Single cell transcriptomes of macrophages with HIV 2024 - comparative analysis
Burnet Bioinformatics group
2025-05-21
Introduction
Ee are interested in comparing DEGs between the following comparison groups:
Latently- vs productively-infected cells (groups 3 vs 4).
Latently-infected vs bystander cells (2 vs 3).
Productively-infected vs mock (4 vs 1)
Mock vs bystander (1 vs 2).
As discussed, I think we will do these comparisons separately for both MDM and AlvMDM samples initially. Then, it would be of interest to compare DEGs for MDM vs AlvMDM for each of the four infection groups.
suppressPackageStartupMessages({
library("kableExtra")
library("ggplot2")
library("plyr")
library("Seurat")
library("hdf5r")
library("SingleCellExperiment")
library("parallel")
library("stringi")
library("beeswarm")
library("muscat")
library("DESeq2")
library("mitch")
library("harmony")
library("celldex")
library("SingleR")
library("limma")
library("gplots")
})Load data
| Sample | Patient | Group |
|---|---|---|
| mock0 | MDMP236 | mock |
| mock1 | MDMV236 | mock |
| mock2 | MDMO236 | mock |
| mock3 | MDMP239 | mock |
| mock4 | AlvP239 | mock |
| mock5 | AlvM239 | mock |
| mock6 | AlvN239 | mock |
| active0 | MDMP236 | active |
| active1 | MDMV236 | active |
| active2 | MDMO236 | active |
| active3 | MDMP239 | active |
| active4 | AlvP239 | active |
| active5 | AlvM239 | active |
| active6 | AlvN239 | active |
| latent0 | MDMP236 | latent |
| latent1 | MDMV236 | latent |
| latent2 | MDMO236 | latent |
| latent3 | MDMP239 | latent |
| latent4 | AlvP239 | latent |
| latent5 | AlvM239 | latent |
| latent6 | AlvN239 | latent |
| bystander0 | MDMP236 | bystander |
| bystander1 | MDMV236 | bystander |
| bystander2 | MDMO236 | bystander |
| bystander3 | MDMP239 | bystander |
| bystander4 | AlvP239 | bystander |
| bystander5 | AlvM239 | bystander |
| bystander6 | AlvN239 | bystander |
| react6 | AlvN239 | reactivated |
exclude react6
ss <- read.table("samplesheet.tsv",header=TRUE,row.names=1)
ss %>% kbl(caption="sample sheet") %>% kable_paper("hover", full_width = F)| Patient | Group | |
|---|---|---|
| mock0 | MDMP236 | mock |
| mock1 | MDMV236 | mock |
| mock2 | MDMO236 | mock |
| mock3 | MDMP239 | mock |
| mock4 | AlvP239 | mock |
| mock5 | AlvM239 | mock |
| mock6 | AlvN239 | mock |
| active0 | MDMP236 | active |
| active1 | MDMV236 | active |
| active2 | MDMO236 | active |
| active3 | MDMP239 | active |
| active4 | AlvP239 | active |
| active5 | AlvM239 | active |
| active6 | AlvN239 | active |
| latent0 | MDMP236 | latent |
| latent1 | MDMV236 | latent |
| latent2 | MDMO236 | latent |
| latent3 | MDMP239 | latent |
| latent4 | AlvP239 | latent |
| latent5 | AlvM239 | latent |
| latent6 | AlvN239 | latent |
| bystander0 | MDMP236 | bystander |
| bystander1 | MDMV236 | bystander |
| bystander2 | MDMO236 | bystander |
| bystander3 | MDMP239 | bystander |
| bystander4 | AlvP239 | bystander |
| bystander5 | AlvM239 | bystander |
| bystander6 | AlvN239 | bystander |
| react6 | AlvN239 | reactivated |
mylist <- readRDS("macrophage_counts.rds")Make single cell experiment object
comb <- do.call(cbind,mylist)
sce <- SingleCellExperiment(list(counts=comb))
sce## class: SingleCellExperiment
## dim: 36622 24311
## metadata(0):
## assays(1): counts
## rownames(36622): HIV_LTRR HIV_LTRU5 ... AC007325.4 AC007325.2
## rowData names(0):
## colnames(24311): mdm_mock1|AAACGAATCACATACG mdm_mock1|AAACGCTCATCAGCGC
## ... react6|TTTGTTGTCTGAACGT react6|TTTGTTGTCTTGGCTC
## colData names(0):## Found more than one class "package_version" in cache; using the first, from namespace 'SeuratObject'## Also defined by 'alabaster.base'## reducedDimNames(0):
## mainExpName: NULL## Found more than one class "package_version" in cache; using the first, from namespace 'SeuratObject'
## Also defined by 'alabaster.base'## altExpNames(0):Normalise data
cellmetadata <- data.frame(colnames(comb) ,sapply(strsplit(colnames(comb),"\\|"),"[[",1))
colnames(cellmetadata) <- c("cell","sample")
comb <- CreateSeuratObject(comb, project = "mac", assay = "RNA", meta.data = cellmetadata)## Warning: Feature names cannot have underscores ('_'), replacing with dashes
## ('-')comb <- NormalizeData(comb)## Normalizing layer: countscomb <- FindVariableFeatures(comb, selection.method = "vst", nfeatures = 2000)## Finding variable features for layer countscomb <- ScaleData(comb)## Centering and scaling data matrixPCA and Cluster
comb <- RunPCA(comb, features = VariableFeatures(object = comb))## PC_ 1
## Positive: GAPDH, FABP3, TXN, IFI30, S100A10, PRDX6, TUBA1B, BLVRB, OTOA, S100A9
## FAH, C15orf48, CYSTM1, GCHFR, CARD16, GSTP1, HAMP, PSMA7, CSTA, FABP4
## ACTG1, CTSB, H2AFZ, LDHB, LINC01827, CFD, TUBA1A, MMP9, LINC02244, SELENOW
## Negative: ARL15, DOCK3, FTX, NEAT1, EXOC4, MALAT1, DPYD, LRMDA, RASAL2, JMJD1C
## TMEM117, PLXDC2, VPS13B, FHIT, ATG7, TRIO, TPRG1, ZNF438, ZFAND3, MAML2
## MITF, COP1, ZEB2, ELMO1, DENND4C, MED13L, TCF12, ERC1, JARID2, FMNL2
## PC_ 2
## Positive: HLA-DRB1, CD74, HLA-DRA, HLA-DPA1, GCLC, HLA-DPB1, LYZ, RCBTB2, KCNMA1, MRC1
## SPRED1, C1QA, FGL2, AC020656.1, SLCO2B1, CYP1B1, AIF1, HLA-DRB5, PTGDS, S100A4
## VAMP5, LINC02345, CA2, CRIP1, CAMK1, ALOX5AP, RTN1, HLA-DQB1, MX1, TGFBI
## Negative: CYSTM1, CD164, PSAT1, FAH, FDX1, GDF15, ATP6V1D, BCAT1, SAT1, CCPG1
## PHGDH, PSMA7, HEBP2, SLAMF9, RETREG1, GARS, HES2, TXN, TCEA1, RHOQ
## RILPL2, B4GALT5, CLGN, NUPR1, CSTA, SPTBN1, HSD17B12, SNHG5, STMN1, PTER
## PC_ 3
## Positive: NCAPH, CRABP2, RGCC, CHI3L1, TM4SF19, DUSP2, GAL, CCL22, AC015660.2, ACAT2
## LINC01010, TMEM114, MGST1, RGS20, TRIM54, LINC02244, MREG, NUMB, TCTEX1D2, GPC4
## CCND1, POLE4, SYNGR1, SLC20A1, SERTAD2, IL1RN, GCLC, CLU, PLEK, AC092353.2
## Negative: MARCKS, CD14, BTG1, MS4A6A, TGFBI, CTSC, FPR3, HLA-DQA1, AIF1, MPEG1
