable to a factor using the factor() function

res <- DESeq(dds)

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

z <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd),kx2f)
kx2de <- as.data.frame(zz[order(zz$pvalue),])

kx2de[1:50,1:6] |> kbl(caption="KIR3DL2 associated genes in HIV+ cells") |> kable_paper("hover", full_width = F)

KIR3DL2 associated genes in HIV+ cells
	baseMean	log2FoldChange	lfcSE	stat	pvalue	padj
KIR3DL2	111.251600	5.1688446	0.2779886	18.593730	0.0e+00	0.0000000
ANXA1	330.695628	-0.3571074	0.0564729	-6.323517	0.0e+00	0.0000017
RPL14	1339.849913	-0.3246228	0.0530914	-6.114419	0.0e+00	0.0000042
RPS13	1055.770716	-0.2646047	0.0443627	-5.964575	0.0e+00	0.0000079
MTRNR2L12	745.895516	-0.2572095	0.0449080	-5.727476	0.0e+00	0.0000264
RPS9	962.893860	-0.2508178	0.0441028	-5.687120	0.0e+00	0.0000278
ZFP36L2	268.298087	-0.3216007	0.0568442	-5.657584	0.0e+00	0.0000283
MYL6	674.762201	-0.2478012	0.0449192	-5.516598	0.0e+00	0.0000558
ENSA	119.471549	-0.3650545	0.0670936	-5.440969	1.0e-07	0.0000761
KLRF1	388.167538	-0.3601608	0.0670454	-5.371895	1.0e-07	0.0001007
ZNF793	7.513469	1.0932337	0.2064566	5.295224	1.0e-07	0.0001359
PFDN5	480.414165	-0.2765681	0.0523375	-5.284320	1.0e-07	0.0001359
RPS4Y1	336.046167	-0.2997723	0.0571280	-5.247380	2.0e-07	0.0001534
RPL36AL	537.139944	-0.2791345	0.0534097	-5.226288	2.0e-07	0.0001597
SERF2	753.238525	-0.2129074	0.0409663	-5.197137	2.0e-07	0.0001744
COX7C	324.658202	-0.2213873	0.0428444	-5.167239	2.0e-07	0.0001919
SRSF5	434.366284	-0.2174878	0.0423934	-5.130233	3.0e-07	0.0002026
SLCO3A1	138.342138	-0.3364410	0.0656454	-5.125130	3.0e-07	0.0002026
ARPC3	487.775569	-0.1969847	0.0384371	-5.124859	3.0e-07	0.0002026
MT-ATP6	7923.155688	-0.2780958	0.0548326	-5.071727	4.0e-07	0.0002548
RPS21	1432.921696	-0.2880248	0.0573086	-5.025855	5.0e-07	0.0003085
FAU	1403.069146	-0.2961102	0.0590991	-5.010398	5.0e-07	0.0003191
EEF1B2	570.653008	-0.3179521	0.0637808	-4.985079	6.0e-07	0.0003481
CRIP1	428.229053	-0.2426964	0.0489161	-4.961481	7.0e-07	0.0003768
CYBA	1019.415429	-0.2671190	0.0539279	-4.953263	7.0e-07	0.0003772
SPRED2	9.427582	1.1186816	0.2262969	4.943425	8.0e-07	0.0003772
MT-ND3	4330.574779	-0.2498882	0.0506015	-4.938355	8.0e-07	0.0003772
RPL31	482.551773	-0.2504642	0.0510465	-4.906587	9.0e-07	0.0004080
MYL12B	404.083840	-0.2112882	0.0430969	-4.902633	9.0e-07	0.0004080
DDX5	1159.083946	-0.2370850	0.0483613	-4.902367	9.0e-07	0.0004080
RPL41	3845.669904	-0.2905363	0.0597830	-4.859844	1.2e-06	0.0004797
MT-ND5	1165.181446	-0.2529116	0.0520705	-4.857104	1.2e-06	0.0004797
ATP5F1E	825.740823	-0.2150538	0.0443261	-4.851623	1.2e-06	0.0004797
RRAS2	110.081277	-0.3598003	0.0742934	-4.842962	1.3e-06	0.0004863
RPL37A	1461.770259	-0.2428534	0.0508757	-4.773464	1.8e-06	0.0006688
RPS15	1564.271198	-0.2413148	0.0506966	-4.759985	1.9e-06	0.0006879
RPL10	3746.676756	-0.2587117	0.0543905	-4.756560	2.0e-06	0.0006879
RPS27A	2898.049396	-0.2796611	0.0589381	-4.744995	2.1e-06	0.0007093
MT-CO2	7043.701193	-0.2552830	0.0539275	-4.733815	2.2e-06	0.0007303
RPL23A	1668.021337	-0.2987048	0.0631943	-4.726765	2.3e-06	0.0007372
ZFP41	6.321451	1.0558292	0.2246751	4.699361	2.6e-06	0.0008228
RPL27A	1387.033833	-0.2886663	0.0618622	-4.666276	3.1e-06	0.0009439
GTF3A	171.201269	-0.2903334	0.0623229	-4.658534	3.2e-06	0.0009573
RPS8	1562.798207	-0.2468416	0.0534194	-4.620827	3.8e-06	0.0011228
ATP5ME	200.767163	-0.2510822	0.0545132	-4.605895	4.1e-06	0.0011734
LRTOMT	6.152779	1.0284531	0.2235787	4.599959	4.2e-06	0.0011734
CHCHD2	346.353623	-0.2153823	0.0468800	-4.594337	4.3e-06	0.0011734
SRSF11	245.848464	-0.2710483	0.0590062	-4.593558	4.4e-06	0.0011734
NDUFB7	168.463219	-0.2577164	0.0563309	-4.575044	4.8e-06	0.0012449
NSMCE2	138.313155	-0.3204694	0.0700837	-4.572666	4.8e-06	0.0012449

write.table(x=kx2de,file="KIR3DL2_de_hivpos.tsv",sep="\t",quote=FALSE)

Now make a model that incorporates HIV+ and HIV- cells.