## MEF2C, CD163, IFI30, TIMP1, HLA-DPB1, ALDH2, SELENOP, NUPR1, NAMPT, HLA-DQB1
## HIF1A, C1QC, MS4A7, FUCA1, EPB41L3, HLA-DQA2, RNASE1, ARL4C, ZNF331, TCF4
## PC_ 4
## Positive: ACTG1, TPM4, CTSB, CCL3, TUBA1B, CSF1, DHCR24, CYTOR, LGMN, INSIG1
## GAPDH, TUBB, CD36, HAMP, C1QA, CCL7, AIF1, MGLL, LIMA1, TYMS
## C1QC, HSP90B1, CCL2, C1QB, TNFSF13, PCLAF, C15orf48, CLSPN, CAMK1, TK1
## Negative: PTGDS, LINC02244, CLU, CSTA, CCPG1, MGST1, SYNGR1, EPHB1, LINC01010, ALDH2
## LY86-AS1, AC015660.2, GAS5, NCF1, BX664727.3, S100P, AP000331.1, TMEM91, SNHG5, CLEC12A
## APOD, PDE4D, C1QTNF4, VAMP5, DIXDC1, LYZ, AC073359.2, ARHGAP15, RCBTB2, CFD
## PC_ 5
## Positive: TYMS, PCLAF, TK1, MKI67, MYBL2, RRM2, CENPM, BIRC5, CEP55, CLSPN
## CDK1, DIAPH3, SHCBP1, NUSAP1, CENPF, CENPK, PRC1, TOP2A, NCAPG, ESCO2
## KIF11, ANLN, CCNA2, TPX2, ASPM, FAM111B, MAD2L1, RAD51AP1, GTSE1, HMMR
## Negative: HIV-BaLEnv, HIV-LTRU5, HIV-Polprot, HIV-Gagp17, HIV-Nef, HIV-TatEx1, HIV-Polp15p31, HIV-LTRR, HIV-Vif, HIV-Gagp1Pol
## HIV-TatEx2Rev, HIV-Gagp2p7, HIV-EnvStart, HIV-Vpu, HIV-Vpr, HIV-EGFP, CTSB, MMP19, IL6R-AS1, CSF1
## IL1RN, CCL3, MGLL, INSIG1, AL157912.1, SDS, TCTEX1D2, TNFRSF9, LGMN, PHLDA1comb <- RunHarmony(comb,"sample")## Transposing data matrix## Initializing state using k-means centroids initialization## Harmony 1/10## Harmony 2/10## Harmony 3/10## Harmony 4/10## Harmony converged after 4 iterationsDimHeatmap(comb, dims = 1:6, cells = 500, balanced = TRUE)
ElbowPlot(comb)
comb <- JackStraw(comb, num.replicate = 100)
comb <- FindNeighbors(comb, dims = 1:10)## Computing nearest neighbor graph## Computing SNNcomb <- FindClusters(comb, resolution = 0.5)## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 24311
## Number of edges: 745637
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8948
## Number of communities: 14
## Elapsed time: 3 secondsUMAP
comb <- RunUMAP(comb, dims = 1:10)## 14:02:19 UMAP embedding parameters a = 0.9922 b = 1.112## 14:02:19 Read 24311 rows and found 10 numeric columns## 14:02:19 Using Annoy for neighbor search, n_neighbors = 30## 14:02:19 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 14:02:21 Writing NN index file to temp file /tmp/RtmpRdHgON/file11d0d411a46f10
## 14:02:21 Searching Annoy index using 1 thread, search_k = 3000
## 14:02:28 Annoy recall = 100%
## 14:02:28 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 14:02:31 Initializing from normalized Laplacian + noise (using RSpectra)
## 14:02:31 Commencing optimization for 200 epochs, with 972676 positive edges
## 14:02:31 Using rng type: pcg
## 14:02:38 Optimization finishedDimPlot(comb, reduction = "umap")
Assign names to clusters
ADGRE1, CCR2, CD169, CX3CR1, CD206, CD163, LYVE1, CD9, TREM2
HLA-DP, HLA-DM, HLA-DOA, HLA-DOB, HLA-DQ, and HLA-DR.
message("macrophage markers")## macrophage markersFeaturePlot(comb, features = c("ADGRE1", "CCR2", "SIGLEC1", "CX3CR1", "MRC1", "CD163", "LYVE1", "CD9", "TREM2"))
DimPlot(comb, reduction = "umap", label = TRUE, pt.size = 0.5) + NoLegend()
That’s pretty useless. Let’s use celldex pkg to annotate cells and get the macs.
ref <- celldex::MonacoImmuneData()
DefaultAssay(comb) <- "RNA"
comb2 <- as.SingleCellExperiment(comb)## Found more than one class "package_version" in cache; using the first, from namespace 'SeuratObject'## Also defined by 'alabaster.base'## Found more than one class "package_version" in cache; using the first, from namespace 'SeuratObject'## Also defined by 'alabaster.base'## Found more than one class "package_version" in cache; using the first, from namespace 'SeuratObject'## Also defined by 'alabaster.base'## Found more than one class "package_version" in cache; using the first, from namespace 'SeuratObject'## Also defined by 'alabaster.base'## Found more than one class "package_version" in cache; using the first, from namespace 'SeuratObject'## Also defined by 'alabaster.base'## Found more than one class "package_version" in cache; using the first, from namespace 'SeuratObject'## Also defined by 'alabaster.base'lc <- logcounts(comb2)
pred_imm_broad <- SingleR(test=comb2, ref=ref,
labels=ref$label.main)
head(pred_imm_broad)## DataFrame with 6 rows and 4 columns
## scores labels
## <matrix> <character>
## mdm_mock1|AAACGAATCACATACG 0.306472:0.325537:0.166567:... Monocytes
## mdm_mock1|AAACGCTCATCAGCGC 0.311527:0.281167:0.189514:... Monocytes
## mdm_mock1|AAACGCTGTCGAGTGA 0.316271:0.276472:0.170736:... Monocytes
## mdm_mock1|AAAGGTAAGCCATATC 0.290547:0.291036:0.154976:... Monocytes
## mdm_mock1|AAAGGTAGTTTCCAAG 0.290907:0.279384:0.181857:... Monocytes
## mdm_mock1|AAATGGAAGATCGCCC 0.242404:0.241021:0.117564:... Monocytes
## delta.next pruned.labels
## <numeric> <character>
## mdm_mock1|AAACGAATCACATACG 0.1823977 Monocytes
## mdm_mock1|AAACGCTCATCAGCGC 0.0338547 Monocytes
## mdm_mock1|AAACGCTGTCGAGTGA 0.1301308 Monocytes
## mdm_mock1|AAAGGTAAGCCATATC 0.1794308 Monocytes
## mdm_mock1|AAAGGTAGTTTCCAAG 0.0952702 Monocytes
## mdm_mock1|AAATGGAAGATCGCCC 0.1508974 Monocytestable(pred_imm_broad$pruned.labels)##
## Basophils Dendritic cells Monocytes
## 1 86 23423cellmetadata$label <- pred_imm_broad$pruned.labels
pred_imm_fine <- SingleR(test=comb2, ref=ref,
labels=ref$label.fine)
head(pred_imm_fine)## DataFrame with 6 rows and 4 columns
## scores labels
## <matrix> <character>
## mdm_mock1|AAACGAATCACATACG 0.180057:0.485292:0.202974:... Classical monocytes
## mdm_mock1|AAACGCTCATCAGCGC 0.195973:0.430960:0.226764:... Classical monocytes
## mdm_mock1|AAACGCTGTCGAGTGA 0.170594:0.441313:0.186890:... Classical monocytes
## mdm_mock1|AAAGGTAAGCCATATC 0.156243:0.415082:0.167816:... Classical monocytes
## mdm_mock1|AAAGGTAGTTTCCAAG 0.185006:0.431679:0.205883:... Classical monocytes
## mdm_mock1|AAATGGAAGATCGCCC 0.125917:0.383407:0.146835:... Classical monocytes
## delta.next pruned.labels
## <numeric> <character>
## mdm_mock1|AAACGAATCACATACG 0.0675290 Classical monocytes
## mdm_mock1|AAACGCTCATCAGCGC 0.1150706 Classical monocytes
## mdm_mock1|AAACGCTGTCGAGTGA 0.0651352 Classical monocytes
## mdm_mock1|AAAGGTAAGCCATATC 0.1076301 Classical monocytes
## mdm_mock1|AAAGGTAGTTTCCAAG 0.1521533 Classical monocytes
## mdm_mock1|AAATGGAAGATCGCCC 0.1282183 Classical monocytestable(pred_imm_fine$pruned.labels)##
## Classical monocytes Intermediate monocytes
## 20826 2648
## Low-density neutrophils Myeloid dendritic cells
## 1 90
## Non classical monocytes Plasmacytoid dendritic cells
## 11 6cellmetadata$finelabel <- pred_imm_fine$pruned.labels
col_pal <- c('#e31a1c', '#ff7f00', "#999900", '#cc00ff', '#1f78b4', '#fdbf6f',
'#33a02c', '#fb9a99', "#a6cee3", "#cc6699", "#b2df8a", "#99004d", "#66ff99",
"#669999", "#006600", "#9966ff", "#cc9900", "#e6ccff", "#3399ff", "#ff66cc",
"#ffcc66", "#003399")
annot_df <- data.frame(
barcodes = rownames(pred_imm_broad),
monaco_broad_annotation = pred_imm_broad$labels,
monaco_broad_pruned_labels = pred_imm_broad$pruned.labels,
monaco_fine_annotation = pred_imm_fine$labels,
monaco_fine_pruned_labels = pred_imm_fine$pruned.labels
)
meta_inf <- comb@meta.data
meta_inf$cell_barcode <- colnames(comb)
meta_inf <- meta_inf %>% dplyr::left_join(y = annot_df,
by = c("cell_barcode" = "barcodes"))
rownames(meta_inf) <- colnames(lc)
comb@meta.data <- meta_inf
DimPlot(comb, label=TRUE, group.by = "monaco_broad_annotation", reduction = "umap",
cols = col_pal, pt.size = 0.5) + ggtitle("Annotation With the Monaco Reference Database")
DimPlot(comb, label=TRUE, group.by = "monaco_fine_annotation", reduction = "umap",
cols = col_pal, pt.size = 0.5) + ggtitle("Annotation With the Monaco Reference Database")
Make MDM object
mdmlist <- mylist[grep("mdm",names(mylist))]
comb1 <- do.call(cbind,mdmlist)
sce1 <- SingleCellExperiment(list(counts=comb1))
sce1## class: SingleCellExperiment
## dim: 36622 10269
## metadata(0):
## assays(1): counts
## rownames(36622): HIV_LTRR HIV_LTRU5 ... AC007325.4 AC007325.2
## rowData names(0):
## colnames(10269): mdm_mock1|AAACGAATCACATACG mdm_mock1|AAACGCTCATCAGCGC
## ... mdm_bystander4|TTTGTTGAGAACGCGT mdm_bystander4|TTTGTTGCAAATGCGG
## colData names(0):## Found more than one class "package_version" in cache; using the first, from namespace 'SeuratObject'## Also defined by 'alabaster.base'## reducedDimNames(0):
## mainExpName: NULL## Found more than one class "package_version" in cache; using the first, from namespace 'SeuratObject'
## Also defined by 'alabaster.base'## altExpNames(0):cellmetadata1 <- data.frame(colnames(comb1) ,sapply(strsplit(colnames(comb1),"\\|"),"[[",1))
colnames(cellmetadata1) <- c("cell","sample")
comb1 <- CreateSeuratObject(comb1, project = "mac", assay = "RNA", meta.data = cellmetadata1)## Warning: Feature names cannot have underscores ('_'), replacing with dashes
## ('-')comb1 <- NormalizeData(comb1)## Normalizing layer: countscomb1 <- FindVariableFeatures(comb1, selection.method = "vst", nfeatures = 2000)## Finding variable features for layer countscomb1 <- ScaleData(comb1)## Centering and scaling data matrixcomb1 <- RunPCA(comb1, features = VariableFeatures(object = comb1))## PC_ 1
## Positive: S100A10, TXN, COX5B, PRDX6, FABP3, C15orf48, BCL2A1, CALM3, PSME2, TUBA1B
## CYSTM1, SPP1, CHI3L1, TUBA1A, CRABP2, ACTB, ACTG1, MGST1, ACAT2, FBP1
## IFI30, FABP4, GAL, HAMP, RGCC, MMP9, AKR7A2, LILRA1, CTSL, LDHA
## Negative: ARL15, FTX, EXOC4, NEAT1, DPYD, FHIT, JMJD1C, RAD51B, VPS13B, ZFAND3
## MALAT1, PLXDC2, TRIO, LRMDA, ZEB2, DOCK3, COP1, MBD5, TCF12, ATXN1
## RASAL2, MAML2, ATG7, ZSWIM6, FNDC3B, DOCK4, ELMO1, ZNF438, SPIDR, ARHGAP15
## PC_ 2
## Positive: TM4SF19, ANO5, GPC4, CYSTM1, FNIP2, TXNRD1, BCL2A1, SPP1, SNX10, PSD3
## RETREG1, RGS20, TXN, TCTEX1D2, SLC28A3, MGST1, EPB41L1, FABP3, RGCC, CALM3
## NIBAN1, TGM2, ATP6V0D2, ACAT2, CCL22, CCDC26, LINC01010, CHI3L1, MREG, CRABP2
## Negative: HLA-DPB1, HLA-DRA, CD74, HLA-DPA1, TGFBI, MS4A6A, AIF1, HLA-DQB1, C1QA, HLA-DQA1
## HLA-DRB1, CEBPD, FPR3, C1QC, MS4A7, CD163, CD14, MPEG1, LYZ, TIMP1
## ST8SIA4, LILRB2, FOS, EPB41L3, TCF4, MAFB, HLA-DRB5, RNASE1, SELENOP, FCN1
## PC_ 3
## Positive: CCPG1, NUPR1, HES2, PSAT1, S100P, CLGN, CARD16, TCEA1, PHGDH, SUPV3L1
## BTG1, NIBAN1, G0S2, BEX2, NMB, PDE4D, PLEKHA5, RAB6B, STMN1, XIST
## ME1, CLEC4A, CLEC4E, RETREG1, IFI6, CYSTM1, GDF15, CXCR4, DUSP1, SEL1L3
## Negative: ACTB, CALR, SLC35F1, TIMP3, TUBA1B, FBP1, ACTG1, LINC01091, HSP90B1, GSN
## MGLL, IL1RN, GLIPR1, INSIG1, LPL, GCLC, PLEK, PDIA4, MADD, RGCC
## LDHA, MANF, ALCAM, HLA-DRB1, IGSF6, TMEM176B, DHCR24, CSF1, TUBA1A, CYP1B1
## PC_ 4
## Positive: PTGDS, NCAPH, CLU, BX664727.3, LINC02244, SYNGR1, COX5B, RCBTB2, CRABP2, AL136317.2
## MT-ATP6, SSBP3, RARRES1, ADRA2B, LINC01010, AC015660.2, S100A4, CRIP1, MT-ND2, LY86-AS1
## RNASE6, HLA-C, S100A8, VAMP5, MT-CO3, MT-CYB, CCL22, CPE, CSRP2, TMEM176B
## Negative: HIV-Gagp17, HIV-BaLEnv, HIV-Polprot, HIV-Polp15p31, HIV-LTRU5, HIV-Vif, HIV-Nef, HIV-TatEx1, HIV-LTRR, HIV-Gagp1Pol
## HIV-Gagp2p7, HIV-TatEx2Rev, MARCKS, CCL3, HIV-Vpu, HIV-EGFP, TPM4, UGCG, SNCA, HIV-EnvStart
## HIV-Vpr, CD36, LGMN, G0S2, HES4, B4GALT5, CLEC4A, BCAT1, TNFRSF9, SDS
## PC_ 5
## Positive: TYMS, PCLAF, BIRC5, MKI67, CEP55, CENPF, CENPM, TK1, CDKN3, PRC1
## CDK1, DIAPH3, MYBL2, SHCBP1, NUSAP1, DLGAP5, RRM2, CENPK, HMMR, TPX2
## ASPM, NCAPG, CCNA2, MAD2L1, PTTG1, TOP2A, CLSPN, KIF4A, CIT, KIF11
## Negative: GCLC, TIMP3, TMEM117, TMEM176B, AC067751.1, CRABP2, NUMB, LINC01091, CHI3L1, LY86-AS1
## LINC00278, TNFSF14, RGCC, KCNJ1, IGSF6, SH3RF3, AC015660.2, IL1RN, DUSP2, MADD
## KCNA2, DOCK3, FLT1, RPS4Y1, TTTY14, TNS3, GADD45G, NCAPH, AL157886.1, TM4SF19-AS1comb1 <- RunHarmony(comb1,"sample")## Transposing data matrix## Initializing state using k-means centroids initialization## Harmony 1/10## Harmony 2/10## Harmony 3/10## Harmony 4/10## Harmony 5/10## Harmony converged after 5 iterationsDimHeatmap(comb1, dims = 1:6, cells = 500, balanced = TRUE)
ElbowPlot(comb1)