kdf

##            patient_id hiv_status batch KIR3DL2
## AH0005 hi     AH0005+          1     2       3
## AH0015 hi     AH0015+          1     2       3
## AH0018 hi     AH0018+          1     1       3
## CC0003 hi     CC0003-          0     1       3
## CC0016 hi     CC0016+          1     3       3
## PM001 hi       PM001+          1     3       3
## PM0020 hi     PM0020-          0     3       3
## PM0027 hi     PM0027-          0     3       3
## PM0028 hi     PM0028-          0     2       3
## PM0032 hi     PM0032-          0     2       3
## PM008 hi       PM008+          1     1       3
## PM017 hi       PM017+          1     1       3
## AH0005 med    AH0005+          1     2       2
## AH0015 med    AH0015+          1     2       2
## AH0018 med    AH0018+          1     1       2
## CC0003 med    CC0003-          0     1       2
## CC0016 med    CC0016+          1     3       2
## PM001 med      PM001+          1     3       2
## PM0020 med    PM0020-          0     3       2
## PM0027 med    PM0027-          0     3       2
## PM0028 med    PM0028-          0     2       2
## PM0032 med    PM0032-          0     2       2
## PM008 med      PM008+          1     1       2
## PM017 med      PM017+          1     1       2
## AH0005 low    AH0005+          1     2       1
## AH0015 low    AH0015+          1     2       1
## AH0018 low    AH0018+          1     1       1
## CC0003 low    CC0003-          0     1       1
## CC0016 low    CC0016+          1     3       1
## PM001 low      PM001+          1     3       1
## PM0020 low    PM0020-          0     3       1
## PM0027 low    PM0027-          0     3       1
## PM0028 low    PM0028-          0     2       1
## PM0032 low    PM0032-          0     2       1
## PM008 low      PM008+          1     1       1
## PM017 low      PM017+          1     1       1

head(kx,2)

##             AH0005 hi AH0015 hi AH0018 hi CC0003 hi CC0016 hi PM001 hi
## gene-HIV1-1         0         0         0         0         0        0
## gene-HIV1-2         0         0         0         0         0        0
##             PM0020 hi PM0027 hi PM0028 hi PM0032 hi PM008 hi PM017 hi
## gene-HIV1-1         0         0         0         0        0        0
## gene-HIV1-2         0         0         0         0        0        0
##             AH0005 med AH0015 med AH0018 med CC0003 med CC0016 med PM001 med
## gene-HIV1-1          0          0          0          0          0         0
## gene-HIV1-2          0          0          0          0          0         0
##             PM0020 med PM0027 med PM0028 med PM0032 med PM008 med PM017 med
## gene-HIV1-1          0          0          0          0         0         0
## gene-HIV1-2          0          0          0          0         0         0
##             AH0005 low AH0015 low AH0018 low CC0003 low CC0016 low PM001 low
## gene-HIV1-1          0          0          0          0          0         0
## gene-HIV1-2          0          0          0          0          0         0
##             PM0020 low PM0027 low PM0028 low PM0032 low PM008 low PM017 low
## gene-HIV1-1          0          0          0          0         0         0
## gene-HIV1-2          0          0          0          0         0         0

kdf3 <- subset(kdf, KIR3DL2!=2)

kx3 <- kx[,which(colnames(kx) %in% rownames(kdf3))]

colSums(kx3)

##  AH0005 hi  AH0015 hi  AH0018 hi  CC0003 hi  CC0016 hi   PM001 hi  PM0020 hi 
##     217038     297540      39159     442207     250893      61946     324598 
##  PM0027 hi  PM0028 hi  PM0032 hi   PM008 hi   PM017 hi AH0005 low AH0015 low 
##      12808      21053      35073     147293     265861    7345282    5384408 
## AH0018 low CC0003 low CC0016 low  PM001 low PM0020 low PM0027 low PM0028 low 
##    1506339    8850484    5759154    3078125    5276990     424172    2643361 
## PM0032 low  PM008 low  PM017 low 
##    1440032    6244346   10425498

plotMDS(kx3)

dim(kx3)

## [1] 36603    24

kx3f <- kx3[which(rowMeans(kx3)>=5),]
dim(kx3f)

## [1] 12609    24

# hiv-
dds <- DESeqDataSetFromMatrix(countData = kx3f , colData = kdf3, design = ~ batch + hiv_status + KIR3DL2)

## converting counts to integer mode

##   the design formula contains one or more numeric variables with integer values,
##   specifying a model with increasing fold change for higher values.
##   did you mean for this to be a factor? if so, first convert
##   this variable to a factor using the factor() function

res <- DESeq(dds)

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

z <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd),kx3f)
kx3de <- as.data.frame(zz[order(zz$pvalue),])

kx3de[1:50,1:6] |> kbl(caption="KIR3DL2 associated genes irrespective of HIV status") |> kable_paper("hover", full_width = F)

KIR3DL2 associated genes irrespective of HIV status
	baseMean	log2FoldChange	lfcSE	stat	pvalue	padj
KIR3DL2	97.307953	6.2748928	0.2988685	20.995497	0.0000000	0.0000000
UHMK1	77.213912	0.1978831	0.0478042	4.139452	0.0000348	0.2193953
KCMF1	77.698935	0.1996632	0.0511112	3.906447	0.0000937	0.3089647
HELLPAR	4.372628	0.8168230	0.2122167	3.849004	0.0001186	0.3089647
BTBD7	54.771378	0.2118470	0.0551901	3.838496	0.0001238	0.3089647
KIR2DL3	23.258110	0.3651384	0.0961916	3.795950	0.0001471	0.3089647
ADCK5	3.800859	0.7419097	0.2045018	3.627889	0.0002857	0.4531163
EOMES	15.669980	-0.5172341	0.1426373	-3.626219	0.0002876	0.4531163
ZNF793	3.251561	0.8012653	0.2242374	3.573291	0.0003525	0.4936892
ADCY4	1.870875	0.7780127	0.2203989	3.530021	0.0004155	0.5229981
AC008083.3	8.731393	0.5899135	0.1683016	3.505097	0.0004564	0.5229981
MRPS14	11.815139	-0.6484091	0.1881568	-3.446110	0.0005687	0.5569805
AC022217.3	24.324045	-0.3846740	0.1117139	-3.443385	0.0005745	0.5569805
AXIN1	50.039516	0.2363560	0.0699812	3.377423	0.0007317	0.6250211
GEN1	11.015877	0.5225860	0.1555977	3.358572	0.0007835	0.6250211
ATG5	92.124772	0.1751891	0.0525373	3.334566	0.0008543	0.6250211
DPP7	37.774690	-0.2835242	0.0852512	-3.325749	0.0008818	0.6250211
URM1	30.680793	0.2403529	0.0726472	3.308498	0.0009380	0.6250211
YJU2	26.078674	0.3342589	0.1013028	3.299603	0.0009682	0.6250211
KLRC1	59.392693	-0.7218007	0.2192026	-3.292848	0.0009918	0.6250211
AC044849.1	23.916146	-0.4524116	0.1396809	-3.238892	0.0011999	0.6580211
NUMA1	33.327456	0.2231371	0.0689952	3.234096	0.0012203	0.6580211
ZNF283	1.889343	0.6838368	0.2129058	3.211922	0.0013185	0.6580211
XPO7	45.150202	0.2133917	0.0665351	3.207205	0.0013403	0.6580211
URB1	4.595532	0.4993579	0.1558063	3.204992	0.0013507	0.6580211
RNF8	14.627144	0.3180501	0.0992802	3.203562	0.0013574	0.6580211
COQ3	4.460614	0.6323281	0.1993022	3.172710	0.0015102	0.6797251
TET2	112.232618	0.1690499	0.0534299	3.163957	0.0015564	0.6797251
BAALC-AS1	1.761370	0.8345782	0.2638943	3.162548	0.0015640	0.6797251
RABGAP1	60.820168	0.2111305	0.0670064	3.150899	0.0016277	0.6838453
PITPNC1	449.720396	0.1304240	0.0415538	3.138680	0.0016971	0.6899897
COPB1	56.570094	-0.2003537	0.0640236	-3.129373	0.0017518	0.6899897
RPL14	990.545038	-0.1037334	0.0335122	-3.095392	0.0019655	0.7331588
RPL35A	937.727296	-0.0914427	0.0295591	-3.093555	0.0019777	0.7331588
SPRED2	4.717274	0.7670289	0.2487434	3.083615	0.0020450	0.7364403
FTL	835.063531	-0.0964821	0.0314225	-3.070478	0.0021372	0.7482456
GPATCH8	199.228473	0.1158290	0.0378410	3.060944	0.0022064	0.7516080
ST8SIA1	3.136623	0.7890891	0.2589560	3.047194	0.0023099	0.7618723
SLC39A14	2.195216	0.7182748	0.2361919	3.041065	0.0023574	0.7618723
ZEB1	26.727442	-0.4058789	0.1345021	-3.017640	0.0025475	0.7620230
AC007114.2	1.996371	0.6376296	0.2122141	3.004653	0.0026588	0.7620230
THAP12	34.928360	-0.2336252	0.0778968	-2.999165	0.0027072	0.7620230
NPC2	26.371800	-0.2969408	0.0993097	-2.990047	0.0027893	0.7620230
SPIN1	35.915940	0.2150037	0.0719163	2.989638	0.0027931	0.7620230
OSBP2	4.392749	0.5598884	0.1872821	2.989545	0.0027939	0.7620230
ZRANB2-AS2	3.167680	0.7118759	0.2381313	2.989426	0.0027950	0.7620230
DNMBP-AS1	6.343188	0.6501072	0.2189099	2.969749	0.0029804	0.7620230
RPL23A	1202.442646	-0.1016582	0.0342699	-2.966402	0.0030131	0.7620230
BRD4	70.444549	0.1489815	0.0502637	2.963998	0.0030367	0.7620230
HIST1H3A	3.477017	0.5177001	0.1750148	2.958036	0.0030961	0.7620230

Pathway analysis.

k1 <- mitch_import(kx1de,DEtype="deseq2")

## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.

## Note: Mean no. genes in input = 12037

## Note: no. genes in output = 12037

## Note: estimated proportion of input genes in output = 1

k1m <- mitch_calc(k1,genesets=go,minsetsize=5,cores=16,priority="effect")

## Note: Enrichments with large effect sizes may not be
##             statistically significant.

head(subset(k1m$enrichment_result, s.dist > 0),20) |> kbl(caption="top enrichments (positive correl)") |> kable_paper("hover", full_width = F)

top enrichments (positive correl)
	set	setSize	pANOVA	s.dist	p.adjustANOVA
2613	GOBP NEURON CELL CELL ADHESION	5	0.0021734	0.7915891	0.0125380
7314	GOMF NEUROPEPTIDE RECEPTOR ACTIVITY	5	0.0065832	0.7016955	0.0302007
1979	GOBP MUCOCILIARY CLEARANCE	5	0.0076384	0.6888963	0.0341866
374	GOBP CARNITINE TRANSPORT	5	0.0084152	0.6804521	0.0368477
6303	GOCC OUTER DYNEIN ARM	5	0.0094338	0.6703790	0.0403661
698	GOBP COMPLEMENT RECEPTOR MEDIATED SIGNALING PATHWAY	5	0.0100335	0.6648936	0.0424712
7119	GOMF INTRACELLULAR CHLORIDE CHANNEL ACTIVITY	6	0.0071402	0.6342227	0.0323050
1505	GOBP INTRACELLULAR PH ELEVATION	5	0.0146879	0.6301197	0.0574262
7088	GOMF HYDROLASE ACTIVITY ACTING ON CARBON NITROGEN BUT NOT PEPTIDE BONDS IN CYCLIC AMIDES	6	0.0093206	0.6129720	0.0399250
1254	GOBP GLYCINE METABOLIC PROCESS	8	0.0029074	0.6079059	0.0158667
6740	GOMF BICARBONATE MONOATOMIC ANION ANTIPORTER ACTIVITY	6	0.0121207	0.5914166	0.0491687
6876	GOMF C METHYLTRANSFERASE ACTIVITY	7	0.0074369	0.5842299	0.0334173
3889	GOBP PYRIDINE CONTAINING COMPOUND CATABOLIC PROCESS	6	0.0164375	0.5655944	0.0622604
5153	GOBP RETINAL ROD CELL DEVELOPMENT	5	0.0285290	0.5655585	0.0952496
839	GOBP DIACYLGLYCEROL BIOSYNTHETIC PROCESS	5	0.0292560	0.5629987	0.0971421
5786	GOCC AMPA GLUTAMATE RECEPTOR COMPLEX	6	0.0172185	0.5615770	0.0645672
6579	GOCC UBIQUINONE BIOSYNTHESIS COMPLEX	6	0.0186533	0.5545951	0.0682600
2837	GOBP PEPTIDYL LYSINE HYDROXYLATION	6	0.0192148	0.5519907	0.0699251
7663	GOMF SCAVENGER RECEPTOR ACTIVITY	5	0.0338525	0.5479721	0.1081690
2805	GOBP OXALATE TRANSPORT	5	0.0343751	0.5463763	0.1093514