comb1 <- JackStraw(comb1, num.replicate = 100)
comb1 <- FindNeighbors(comb1, dims = 1:10)## Computing nearest neighbor graph## Computing SNNcomb1 <- FindClusters(comb1, resolution = 0.5)## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 10269
## Number of edges: 322519
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8766
## Number of communities: 12
## Elapsed time: 0 secondscomb1 <- RunUMAP(comb1, dims = 1:10)## 14:06:37 UMAP embedding parameters a = 0.9922 b = 1.112## 14:06:37 Read 10269 rows and found 10 numeric columns## 14:06:37 Using Annoy for neighbor search, n_neighbors = 30## 14:06:37 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 14:06:37 Writing NN index file to temp file /tmp/RtmpRdHgON/file11d0d455058116
## 14:06:38 Searching Annoy index using 1 thread, search_k = 3000
## 14:06:40 Annoy recall = 100%
## 14:06:41 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 14:06:43 Initializing from normalized Laplacian + noise (using RSpectra)
## 14:06:43 Commencing optimization for 200 epochs, with 405542 positive edges
## 14:06:43 Using rng type: pcg
## 14:06:47 Optimization finishedDimPlot(comb1, reduction = "umap")
message("macrophage markers")## macrophage markersFeaturePlot(comb1, features = c("ADGRE1", "CCR2", "SIGLEC1", "CX3CR1", "MRC1", "CD163", "LYVE1", "CD9", "TREM2"))
DimPlot(comb1, reduction = "umap", label = TRUE, pt.size = 0.5) + NoLegend()
ref <- celldex::MonacoImmuneData()
DefaultAssay(comb1) <- "RNA"
comb21 <- as.SingleCellExperiment(comb1)## Found more than one class "package_version" in cache; using the first, from namespace 'SeuratObject'
## Also defined by 'alabaster.base'
## Found more than one class "package_version" in cache; using the first, from namespace 'SeuratObject'
## Also defined by 'alabaster.base'
## Found more than one class "package_version" in cache; using the first, from namespace 'SeuratObject'
## Also defined by 'alabaster.base'
## Found more than one class "package_version" in cache; using the first, from namespace 'SeuratObject'
## Also defined by 'alabaster.base'
## Found more than one class "package_version" in cache; using the first, from namespace 'SeuratObject'
## Also defined by 'alabaster.base'
## Found more than one class "package_version" in cache; using the first, from namespace 'SeuratObject'
## Also defined by 'alabaster.base'lc1 <- logcounts(comb21)
pred_imm_broad1 <- SingleR(test=comb21, ref=ref,labels=ref$label.main)
head(pred_imm_broad1)## DataFrame with 6 rows and 4 columns
## scores labels
## <matrix> <character>
## mdm_mock1|AAACGAATCACATACG 0.306472:0.325537:0.166567:... Monocytes
## mdm_mock1|AAACGCTCATCAGCGC 0.311527:0.281167:0.189514:... Monocytes
## mdm_mock1|AAACGCTGTCGAGTGA 0.316271:0.276472:0.170736:... Monocytes
## mdm_mock1|AAAGGTAAGCCATATC 0.290547:0.291036:0.154976:... Monocytes
## mdm_mock1|AAAGGTAGTTTCCAAG 0.290907:0.279384:0.181857:... Monocytes
## mdm_mock1|AAATGGAAGATCGCCC 0.242404:0.241021:0.117564:... Monocytes
## delta.next pruned.labels
## <numeric> <character>
## mdm_mock1|AAACGAATCACATACG 0.1823977 Monocytes
## mdm_mock1|AAACGCTCATCAGCGC 0.0338547 Monocytes
## mdm_mock1|AAACGCTGTCGAGTGA 0.1301308 Monocytes
## mdm_mock1|AAAGGTAAGCCATATC 0.1794308 Monocytes
## mdm_mock1|AAAGGTAGTTTCCAAG 0.0952702 Monocytes
## mdm_mock1|AAATGGAAGATCGCCC 0.1508974 Monocytestable(pred_imm_broad1$pruned.labels)##
## Basophils Dendritic cells Monocytes
## 1 71 9629cellmetadata1$label <- pred_imm_broad1$pruned.labels
pred_imm_fine1 <- SingleR(test=comb21, ref=ref, labels=ref$label.fine)
head(pred_imm_fine1)## DataFrame with 6 rows and 4 columns
## scores labels
## <matrix> <character>
## mdm_mock1|AAACGAATCACATACG 0.180057:0.485292:0.202974:... Classical monocytes
## mdm_mock1|AAACGCTCATCAGCGC 0.195973:0.430960:0.226764:... Classical monocytes
## mdm_mock1|AAACGCTGTCGAGTGA 0.170594:0.441313:0.186890:... Classical monocytes
## mdm_mock1|AAAGGTAAGCCATATC 0.156243:0.415082:0.167816:... Classical monocytes
## mdm_mock1|AAAGGTAGTTTCCAAG 0.185006:0.431679:0.205883:... Classical monocytes
## mdm_mock1|AAATGGAAGATCGCCC 0.125917:0.383407:0.146835:... Classical monocytes
## delta.next pruned.labels
## <numeric> <character>
## mdm_mock1|AAACGAATCACATACG 0.0675290 Classical monocytes
## mdm_mock1|AAACGCTCATCAGCGC 0.1150706 Classical monocytes
## mdm_mock1|AAACGCTGTCGAGTGA 0.0651352 Classical monocytes
## mdm_mock1|AAAGGTAAGCCATATC 0.1076301 Classical monocytes
## mdm_mock1|AAAGGTAGTTTCCAAG 0.1521533 Classical monocytes
## mdm_mock1|AAATGGAAGATCGCCC 0.1282183 Classical monocytestable(pred_imm_fine1$pruned.labels)##
## Classical monocytes Intermediate monocytes
## 8841 823
## Low-density neutrophils Myeloid dendritic cells
## 1 51
## Non classical monocytes Plasmacytoid dendritic cells
## 8 6cellmetadata1$finelabel <- pred_imm_fine1$pruned.labels
col_pal <- c('#e31a1c', '#ff7f00', "#999900", '#cc00ff', '#1f78b4', '#fdbf6f',
'#33a02c', '#fb9a99', "#a6cee3", "#cc6699", "#b2df8a", "#99004d", "#66ff99",
"#669999", "#006600", "#9966ff", "#cc9900", "#e6ccff", "#3399ff", "#ff66cc",
"#ffcc66", "#003399")
annot_df1 <- data.frame(
barcodes = rownames(pred_imm_broad1),
monaco_broad_annotation = pred_imm_broad1$labels,
monaco_broad_pruned_labels = pred_imm_broad1$pruned.labels,
monaco_fine_annotation = pred_imm_fine1$labels,
monaco_fine_pruned_labels = pred_imm_fine1$pruned.labels)
meta_inf1 <- comb1@meta.data
meta_inf1$cell_barcode <- colnames(comb1)
meta_inf1 <- meta_inf1 %>% dplyr::left_join(y = annot_df1, by = c("cell_barcode" = "barcodes"))
rownames(meta_inf1) <- colnames(lc1)
comb1@meta.data <- meta_inf1
DimPlot(comb1, label=TRUE, group.by = "monaco_broad_annotation", reduction = "umap",
cols = col_pal, pt.size = 0.5) + ggtitle("Annotation With the Monaco Reference Database")
DimPlot(comb1, label=TRUE, group.by = "monaco_fine_annotation", reduction = "umap",
cols = col_pal, pt.size = 0.5) + ggtitle("Annotation With the Monaco Reference Database")
message("extract mono")## extract monomono <- comb1[,which(meta_inf1$monaco_broad_annotation == "Monocytes")]
mono_metainf1 <- meta_inf1[which(meta_inf1$monaco_broad_annotation == "Monocytes"),]
mono_metainf1 <- mono_metainf1[grep("monocytes",mono_metainf1$monaco_fine_pruned_labels),]
mono <- mono[,which(colnames(mono) %in% rownames(mono_metainf1))]
mono <- FindVariableFeatures(mono, selection.method = "vst", nfeatures = 2000)## Finding variable features for layer countsmono <- RunPCA(mono, features = VariableFeatures(object = mono))## Warning in PrepDR5(object = object, features = features, layer = layer, : The
## following features were not available: AL358779.1, CSTA, RALA, PLAUR, ACP5,
## ALDH1A1, NABP1, PTPRJ, S100A9, ZCCHC7, LCP1, GATAD2B, FDX1, RPS20, EML1, RAB10,
## AL669970.1, RPS6KA2, RASGEF1B, FAM13B, SLC11A1, AC002429.2.## PC_ 1
## Positive: S100A10, TXN, COX5B, PRDX6, C15orf48, FABP3, BCL2A1, PSME2, TUBA1B, CALM3
## ACTB, CYSTM1, CHI3L1, ACTG1, TUBA1A, CRABP2, ACAT2, MGST1, IFI30, SPP1
## FBP1, RGCC, LDHA, MMP9, CTSL, HAMP, AKR7A2, ANXA1, LILRA1, HLA-C
## Negative: ARL15, FTX, EXOC4, NEAT1, DPYD, FHIT, RAD51B, MALAT1, VPS13B, JMJD1C
## ZFAND3, MBD5, LRMDA, TRIO, ZEB2, TCF12, DOCK4, COP1, DOCK3, ZSWIM6
## SPIDR, ARHGAP15, ELMO1, PLXDC2, MAML2, RERE, SBF2, ATP9B, MED13L, ATG7
## PC_ 2
## Positive: TM4SF19, ANO5, GPC4, CYSTM1, FNIP2, TXNRD1, BCL2A1, SPP1, PSD3, SNX10
## RETREG1, RGS20, TCTEX1D2, SLC28A3, TXN, EPB41L1, NIBAN1, MGST1, CALM3, FABP3
## RGCC, TGM2, CCL22, ATP6V0D2, CCDC26, LINC01010, AC092353.2, ACAT2, LINC01135, HES2
## Negative: HLA-DPB1, HLA-DRA, CD74, HLA-DPA1, TGFBI, AIF1, HLA-DQB1, MS4A6A, C1QA, HLA-DQA1
## HLA-DRB1, CEBPD, C1QC, FPR3, MS4A7, CD163, CD14, MPEG1, TIMP1, LYZ
## ST8SIA4, FOS, EPB41L3, MAFB, TCF4, HLA-DRB5, SELENOP, FCN1, RNASE1, ARL4C
## PC_ 3
## Positive: CCPG1, NUPR1, HES2, PSAT1, CARD16, CLGN, S100P, TCEA1, BTG1, SUPV3L1
## PHGDH, NIBAN1, G0S2, BEX2, NMB, STMN1, IFI6, CLEC4A, CLEC4E, PLEKHA5
## RAB6B, DUSP1, GDF15, CYSTM1, ME1, PDE4D, CXCR4, RETREG1, QPCT, XIST
## Negative: ACTB, CALR, SLC35F1, TIMP3, LINC01091, TUBA1B, FBP1, ACTG1, IL1RN, HSP90B1
## GSN, INSIG1, MGLL, LPL, GLIPR1, GCLC, MADD, PDIA4, ALCAM, PLEK
## MANF, RGCC, CSF1, DHCR24, LDHA, GADD45G, TMEM176B, HLA-DRB1, DUSP2, TNS3
## PC_ 4
## Positive: HIV-Gagp17, HIV-BaLEnv, HIV-Polprot, HIV-Polp15p31, HIV-LTRU5, HIV-Vif, HIV-Nef, HIV-TatEx1, HIV-LTRR, HIV-Gagp1Pol
## HIV-Gagp2p7, HIV-TatEx2Rev, HIV-Vpu, HIV-EGFP, MARCKS, CCL3, HIV-EnvStart, TPM4, SNCA, UGCG
## HIV-Vpr, G0S2, CD36, LGMN, HES4, B4GALT5, TNFRSF9, CLEC4A, BCAT1, SDS
## Negative: PTGDS, CLU, NCAPH, BX664727.3, LINC02244, SYNGR1, COX5B, RCBTB2, MT-ATP6, CRABP2
## AL136317.2, RARRES1, SSBP3, LINC01010, ADRA2B, AC015660.2, MT-ND2, S100A4, CRIP1, MT-CYB
## LY86-AS1, RNASE6, MT-CO3, S100A8, HLA-C, VAMP5, CCL22, CPE, CSRP2, TMEM176B
## PC_ 5
## Positive: TYMS, BIRC5, MKI67, PCLAF, CEP55, CENPF, CENPM, TK1, PRC1, CDKN3
## DIAPH3, CDK1, MYBL2, SHCBP1, NUSAP1, DLGAP5, RRM2, CENPK, HMMR, ASPM
## TPX2, NCAPG, CCNA2, MAD2L1, TOP2A, CIT, KIF4A, CLSPN, KIF11, PTTG1
## Negative: RGCC, TMEM176B, CRABP2, IGSF6, GCLC, TIMP3, IFI30, AC005280.2, GSN, CCND1
## NUMB, TNFSF14, PLEK, BCL2A1, NCAPH, KCNJ1, GPAT3, MGLL, AC015660.2, MREG
## PTGDS, RPS4Y1, RASSF4, TMEM117, CFD, CHI3L1, HLA-DRB1, DUSP2, ACTB, AC067751.1DimHeatmap(mono, dims = 1:2, cells = 500, balanced = TRUE)
DimHeatmap(mono, dims = 3:4, cells = 500, balanced = TRUE)
ElbowPlot(mono)
mono <- FindNeighbors(mono, dims = 1:4)## Computing nearest neighbor graph
## Computing SNNmono <- FindClusters(mono, algorithm = 3, resolution = 0.3, verbose = FALSE)
mono <- RunUMAP(mono, dims = 1:4)## 14:07:21 UMAP embedding parameters a = 0.9922 b = 1.112
## 14:07:21 Read 9669 rows and found 4 numeric columns
## 14:07:21 Using Annoy for neighbor search, n_neighbors = 30
## 14:07:21 Building Annoy index with metric = cosine, n_trees = 50
## 0% 10 20 30 40 50 60 70 80 90 100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 14:07:22 Writing NN index file to temp file /tmp/RtmpRdHgON/file11d0d46217c49b
## 14:07:22 Searching Annoy index using 1 thread, search_k = 3000
## 14:07:25 Annoy recall = 100%
## 14:07:26 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 14:07:28 Initializing from normalized Laplacian + noise (using RSpectra)
## 14:07:29 Commencing optimization for 500 epochs, with 332446 positive edges
## 14:07:29 Using rng type: pcg
## 14:07:35 Optimization finishedDimPlot(mono, reduction = "umap", label=TRUE)
DimPlot(mono, group.by="monaco_fine_annotation" , reduction = "umap", label=TRUE)
DimPlot(mono, group.by="sample" , reduction = "umap", label=TRUE)
Make Alv object
alvlist <- mylist[grep("alv",names(mylist))]
comb1 <- do.call(cbind,alvlist)
sce1 <- SingleCellExperiment(list(counts=comb1))
sce1## class: SingleCellExperiment
## dim: 36622 11212
## metadata(0):
## assays(1): counts
## rownames(36622): HIV_LTRR HIV_LTRU5 ... AC007325.4 AC007325.2
## rowData names(0):
## colnames(11212): alv_mock1|AAACCCAGTGCTGCAC alv_mock1|AAAGGATAGCATGAAT
## ... alv_bystander3|TTTGGTTCAGGTTCCG alv_bystander3|TTTGTTGTCGCGTTTC
## colData names(0):## Found more than one class "package_version" in cache; using the first, from namespace 'SeuratObject'## Also defined by 'alabaster.base'## reducedDimNames(0):
## mainExpName: NULL## Found more than one class "package_version" in cache; using the first, from namespace 'SeuratObject'
## Also defined by 'alabaster.base'## altExpNames(0):cellmetadata1 <- data.frame(colnames(comb1) ,sapply(strsplit(colnames(comb1),"\\|"),"[[",1))
colnames(cellmetadata1) <- c("cell","sample")