head(subset(k1m$enrichment_result, s.dist < 0),20) |> kbl(caption="top enrichments (negative correl)") |> kable_paper("hover", full_width = F)

top enrichments (negative correl)
	set	setSize	pANOVA	s.dist	p.adjustANOVA
5972	GOCC CYTOSOLIC SMALL RIBOSOMAL SUBUNIT	38	0.0000000	-0.9425303	0.0000000
3247	GOBP POSITIVE REGULATION OF INTRINSIC APOPTOTIC SIGNALING PATHWAY BY P53 CLASS MEDIATOR	5	0.0002763	-0.9389960	0.0022719
5969	GOCC CYTOSOLIC LARGE RIBOSOMAL SUBUNIT	55	0.0000000	-0.9031305	0.0000000
309	GOBP CALCIUM ION EXPORT ACROSS PLASMA MEMBRANE	5	0.0006623	-0.8791556	0.0047657
6356	GOCC POLYSOMAL RIBOSOME	29	0.0000000	-0.8651646	0.0000000
3401	GOBP POSITIVE REGULATION OF OXIDATIVE STRESS INDUCED INTRINSIC APOPTOTIC SIGNALING PATHWAY	5	0.0008291	-0.8631981	0.0057575
5799	GOCC ARP2 3 PROTEIN COMPLEX	9	0.0000138	-0.8365850	0.0001680
6654	GOMF ADENYLATE CYCLASE REGULATOR ACTIVITY	5	0.0012905	-0.8309840	0.0082540
5971	GOCC CYTOSOLIC RIBOSOME	109	0.0000000	-0.8234268	0.0000000
1656	GOBP LYMPHOCYTE ANERGY	5	0.0016555	-0.8123670	0.0101281
7777	GOMF TPR DOMAIN BINDING	6	0.0005712	-0.8120688	0.0041978
1857	GOBP MITOCHONDRIAL ELECTRON TRANSPORT CYTOCHROME C TO OXYGEN	18	0.0000000	-0.8102172	0.0000001
5945	GOCC CRD MEDIATED MRNA STABILITY COMPLEX	5	0.0017140	-0.8097407	0.0103940
2239	GOBP NEGATIVE REGULATION OF FIBROBLAST MIGRATION	5	0.0017589	-0.8077793	0.0105928
6169	GOCC MCRD MEDIATED MRNA STABILITY COMPLEX	5	0.0019447	-0.8001330	0.0115219
6181	GOCC MHC CLASS I PROTEIN COMPLEX	7	0.0002699	-0.7949650	0.0022314
593	GOBP CELL SUBSTRATE JUNCTION DISASSEMBLY	5	0.0021042	-0.7940824	0.0122024
4264	GOBP REGULATION OF FOCAL ADHESION DISASSEMBLY	5	0.0021042	-0.7940824	0.0122024
4528	GOBP REGULATION OF NATURAL KILLER CELL CHEMOTAXIS	5	0.0022943	-0.7874003	0.0130824
6540	GOCC TRANSCRIPTION FACTOR AP 1 COMPLEX	5	0.0026196	-0.7770612	0.0145891

up <- head(subset(k1m$enrichment_result, s.dist > 0 & p.adjustANOVA < 0.05),20)
dn <- head(subset(k1m$enrichment_result, s.dist < 0 & p.adjustANOVA < 0.05),20)
top <- rbind(up,dn)
top <- top[order(top$s.dist),]

par(mar=c(5.1, 25.1, 4.1, 2.1))
gsn <- substr(top$set, start = 1, stop = 70 )
barplot(top$s.dist,horiz=TRUE,las=1,xlab="ES",names.arg=gsn,cex.names=0.6,main="HIV neg")

mitch_plots(k1m,outfile="KIR3DL2_assoc_hivneg.pdf")

## png 
##   2

if ( ! file.exists("KIR3DL2_assoc_hivneg.html") ) {
  mitch_report(res=k1m,outfile="KIR3DL2_assoc_hivneg.html")
}

# pos
k2 <- mitch_import(kx2de,DEtype="deseq2")

## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.

## Note: Mean no. genes in input = 12931

## Note: no. genes in output = 12931

## Note: estimated proportion of input genes in output = 1

k2m <- mitch_calc(k2,genesets=go,minsetsize=5,cores=16,priority="effect")

## Note: Enrichments with large effect sizes may not be
##             statistically significant.

head(subset(k2m$enrichment_result, s.dist > 0),20) |> kbl(caption="top enrichments (positive correl)")  |> kable_paper("hover", full_width = F)

top enrichments (positive correl)
	set	setSize	pANOVA	s.dist	p.adjustANOVA
2670	GOBP NEURON CELL CELL ADHESION	5	0.0043995	0.7354479	0.0242289
637	GOBP CEREBROSPINAL FLUID CIRCULATION	5	0.0050641	0.7238125	0.0272738
441	GOBP CELLULAR RESPONSE TO CAFFEINE	7	0.0020426	0.6730999	0.0128297
127	GOBP AMYLOID BETA CLEARANCE BY CELLULAR CATABOLIC PROCESS	5	0.0096870	0.6680179	0.0455827
514	GOBP CELLULAR RESPONSE TO PURINE CONTAINING COMPOUND	8	0.0015824	0.6449934	0.0104075
3020	GOBP POLYOL TRANSMEMBRANE TRANSPORT	5	0.0130893	0.6407860	0.0577868
828	GOBP DETECTION OF MOLECULE OF BACTERIAL ORIGIN	6	0.0068814	0.6371115	0.0346090
1056	GOBP ERBB4 SIGNALING PATHWAY	6	0.0074205	0.6311799	0.0369054
1153	GOBP FATTY ACYL COA CATABOLIC PROCESS	6	0.0078378	0.6268472	0.0384355
2988	GOBP PIRNA PROCESSING	5	0.0161310	0.6213214	0.0673405
2021	GOBP MUCOCILIARY CLEARANCE	5	0.0166897	0.6181030	0.0691356
2880	GOBP PARTURITION	5	0.0167662	0.6176698	0.0693455
7679	GOMF POSTSYNAPTIC NEUROTRANSMITTER RECEPTOR ACTIVITY	9	0.0013424	0.6172935	0.0090428
648	GOBP CHEMOSENSORY BEHAVIOR	6	0.0089316	0.6164023	0.0428092
7154	GOMF GLYCERALDEHYDE 3 PHOSPHATE DEHYDROGENASE NADPLUS NON PHOSPHORYLATING ACTIVITY	5	0.0203407	0.5991335	0.0799407
7827	GOMF SCAVENGER RECEPTOR ACTIVITY	8	0.0056341	0.5652325	0.0297082
8050	GOMF VOLTAGE GATED SODIUM CHANNEL ACTIVITY	5	0.0302019	0.5597401	0.1065865
1200	GOBP FRUCTOSE METABOLIC PROCESS	10	0.0022610	0.5576813	0.0140255
7600	GOMF PHOSPHATE ION BINDING	6	0.0194246	0.5510251	0.0771717
6798	GOMF ADP D RIBOSE MODIFICATION DEPENDENT PROTEIN BINDING	5	0.0335761	0.5488163	0.1147367

head(subset(k2m$enrichment_result, s.dist < 0),20) |> kbl(caption="top enrichments (negative correl)") |> kable_paper("hover", full_width = F)

top enrichments (negative correl)
	set	setSize	pANOVA	s.dist	p.adjustANOVA
6099	GOCC CYTOSOLIC SMALL RIBOSOMAL SUBUNIT	38	0.0000000	-0.9505525	0.0000000
6337	GOCC MITOCHONDRIAL PROTON TRANSPORTING ATP SYNTHASE COMPLEX COUPLING FACTOR F O	10	0.0000002	-0.9438279	0.0000042
6539	GOCC PROTON TRANSPORTING ATP SYNTHASE COMPLEX CATALYTIC CORE F 1	6	0.0000993	-0.9174726	0.0009754
6096	GOCC CYTOSOLIC LARGE RIBOSOMAL SUBUNIT	55	0.0000000	-0.9150527	0.0000000
6538	GOCC PROTON TRANSPORTING ATP SYNTHASE COMPLEX	18	0.0000000	-0.9053839	0.0000000
6540	GOCC PROTON TRANSPORTING ATP SYNTHASE COMPLEX COUPLING FACTOR F O	13	0.0000000	-0.9010921	0.0000004
6490	GOCC POLYSOMAL RIBOSOME	29	0.0000000	-0.8931574	0.0000000
7924	GOMF TAP BINDING	7	0.0000509	-0.8841358	0.0005440
8015	GOMF UBIQUITIN LIGASE INHIBITOR ACTIVITY	8	0.0000173	-0.8772150	0.0002134
5926	GOCC ARP2 3 PROTEIN COMPLEX	9	0.0000056	-0.8738241	0.0000776
7761	GOMF PROTON TRANSPORTING ATP SYNTHASE ACTIVITY ROTATIONAL MECHANISM	16	0.0000000	-0.8699187	0.0000000
3325	GOBP POSITIVE REGULATION OF INTRINSIC APOPTOTIC SIGNALING PATHWAY BY P53 CLASS MEDIATOR	5	0.0009716	-0.8517716	0.0069581
6098	GOCC CYTOSOLIC RIBOSOME	109	0.0000000	-0.8320275	0.0000000
1901	GOBP MITOCHONDRIAL ELECTRON TRANSPORT UBIQUINOL TO CYTOCHROME C	12	0.0000006	-0.8308048	0.0000103
6470	GOCC PICLN SM PROTEIN COMPLEX	6	0.0004743	-0.8238298	0.0038211
6610	GOCC SIGNAL PEPTIDASE COMPLEX	5	0.0014482	-0.8224045	0.0096514
6792	GOMF ADENYLATE CYCLASE REGULATOR ACTIVITY	5	0.0014810	-0.8207334	0.0098127
6423	GOCC NURF COMPLEX	6	0.0006112	-0.8077369	0.0047215
5599	GOBP TELOMERASE HOLOENZYME COMPLEX ASSEMBLY	6	0.0006267	-0.8061380	0.0048098
8009	GOMF UBIQUINOL CYTOCHROME C REDUCTASE ACTIVITY	6	0.0006277	-0.8060348	0.0048098

up <- head(subset(k2m$enrichment_result, s.dist > 0 & p.adjustANOVA < 0.05),20)
dn <- head(subset(k2m$enrichment_result, s.dist < 0 & p.adjustANOVA < 0.05),20)
top <- rbind(up,dn)
top <- top[order(top$s.dist),]

par(mar=c(5.1, 25.1, 4.1, 2.1))
gsn <- substr(top$set, start = 1, stop = 70 )
barplot(top$s.dist,horiz=TRUE,las=1,xlab="ES",names.arg=gsn,cex.names=0.6,main="HIV pos")

mitch_plots(k2m,outfile="KIR3DL2_assoc_hivpos.pdf")

## png 
##   2

if ( ! file.exists("KIR3DL2_assoc_hivpos.html") ) {
  mitch_report(res=k2m,outfile="KIR3DL2_assoc_hivpos.html")
}

par(mar=c(5.1, 4.1, 4.1, 2.1))

Multi-contrast enrichment

km <- merge(k1,k2,by=0)
rownames(km) <- km[,1]
km[,1]=NULL
colnames(km) <- c("neg","pos")
dim(km)

## [1] 11958     2

kmm <- mitch_calc(km,genesets=go,minsetsize=5,cores=16,priority="effect")