comb1 <- CreateSeuratObject(comb1, project = "mac", assay = "RNA", meta.data = cellmetadata1)## Warning: Feature names cannot have underscores ('_'), replacing with dashes
## ('-')comb1 <- NormalizeData(comb1)## Normalizing layer: countscomb1 <- FindVariableFeatures(comb1, selection.method = "vst", nfeatures = 2000)## Finding variable features for layer countscomb1 <- ScaleData(comb1)## Centering and scaling data matrixcomb1 <- RunPCA(comb1, features = VariableFeatures(object = comb1))## PC_ 1
## Positive: S100A6, GAPDH, LGALS1, DBI, MIF, LGALS3, PRDX6, PSME2, CSTB, GSTO1
## LINC02244, PTGDS, CALM3, CYSTM1, ELOC, TXN, TMEM176B, GSTP1, CLU, MGST1
## CRIP1, MMP9, CHI3L1, SYNGR1, FAH, H2AFZ, ACTB, TMEM176A, TUBA1A, LDHA
## Negative: DOCK3, ARL15, MALAT1, RASAL2, LRMDA, TMEM117, DPYD, PLXDC2, EXOC4, ASAP1
## FTX, ATG7, NEAT1, MITF, TPRG1, JMJD1C, VPS13B, FHIT, ELMO1, UBE2E2
## MAML2, ZNF438, ZFAND3, FMNL2, FRMD4B, LPP, COP1, TRIO, ZEB2, DENND4C
## PC_ 2
## Positive: HLA-DPA1, HLA-DRA, CD74, HLA-DPB1, LYZ, AIF1, MRC1, HLA-DRB1, TGFBI, CTSC
## C1QA, VAMP5, RCBTB2, SAMSN1, HMOX1, FOS, CLEC7A, SLCO2B1, FCGR2A, C1QC
## FGL2, SPRED1, SLC8A1, RBPJ, SELENOP, PDGFC, CLEC4A, ME1, FCGR3A, CD14
## Negative: TM4SF19, GAL, CCL22, CYSTM1, ATP6V1D, GM2A, CD164, FDX1, SCD, ACAT2
## CSTB, TGM2, CIR1, IARS, TCTEX1D2, RHOF, BCAT1, CYTOR, NCAPH, EPB41L1
## DCSTAMP, SLC20A1, GOLGA7B, LGALS1, CSF1, SNHG32, ADCY3, DUSP13, NRIP3, MREG
## PC_ 3
## Positive: PTGDS, TMEM176B, LINC02244, CLU, LINC01800, RGS20, LGALS3, TMEM176A, MGST1, KCNMA1
## SERTAD2, NCAPH, CRIP1, AC067751.1, SYNGR1, GPC4, GCLC, C2orf92, NOS1AP, TRIM54
## S100A6, LINC01010, FCMR, SLC35F1, LY86-AS1, NCF1, FGL2, ST5, NRCAM, CT69
## Negative: CTSZ, SLC11A1, MS4A7, AIF1, MRC1, FCER1G, CTSB, LGMN, ID3, MSR1
## FCGR3A, TPM4, CLEC7A, FPR3, C1QA, CTSC, CAMK1, CTSL, HLA-DRB5, CCL3
## S100A9, C1QC, HAMP, CSTB, HLA-DQA1, HLA-DQB1, MARCO, MARCKS, SLA, PLAU
## PC_ 4
## Positive: TYMS, PCLAF, CLSPN, TK1, DIAPH3, MYBL2, RRM2, ESCO2, CENPM, MKI67
## FAM111B, TCF19, SHCBP1, CDK1, HELLS, CEP55, CENPK, BIRC5, CENPU, ATAD2
## DTL, KIF11, NCAPG, NUSAP1, MCM10, TOP2A, PRC1, GINS2, ANLN, TPX2
## Negative: GCHFR, XIST, HLA-DRB5, GPX3, SAT1, SLC11A1, MS4A7, MSR1, QPCT, AC020656.1
## GPRIN3, MARCO, NMB, PAX8-AS1, FRMD4A, ST6GAL1, AL035446.2, FDX1, SERINC2, CTSZ
## S100A9, STX4, FUCA1, RARRES1, SASH1, AC008591.1, LINC01500, CCDC26, GM2A, C22orf34
## PC_ 5
## Positive: AC020656.1, NIPAL2, LINC02244, GCHFR, RARRES1, TDRD3, BX664727.3, FDX1, XIST, AL136317.2
## LINC01010, TDRD9, OSBP2, QPCT, GJB2, CFD, LYZ, S100A9, GAPLINC, TMTC1
## PKD1L1, PRSS21, SLC6A16, CCDC26, GM2A, HES2, CTSK, PLEKHA5, HLA-DRB5, ANO5
## Negative: HIV-Gagp17, HIV-BaLEnv, HIV-LTRU5, HIV-TatEx1, HIV-Polprot, MIF, HIV-Nef, HIV-LTRR, HIV-Polp15p31, HIV-Vif
## HIV-Gagp1Pol, IL1RN, PLEK, HIV-EnvStart, HIV-TatEx2Rev, HIV-Vpu, HIV-Gagp2p7, SLC35F1, ACTB, HIV-Vpr
## TMEM176A, CYTOR, ACTG1, HIV-EGFP, PSME2, TUBA1A, CTSB, MARCKS, MYL9, PHLDA1comb1 <- RunHarmony(comb1,"sample")## Transposing data matrix## Initializing state using k-means centroids initialization## Harmony 1/10## Harmony 2/10## Harmony 3/10## Harmony 4/10## Harmony converged after 4 iterationsDimHeatmap(comb1, dims = 1:6, cells = 500, balanced = TRUE)
ElbowPlot(comb1)
comb1 <- JackStraw(comb1, num.replicate = 100)
comb1 <- FindNeighbors(comb1, dims = 1:10)## Computing nearest neighbor graph## Computing SNNcomb1 <- FindClusters(comb1, resolution = 0.5)## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 11212
## Number of edges: 344775
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8624
## Number of communities: 10
## Elapsed time: 1 secondscomb1 <- RunUMAP(comb1, dims = 1:10)## 14:11:24 UMAP embedding parameters a = 0.9922 b = 1.112## 14:11:24 Read 11212 rows and found 10 numeric columns## 14:11:24 Using Annoy for neighbor search, n_neighbors = 30## 14:11:24 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 14:11:25 Writing NN index file to temp file /tmp/RtmpRdHgON/file11d0d472ae0258
## 14:11:25 Searching Annoy index using 1 thread, search_k = 3000
## 14:11:29 Annoy recall = 100%
## 14:11:30 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 14:11:32 Initializing from normalized Laplacian + noise (using RSpectra)
## 14:11:33 Commencing optimization for 200 epochs, with 450140 positive edges
## 14:11:33 Using rng type: pcg
## 14:11:37 Optimization finishedDimPlot(comb1, reduction = "umap")
message("macrophage markers")## macrophage markersFeaturePlot(comb1, features = c("ADGRE1", "CCR2", "SIGLEC1", "CX3CR1", "MRC1", "CD163", "LYVE1", "CD9", "TREM2"))
DimPlot(comb1, reduction = "umap", label = TRUE, pt.size = 0.5) + NoLegend()
ref <- celldex::MonacoImmuneData()
DefaultAssay(comb1) <- "RNA"
comb21 <- as.SingleCellExperiment(comb1)## Found more than one class "package_version" in cache; using the first, from namespace 'SeuratObject'
## Also defined by 'alabaster.base'
## Found more than one class "package_version" in cache; using the first, from namespace 'SeuratObject'
## Also defined by 'alabaster.base'
## Found more than one class "package_version" in cache; using the first, from namespace 'SeuratObject'
## Also defined by 'alabaster.base'
## Found more than one class "package_version" in cache; using the first, from namespace 'SeuratObject'
## Also defined by 'alabaster.base'
## Found more than one class "package_version" in cache; using the first, from namespace 'SeuratObject'
## Also defined by 'alabaster.base'
## Found more than one class "package_version" in cache; using the first, from namespace 'SeuratObject'
## Also defined by 'alabaster.base'lc1 <- logcounts(comb21)
pred_imm_broad1 <- SingleR(test=comb21, ref=ref,labels=ref$label.main)