## Note: Enrichments with large effect sizes may not be 
##             statistically significant.

head(subset(kmm$enrichment_result, s.dist > 0),20) |> kbl(caption="top multi-enrichment") |> kable_paper("hover", full_width = F)

top multi-enrichment
	set	setSize	pMANOVA	s.neg	s.pos	p.neg	p.pos	s.dist	SD	p.adjustMANOVA
5960	GOCC CYTOSOLIC SMALL RIBOSOMAL SUBUNIT	38	0.0000000	-0.9421494	-0.9465162	0.0000000	0.0000000	1.335492	0.0030878	0.0000000
5957	GOCC CYTOSOLIC LARGE RIBOSOMAL SUBUNIT	55	0.0000000	-0.9024875	-0.9081271	0.0000000	0.0000000	1.280304	0.0039877	0.0000000
3238	GOBP POSITIVE REGULATION OF INTRINSIC APOPTOTIC SIGNALING PATHWAY BY P53 CLASS MEDIATOR	5	0.0008915	-0.9385928	-0.8397055	0.0002780	0.0011465	1.259389	0.0699239	0.0067230
6344	GOCC POLYSOMAL RIBOSOME	29	0.0000000	-0.8642717	-0.8844427	0.0000000	0.0000000	1.236610	0.0142631	0.0000000
5788	GOCC ARP2 3 PROTEIN COMPLEX	9	0.0000119	-0.8355046	-0.8635497	0.0000142	0.0000072	1.201577	0.0198309	0.0001543
6195	GOCC MITOCHONDRIAL PROTON TRANSPORTING ATP SYNTHASE COMPLEX COUPLING FACTOR F O	10	0.0000015	-0.7472548	-0.9392534	0.0000427	0.0000003	1.200244	0.1357636	0.0000244
5959	GOCC CYTOSOLIC RIBOSOME	109	0.0000000	-0.8222805	-0.8183844	0.0000000	0.0000000	1.160129	0.0027550	0.0000000
6641	GOMF ADENYLATE CYCLASE REGULATOR ACTIVITY	5	0.0029100	-0.8298670	-0.8061407	0.0013102	0.0017973	1.156954	0.0167770	0.0180824
6169	GOCC MHC CLASS I PROTEIN COMPLEX	7	0.0002927	-0.7936096	-0.8361165	0.0002765	0.0001275	1.152782	0.0300569	0.0026125
7833	GOMF UBIQUITIN LIGASE INHIBITOR ACTIVITY	8	0.0000790	-0.7549791	-0.8672176	0.0002173	0.0000215	1.149809	0.0793646	0.0008399
3392	GOBP POSITIVE REGULATION OF OXIDATIVE STRESS INDUCED INTRINSIC APOPTOTIC SIGNALING PATHWAY	5	0.0029154	-0.8622940	-0.7510583	0.0008397	0.0036324	1.143521	0.0786555	0.0180877
7743	GOMF TAP BINDING	7	0.0002695	-0.7227728	-0.8747027	0.0009276	0.0000612	1.134683	0.1074307	0.0024475
6394	GOCC PROTON TRANSPORTING ATP SYNTHASE COMPLEX COUPLING FACTOR F O	13	0.0000002	-0.6990566	-0.8930354	0.0000127	0.0000000	1.134104	0.1371637	0.0000030
7584	GOMF PROTON TRANSPORTING ATP SYNTHASE ACTIVITY ROTATIONAL MECHANISM	16	0.0000000	-0.7004166	-0.8593200	0.0000012	0.0000000	1.108609	0.1123617	0.0000003
6392	GOCC PROTON TRANSPORTING ATP SYNTHASE COMPLEX	18	0.0000000	-0.6499814	-0.8976736	0.0000018	0.0000000	1.108284	0.1751448	0.0000000
6464	GOCC SIGNAL PEPTIDASE COMPLEX	5	0.0047404	-0.7534008	-0.8079478	0.0035283	0.0017551	1.104714	0.0385705	0.0268339
1853	GOBP MITOCHONDRIAL ELECTRON TRANSPORT CYTOCHROME C TO OXYGEN	18	0.0000000	-0.8089708	-0.7324028	0.0000000	0.0000001	1.091260	0.0541418	0.0000002
2235	GOBP NEGATIVE REGULATION OF FIBROBLAST MIGRATION	5	0.0053152	-0.8065088	-0.7331214	0.0017886	0.0045262	1.089919	0.0518928	0.0293249
6393	GOCC PROTON TRANSPORTING ATP SYNTHASE COMPLEX CATALYTIC CORE F 1	6	0.0005209	-0.5848951	-0.9107541	0.0131037	0.0001116	1.082393	0.2304171	0.0042945
309	GOBP CALCIUM ION EXPORT ACROSS PLASMA MEMBRANE	5	0.0030725	-0.8783569	-0.6319920	0.0006699	0.0143958	1.082093	0.1742063	0.0188833

mitch_plots(kmm,outfile="KIR3DL2_assoc_hivnegpos.pdf")

## png 
##   2

if ( ! file.exists("KIR3DL2_assoc_hivnegpos.html") ) {
  mitch_report(res=kmm,outfile="KIR3DL2_assoc_hivnegpos.html")
}

Pathway analysis irrespective of HIV status.

k3 <- mitch_import(kx3de,DEtype="deseq2")

## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.

## Note: Mean no. genes in input = 12609

## Note: no. genes in output = 12609

## Note: estimated proportion of input genes in output = 1

k3m <- mitch_calc(k3,genesets=go,minsetsize=5,cores=16,priority="effect")

## Note: Enrichments with large effect sizes may not be
##             statistically significant.

head(subset(k3m$enrichment_result, s.dist > 0),20) |> kbl(caption="top enrichments (positive correl)") |> kable_paper("hover", full_width = F)

top enrichments (positive correl)
	set	setSize	pANOVA	s.dist	p.adjustANOVA
5988	GOCC CIS GOLGI NETWORK MEMBRANE	7	0.0001954	0.8129095	0.0211285
2645	GOBP NEURON CELL CELL ADHESION	5	0.0043860	0.7357030	0.2013826
6693	GOMF 1 ACYLGLYCEROPHOSPHOCHOLINE O ACYLTRANSFERASE ACTIVITY	5	0.0051584	0.7222786	0.2230638
3878	GOBP PROTEIN RETENTION IN GOLGI APPARATUS	5	0.0053317	0.7195176	0.2268801
5492	GOBP STRESS INDUCED PREMATURE SENESCENCE	5	0.0054152	0.7182164	0.2292138
5326	GOBP SECRETORY GRANULE LOCALIZATION	7	0.0014294	0.6959440	0.1039529
5011	GOBP RENAL SODIUM ION ABSORPTION	5	0.0084301	0.6802920	0.2957945
5012	GOBP RENAL SODIUM ION TRANSPORT	5	0.0084301	0.6802920	0.2957945
2557	GOBP NEGATIVE REGULATION OF TOLL LIKE RECEPTOR 2 SIGNALING PATHWAY	5	0.0115801	0.6519835	0.3443884
7149	GOMF HISTONE H2A UBIQUITIN LIGASE ACTIVITY	5	0.0118006	0.6502698	0.3476446
3594	GOBP POSITIVE REGULATION OF SYNAPTIC PLASTICITY	5	0.0136329	0.6370359	0.3760056
7609	GOMF PLUS END DIRECTED MICROTUBULE MOTOR ACTIVITY	10	0.0008627	0.6084769	0.0711521
7967	GOMF UBIQUITIN LIKE PROTEIN SPECIFIC ENDOPEPTIDASE ACTIVITY	5	0.0187105	0.6072041	0.4192825
7625	GOMF POTASSIUM CHLORIDE SYMPORTER ACTIVITY	5	0.0189529	0.6059664	0.4199947
3612	GOBP POSITIVE REGULATION OF TORC2 SIGNALING	5	0.0207011	0.5974294	0.4335315
1540	GOBP INTRACILIARY TRANSPORT INVOLVED IN CILIUM ASSEMBLY	6	0.0130381	0.5853104	0.3685670
1516	GOBP INTESTINAL LIPID ABSORPTION	7	0.0082523	0.5765751	0.2921165
3527	GOBP POSITIVE REGULATION OF RAC PROTEIN SIGNAL TRANSDUCTION	5	0.0256785	0.5761663	0.4611018
3015	GOBP POSITIVE REGULATION OF ADIPOSE TISSUE DEVELOPMENT	5	0.0284451	0.5658521	0.4860487
7104	GOMF GLYCOLIPID MANNOSYLTRANSFERASE ACTIVITY	5	0.0285966	0.5653126	0.4860487

head(subset(k3m$enrichment_result, s.dist < 0),20) |> kbl(caption="top enrichments (negative correl)") |> kable_paper("hover", full_width = F)

top enrichments (negative correl)
	set	setSize	pANOVA	s.dist	p.adjustANOVA
6052	GOCC CYTOSOLIC LARGE RIBOSOMAL SUBUNIT	55	0.0000000	-0.8330731	0.0000000
6055	GOCC CYTOSOLIC SMALL RIBOSOMAL SUBUNIT	38	0.0000000	-0.8030890	0.0000000
6444	GOCC POLYSOMAL RIBOSOME	29	0.0000000	-0.7480566	0.0000000
2712	GOBP NUCLEAR PORE LOCALIZATION	5	0.0069471	-0.6970803	0.2646531
5193	GOBP RESPONSE TO SELENIUM ION	5	0.0070739	-0.6955252	0.2658834
4762	GOBP REGULATION OF PROTEIN OLIGOMERIZATION	5	0.0070791	-0.6954618	0.2658834
7958	GOMF UBIQUITIN LIGASE INHIBITOR ACTIVITY	8	0.0006700	-0.6944687	0.0576383
6054	GOCC CYTOSOLIC RIBOSOME	109	0.0000000	-0.6938569	0.0000000
6252	GOCC MATRIX SIDE OF MITOCHONDRIAL INNER MEMBRANE	6	0.0038457	-0.6814251	0.1947206
6292	GOCC MITOCHONDRIAL PROTON TRANSPORTING ATP SYNTHASE COMPLEX COUPLING FACTOR F O	10	0.0003309	-0.6555600	0.0326862
1758	GOBP MARGINAL ZONE B CELL DIFFERENTIATION	7	0.0027495	-0.6535697	0.1556077
731	GOBP CO TRANSCRIPTIONAL RNA 3 END PROCESSING CLEAVAGE AND POLYADENYLATION PATHWAY	6	0.0056890	-0.6518818	0.2370421
730	GOBP CO TRANSCRIPTIONAL MRNA 3 END PROCESSING CLEAVAGE AND POLYADENYLATION PATHWAY	5	0.0150052	-0.6281181	0.4001451
2337	GOBP NEGATIVE REGULATION OF LIPASE ACTIVITY	5	0.0157121	-0.6238020	0.4081062
4207	GOBP REGULATION OF CYTOCHROME C OXIDASE ACTIVITY	5	0.0184402	-0.6086004	0.4192825
6073	GOCC DOPAMINERGIC SYNAPSE	5	0.0191098	-0.6051730	0.4199947
3231	GOBP POSITIVE REGULATION OF GLIAL CELL MIGRATION	6	0.0104254	-0.6038509	0.3270701
3930	GOBP PURINE NUCLEOSIDE DIPHOSPHATE BIOSYNTHETIC PROCESS	5	0.0200420	-0.6005712	0.4285076
2593	GOBP NEGATIVE REGULATION OF UBIQUITIN PROTEIN LIGASE ACTIVITY	8	0.0034703	-0.5967384	0.1812063
1883	GOBP MITOCHONDRIAL ELECTRON TRANSPORT UBIQUINOL TO CYTOCHROME C	12	0.0003536	-0.5956048	0.0336781

up <- head(subset(k3m$enrichment_result, s.dist > 0 & p.adjustANOVA < 0.05),20)
dn <- head(subset(k3m$enrichment_result, s.dist < 0 & p.adjustANOVA < 0.05),20)
top <- rbind(up,dn)
top <- top[order(top$s.dist),]

par(mar=c(5.1, 25.1, 4.1, 2.1))
gsn <- substr(top$set, start = 1, stop = 70 )
barplot(top$s.dist,horiz=TRUE,las=1,xlab="ES",names.arg=gsn,cex.names=0.6,main="HIV neg")

mitch_plots(k3m,outfile="KIR3DL2_assoc_all.pdf")

## png 
##   2

Session information

For reproducibility.

save.image("scanalyse2.Rdata")

sessionInfo()

## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.5 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Australia/Melbourne
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] gplots_3.2.0                kableExtra_1.4.0           
##  [3] limma_3.60.6                mitch_1.19.3               
##  [5] DESeq2_1.44.0               muscat_1.18.0              
##  [7] beeswarm_0.4.0              stringi_1.8.4              
##  [9] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
## [11] Biobase_2.64.0              GenomicRanges_1.56.2       
## [13] GenomeInfoDb_1.40.1         IRanges_2.38.1             
## [15] S4Vectors_0.42.1            BiocGenerics_0.50.0        
## [17] MatrixGenerics_1.16.0       matrixStats_1.4.1          
## [19] hdf5r_1.3.11                Seurat_5.1.0               
## [21] SeuratObject_5.0.2          sp_2.1-4                   
## [23] plyr_1.8.9                 
## 
## loaded via a namespace (and not attached):
##   [1] bitops_1.0-9              spatstat.sparse_3.1-0    
##   [3] httr_1.4.7                RColorBrewer_1.1-3       
##   [5] doParallel_1.0.17         numDeriv_2016.8-1.1      
##   [7] backports_1.5.0           tools_4.4.1              
##   [9] sctransform_0.4.1         utf8_1.2.4               
##  [11] R6_2.5.1                  lazyeval_0.2.2           
##  [13] uwot_0.2.2                mgcv_1.9-1               
##  [15] GetoptLong_1.0.5          withr_3.0.2              
##  [17] GGally_2.2.1              prettyunits_1.2.0        
##  [19] gridExtra_2.3             progressr_0.15.1         
##  [21] cli_3.6.3                 spatstat.explore_3.3-3   
##  [23] fastDummies_1.7.4         labeling_0.4.3           
##  [25] sass_0.4.9                mvtnorm_1.3-2            
##  [27] spatstat.data_3.1-4       blme_1.0-6               
##  [29] ggridges_0.5.6            pbapply_1.7-2            
##  [31] systemfonts_1.1.0         svglite_2.1.3            
##  [33] scater_1.32.1             parallelly_1.39.0        
##  [35] rstudioapi_0.17.1         generics_0.1.3           
##  [37] shape_1.4.6.1             gtools_3.9.5             
##  [39] ica_1.0-3                 spatstat.random_3.3-2    
##  [41] dplyr_1.1.4               Matrix_1.7-1             
##  [43] ggbeeswarm_0.7.2          fansi_1.0.6              
##  [45] abind_1.4-8               lifecycle_1.0.4          
##  [47] yaml_2.3.10               edgeR_4.2.2              
##  [49] SparseArray_1.4.8         Rtsne_0.17               
##  [51] grid_4.4.1                promises_1.3.1           
##  [53] crayon_1.5.3              miniUI_0.1.1.1           
##  [55] lattice_0.22-6            echarts4r_0.4.5          
##  [57] beachmat_2.20.0           cowplot_1.1.3            
##  [59] pillar_1.9.0              knitr_1.49               
##  [61] ComplexHeatmap_2.20.0     rjson_0.2.23             
##  [63] boot_1.3-31               corpcor_1.6.10           
##  [65] future.apply_1.11.3       codetools_0.2-20         
##  [67] leiden_0.4.3.1            glue_1.8.0               
##  [69] spatstat.univar_3.1-1     data.table_1.16.2        
##  [71] vctrs_0.6.5               png_0.1-8                
##  [73] spam_2.11-0               Rdpack_2.6.2             
##  [75] gtable_0.3.6              cachem_1.1.0             
##  [77] xfun_0.49                 rbibutils_2.3            
##  [79] S4Arrays_1.4.1            mime_0.12                
##  [81] reformulas_0.4.0          survival_3.7-0           
##  [83] iterators_1.0.14          statmod_1.5.0            
##  [85] fitdistrplus_1.2-1        ROCR_1.0-11              
##  [87] nlme_3.1-166              pbkrtest_0.5.3           
##  [89] bit64_4.5.2               EnvStats_3.0.0           
##  [91] progress_1.2.3            RcppAnnoy_0.0.22         
##  [93] bslib_0.8.0               TMB_1.9.15               
##  [95] irlba_2.3.5.1             vipor_0.4.7              
##  [97] KernSmooth_2.23-24        colorspace_2.1-1         
##  [99] ggrastr_1.0.2             tidyselect_1.2.1         
## [101] bit_4.5.0                 compiler_4.4.1           
## [103] BiocNeighbors_1.22.0      xml2_1.3.6               
## [105] DelayedArray_0.30.1       plotly_4.10.4            
## [107] scales_1.3.0              caTools_1.18.3           
## [109] remaCor_0.0.18            lmtest_0.9-40            
## [111] stringr_1.5.1             digest_0.6.37            
## [113] goftest_1.2-3             spatstat.utils_3.1-1     
## [115] minqa_1.2.8               variancePartition_1.34.0 
## [117] rmarkdown_2.29            aod_1.3.3                
## [119] XVector_0.44.0            RhpcBLASctl_0.23-42      
## [121] htmltools_0.5.8.1         pkgconfig_2.0.3          
## [123] lme4_1.1-35.5             sparseMatrixStats_1.16.0 
## [125] fastmap_1.2.0             rlang_1.1.4              
## [127] GlobalOptions_0.1.2       htmlwidgets_1.6.4        
## [129] UCSC.utils_1.0.0          shiny_1.9.1              
## [131] DelayedMatrixStats_1.26.0 farver_2.1.2             
## [133] jquerylib_0.1.4           zoo_1.8-12               
## [135] jsonlite_1.8.9            BiocParallel_1.38.0      
## [137] BiocSingular_1.20.0       magrittr_2.0.3           
## [139] scuttle_1.14.0            GenomeInfoDbData_1.2.12  
## [141] dotCall64_1.2             patchwork_1.3.0          
## [143] munsell_0.5.1             Rcpp_1.0.13-1            
## [145] viridis_0.6.5             reticulate_1.40.0        
## [147] zlibbioc_1.50.0           MASS_7.3-61              
## [149] ggstats_0.7.0             listenv_0.9.1            
## [151] ggrepel_0.9.6             deldir_2.0-4             
## [153] splines_4.4.1             tensor_1.5               
## [155] hms_1.1.3                 circlize_0.4.16          
## [157] locfit_1.5-9.10           igraph_2.1.1             
## [159] spatstat.geom_3.3-4       RcppHNSW_0.6.0           
## [161] reshape2_1.4.4            ScaledMatrix_1.12.0      
## [163] evaluate_1.0.1            nloptr_2.1.1             
## [165] foreach_1.5.2             httpuv_1.6.15            
## [167] RANN_2.6.2                tidyr_1.3.1              
## [169] purrr_1.0.2               polyclip_1.10-7          
## [171] future_1.34.0             clue_0.3-66              
## [173] scattermore_1.2           ggplot2_3.5.1            
## [175] rsvd_1.0.5                broom_1.0.7              
## [177] xtable_1.8-4              fANCOVA_0.6-1            
## [179] RSpectra_0.16-2           later_1.4.0              
## [181] viridisLite_0.4.2         tibble_3.2.1             
## [183] lmerTest_3.1-3            glmmTMB_1.1.10           
## [185] cluster_2.1.6             globals_0.16.3