head(pred_imm_broad1)## DataFrame with 6 rows and 4 columns
## scores labels
## <matrix> <character>
## alv_mock1|AAACCCAGTGCTGCAC 0.273560:0.272344:0.161242:... Monocytes
## alv_mock1|AAAGGATAGCATGAAT 0.318711:0.307377:0.191663:... Monocytes
## alv_mock1|AAAGGATAGTCAGGGT 0.294485:0.275673:0.182914:... Monocytes
## alv_mock1|AAAGGATAGTTCCGGC 0.294678:0.294725:0.183945:... Monocytes
## alv_mock1|AAAGGATTCACCATCC 0.278966:0.268607:0.190627:... Monocytes
## alv_mock1|AAAGGGCCATGACGTT 0.284776:0.293739:0.171230:... Monocytes
## delta.next pruned.labels
## <numeric> <character>
## alv_mock1|AAACCCAGTGCTGCAC 0.134488 Monocytes
## alv_mock1|AAAGGATAGCATGAAT 0.104645 Monocytes
## alv_mock1|AAAGGATAGTCAGGGT 0.140018 Monocytes
## alv_mock1|AAAGGATAGTTCCGGC 0.128407 Monocytes
## alv_mock1|AAAGGATTCACCATCC 0.147810 Monocytes
## alv_mock1|AAAGGGCCATGACGTT 0.166792 Monocytestable(pred_imm_broad1$pruned.labels)##
## Dendritic cells Monocytes
## 12 11014cellmetadata1$label <- pred_imm_broad1$pruned.labels
pred_imm_fine1 <- SingleR(test=comb21, ref=ref, labels=ref$label.fine)
head(pred_imm_fine1)## DataFrame with 6 rows and 4 columns
## scores labels
## <matrix> <character>
## alv_mock1|AAACCCAGTGCTGCAC 0.163017:0.398232:0.176698:... Classical monocytes
## alv_mock1|AAAGGATAGCATGAAT 0.180234:0.435146:0.208731:... Classical monocytes
## alv_mock1|AAAGGATAGTCAGGGT 0.169967:0.389207:0.187751:... Classical monocytes
## alv_mock1|AAAGGATAGTTCCGGC 0.166462:0.422480:0.189466:... Classical monocytes
## alv_mock1|AAAGGATTCACCATCC 0.184520:0.383707:0.203877:... Classical monocytes
## alv_mock1|AAAGGGCCATGACGTT 0.173873:0.439659:0.198357:... Classical monocytes
## delta.next pruned.labels
## <numeric> <character>
## alv_mock1|AAACCCAGTGCTGCAC 0.1433756 Classical monocytes
## alv_mock1|AAAGGATAGCATGAAT 0.1213924 Classical monocytes
## alv_mock1|AAAGGATAGTCAGGGT 0.0502055 Classical monocytes
## alv_mock1|AAAGGATAGTTCCGGC 0.0994518 Classical monocytes
## alv_mock1|AAAGGATTCACCATCC 0.0283404 Classical monocytes
## alv_mock1|AAAGGGCCATGACGTT 0.0687853 Classical monocytestable(pred_imm_fine1$pruned.labels)##
## Classical monocytes Intermediate monocytes Myeloid dendritic cells
## 9702 1332 25
## Non classical monocytes
## 3cellmetadata1$finelabel <- pred_imm_fine1$pruned.labels
col_pal <- c('#e31a1c', '#ff7f00', "#999900", '#cc00ff', '#1f78b4', '#fdbf6f',
'#33a02c', '#fb9a99', "#a6cee3", "#cc6699", "#b2df8a", "#99004d", "#66ff99",
"#669999", "#006600", "#9966ff", "#cc9900", "#e6ccff", "#3399ff", "#ff66cc",
"#ffcc66", "#003399")
annot_df1 <- data.frame(
barcodes = rownames(pred_imm_broad1),
monaco_broad_annotation = pred_imm_broad1$labels,
monaco_broad_pruned_labels = pred_imm_broad1$pruned.labels,
monaco_fine_annotation = pred_imm_fine1$labels,
monaco_fine_pruned_labels = pred_imm_fine1$pruned.labels)
meta_inf1 <- comb1@meta.data
meta_inf1$cell_barcode <- colnames(comb1)
meta_inf1 <- meta_inf1 %>% dplyr::left_join(y = annot_df1, by = c("cell_barcode" = "barcodes"))
rownames(meta_inf1) <- colnames(lc1)
comb1@meta.data <- meta_inf1
DimPlot(comb1, label=TRUE, group.by = "monaco_broad_annotation", reduction = "umap",
cols = col_pal, pt.size = 0.5) + ggtitle("Annotation With the Monaco Reference Database")
DimPlot(comb1, label=TRUE, group.by = "monaco_fine_annotation", reduction = "umap",
cols = col_pal, pt.size = 0.5) + ggtitle("Annotation With the Monaco Reference Database")
message("extract mono")## extract monomono <- comb1[,which(meta_inf1$monaco_broad_annotation == "Monocytes")]
mono_metainf1 <- meta_inf1[which(meta_inf1$monaco_broad_annotation == "Monocytes"),]
mono_metainf1 <- mono_metainf1[grep("monocytes",mono_metainf1$monaco_fine_pruned_labels),]
mono <- mono[,which(colnames(mono) %in% rownames(mono_metainf1))]
mono <- FindVariableFeatures(mono, selection.method = "vst", nfeatures = 2000)## Finding variable features for layer countsmono <- RunPCA(mono, features = VariableFeatures(object = mono))## Warning in PrepDR5(object = object, features = features, layer = layer, : The
## following features were not available: AC074099.1, MBOAT4, CYP1B1, AC022035.1,
## MS4A4A, IFI30, PSMA7, ZCCHC7, CPEB2-DT, ZNF609, CEP85L, AC023194.3, GLUL,
## LINC01951, LINC02643, DIAPH3-AS1, F2RL1, AC108860.2, DNAI1, HULC, AL135818.3,
## KIF16B, TESK2, HCAR3, LIMCH1, PGBD5, DOP1B, YJEFN3, LINC02732.## PC_ 1
## Positive: S100A6, GAPDH, LGALS1, DBI, MIF, LGALS3, PSME2, PRDX6, CSTB, GSTO1
## LINC02244, PTGDS, CYSTM1, ELOC, CALM3, TXN, GSTP1, TMEM176B, MMP9, CRIP1
## MGST1, CLU, CHI3L1, H2AFZ, FAH, TUBA1A, LDHA, TMEM176A, SYNGR1, S100A4
## Negative: DOCK3, ARL15, MALAT1, RASAL2, LRMDA, TMEM117, DPYD, PLXDC2, FTX, EXOC4
## ASAP1, TPRG1, ATG7, MITF, NEAT1, JMJD1C, VPS13B, FHIT, ELMO1, MAML2
## UBE2E2, ZNF438, COP1, FMNL2, LPP, ZFAND3, TRIO, FRMD4B, ZEB2, MED13L
## PC_ 2
## Positive: TM4SF19, CCL22, GAL, CYSTM1, ATP6V1D, GM2A, CD164, FDX1, SCD, ACAT2
## CSTB, TGM2, IARS, CIR1, TCTEX1D2, RHOF, BCAT1, CYTOR, NCAPH, EPB41L1
## DCSTAMP, SLC20A1, GOLGA7B, CSF1, LGALS1, ADCY3, SNHG32, DUSP13, NRIP3, MREG
## Negative: HLA-DPA1, HLA-DRA, CD74, HLA-DPB1, AIF1, LYZ, HLA-DRB1, MRC1, CTSC, TGFBI
## VAMP5, C1QA, RCBTB2, SAMSN1, HMOX1, FOS, CLEC7A, SLCO2B1, FCGR2A, C1QC
## FGL2, SPRED1, SLC8A1, SELENOP, RBPJ, PDGFC, CLEC4A, ME1, FCGR3A, CD14
## PC_ 3
## Positive: PTGDS, TMEM176B, LINC02244, CLU, LINC01800, LGALS3, TMEM176A, RGS20, MGST1, KCNMA1
## CRIP1, NCAPH, SERTAD2, SYNGR1, AC067751.1, GPC4, GCLC, TRIM54, C2orf92, NOS1AP
## S100A6, FCMR, SLC35F1, LINC01010, NCF1, LY86-AS1, FGL2, ST5, PLEK, MX1
## Negative: CTSZ, SLC11A1, MS4A7, AIF1, MRC1, FCER1G, LGMN, CTSB, MSR1, FCGR3A
## ID3, TPM4, CLEC7A, FPR3, CAMK1, C1QA, CTSC, HLA-DRB5, CTSL, CCL3
## S100A9, HAMP, C1QC, CSTB, HLA-DQA1, MARCO, HLA-DQB1, FMN1, SLA, MARCKS
## PC_ 4
## Positive: GCHFR, XIST, SAT1, GPX3, HLA-DRB5, QPCT, MS4A7, SLC11A1, AC020656.1, MSR1
## GPRIN3, NMB, MARCO, PAX8-AS1, FRMD4A, ST6GAL1, FDX1, AL035446.2, SERINC2, CTSZ
## FUCA1, S100A9, STX4, RARRES1, SASH1, AC008591.1, LINC01500, CCDC26, GM2A, CFD
## Negative: TYMS, PCLAF, CLSPN, TK1, MYBL2, DIAPH3, RRM2, ESCO2, CENPM, FAM111B
## MKI67, TCF19, SHCBP1, HELLS, CDK1, CENPU, CEP55, CENPK, DTL, BIRC5
## ATAD2, NCAPG, KIF11, MCM10, GINS2, NUSAP1, TOP2A, PRC1, TPX2, ANLN
## PC_ 5
## Positive: AC020656.1, NIPAL2, LINC02244, GCHFR, RARRES1, TDRD3, BX664727.3, XIST, FDX1, AL136317.2
## LINC01010, GJB2, CFD, QPCT, OSBP2, TDRD9, LYZ, S100A9, GAPLINC, TMTC1
## PRSS21, CTSK, SLC6A16, PKD1L1, GM2A, HES2, CCDC26, PLEKHA5, HLA-DRB5, ANO5
## Negative: HIV-Gagp17, HIV-LTRU5, HIV-BaLEnv, HIV-TatEx1, HIV-Polprot, MIF, HIV-Nef, HIV-LTRR, HIV-Polp15p31, HIV-Vif
## HIV-Gagp1Pol, IL1RN, PLEK, HIV-EnvStart, HIV-Vpu, HIV-TatEx2Rev, HIV-Gagp2p7, ACTB, SLC35F1, HIV-Vpr
## CYTOR, ACTG1, TMEM176A, HIV-EGFP, PSME2, CTSB, MARCKS, TUBA1A, MYL9, PHLDA1DimHeatmap(mono, dims = 1:2, cells = 500, balanced = TRUE)
DimHeatmap(mono, dims = 3:4, cells = 500, balanced = TRUE)
ElbowPlot(mono)
mono <- FindNeighbors(mono, dims = 1:4)## Computing nearest neighbor graph
## Computing SNNmono <- FindClusters(mono, algorithm = 3, resolution = 0.3, verbose = FALSE)
mono <- RunUMAP(mono, dims = 1:4)## 14:12:24 UMAP embedding parameters a = 0.9922 b = 1.112
## 14:12:24 Read 11036 rows and found 4 numeric columns
## 14:12:24 Using Annoy for neighbor search, n_neighbors = 30
## 14:12:24 Building Annoy index with metric = cosine, n_trees = 50
## 0% 10 20 30 40 50 60 70 80 90 100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 14:12:25 Writing NN index file to temp file /tmp/RtmpRdHgON/file11d0d470aad126
## 14:12:25 Searching Annoy index using 1 thread, search_k = 3000
## 14:12:29 Annoy recall = 100%
## 14:12:31 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 14:12:33 Initializing from normalized Laplacian + noise (using RSpectra)
## 14:12:33 Commencing optimization for 200 epochs, with 374004 positive edges
## 14:12:33 Using rng type: pcg
## 14:12:37 Optimization finishedDimPlot(mono, reduction = "umap", label=TRUE)
DimPlot(mono, group.by="monaco_fine_annotation" , reduction = "umap", label=TRUE)
DimPlot(mono, group.by="sample" , reduction = "umap", label=TRUE)
Extract monocytes MDM only
mono <- comb[,which(meta_inf$monaco_broad_annotation == "Monocytes")]
mono_metainf <- meta_inf[which(meta_inf$monaco_broad_annotation == "Monocytes"),]
mono_metainf1 <- mono_metainf[grep("monocytes",mono_metainf$monaco_fine_pruned_labels),]
mono <- mono[,which(colnames(mono) %in% rownames(mono_metainf))]
mono <- FindVariableFeatures(mono, selection.method = "vst", nfeatures = 2000)## Finding variable features for layer countsmono <- RunPCA(mono, features = VariableFeatures(object = mono))## Warning in PrepDR5(object = object, features = features, layer = layer, : The
## following features were not available: ADGRF1, AP000812.1, ACTB, AC005740.5,
## AC138123.1, HIF1A-AS3, PRH1, AL592494.2.## PC_ 1
## Positive: GAPDH, FABP3, TXN, IFI30, S100A10, PRDX6, TUBA1B, BLVRB, OTOA, S100A9
## FAH, C15orf48, GCHFR, CYSTM1, CARD16, GSTP1, HAMP, PSMA7, CTSB, CSTA
## ACTG1, FABP4, H2AFZ, LDHB, LINC01827, CFD, TUBA1A, MMP9, SELENOW, LINC02244
## Negative: ARL15, DOCK3, FTX, NEAT1, EXOC4, MALAT1, DPYD, LRMDA, RASAL2, JMJD1C
## TMEM117, PLXDC2, VPS13B, FHIT, TPRG1, TRIO, ATG7, ZNF438, MAML2, ZFAND3
## MITF, COP1, ZEB2, ELMO1, MED13L, DENND4C, TCF12, ERC1, JARID2, FMNL2
## PC_ 2
## Positive: HLA-DRB1, CD74, HLA-DRA, HLA-DPA1, GCLC, HLA-DPB1, LYZ, RCBTB2, MRC1, KCNMA1
## SPRED1, C1QA, FGL2, AC020656.1, SLCO2B1, CYP1B1, AIF1, HLA-DRB5, PTGDS, S100A4
## VAMP5, LINC02345, CA2, CRIP1, CAMK1, ALOX5AP, RTN1, HLA-DQB1, MX1, TGFBI
## Negative: CYSTM1, CD164, PSAT1, FAH, FDX1, GDF15, ATP6V1D, BCAT1, SAT1, CCPG1
## PHGDH, PSMA7, HEBP2, SLAMF9, RETREG1, GARS, HES2, TCEA1, TXN, RHOQ
## RILPL2, B4GALT5, CLGN, NUPR1, CSTA, SPTBN1, HSD17B12, STMN1, SNHG5, PTER
## PC_ 3
## Positive: MARCKS, CD14, BTG1, MS4A6A, TGFBI, CTSC, FPR3, HLA-DQA1, AIF1, MPEG1
## MEF2C, CD163, IFI30, TIMP1, HLA-DPB1, ALDH2, SELENOP, NUPR1, NAMPT, HLA-DQB1
## HIF1A, C1QC, MS4A7, FUCA1, EPB41L3, HLA-DQA2, RNASE1, ARL4C, ZNF331, TCF4
## Negative: NCAPH, CRABP2, RGCC, CHI3L1, TM4SF19, DUSP2, GAL, AC015660.2, CCL22, ACAT2
## LINC01010, TMEM114, MGST1, RGS20, TRIM54, LINC02244, MREG, NUMB, TCTEX1D2, GPC4
## CCND1, POLE4, SYNGR1, SLC20A1, SERTAD2, IL1RN, GCLC, CLU, PLEK, AC092353.2
## PC_ 4
## Positive: ACTG1, TPM4, CCL3, CTSB, TUBA1B, CSF1, DHCR24, CYTOR, LGMN, INSIG1
## GAPDH, TUBB, CD36, HAMP, CCL7, C1QA, AIF1, MGLL, TYMS, LIMA1
## C1QC, PCLAF, CCL2, HSP90B1, CLSPN, C1QB, TNFSF13, TK1, C15orf48, CAMK1
## Negative: PTGDS, LINC02244, CLU, CSTA, CCPG1, MGST1, SYNGR1, LINC01010, EPHB1, ALDH2
## AC015660.2, LY86-AS1, GAS5, NCF1, BX664727.3, S100P, TMEM91, SNHG5, CLEC12A, AP000331.1
## APOD, PDE4D, C1QTNF4, VAMP5, LYZ, CFD, RCBTB2, DIXDC1, AC073359.2, ARHGAP15
## PC_ 5
## Positive: TYMS, PCLAF, TK1, MKI67, MYBL2, RRM2, CENPM, BIRC5, CEP55, CLSPN
## CDK1, DIAPH3, SHCBP1, NUSAP1, CENPF, CENPK, PRC1, TOP2A, NCAPG, ESCO2
## KIF11, ANLN, CCNA2, TPX2, ASPM, FAM111B, MAD2L1, RAD51AP1, GTSE1, HMMR
## Negative: HIV-BaLEnv, HIV-LTRU5, HIV-Polprot, HIV-Gagp17, HIV-Nef, HIV-TatEx1, HIV-Polp15p31, HIV-LTRR, HIV-Vif, HIV-Gagp1Pol
## HIV-TatEx2Rev, HIV-Gagp2p7, HIV-EnvStart, HIV-Vpu, HIV-Vpr, HIV-EGFP, CTSB, MMP19, IL6R-AS1, CSF1
## CCL3, MGLL, IL1RN, INSIG1, AL157912.1, SDS, LGMN, TCTEX1D2, TNFRSF9, PHLDA1DimHeatmap(mono, dims = 1:2, cells = 500, balanced = TRUE)
DimHeatmap(mono, dims = 3:4, cells = 500, balanced = TRUE)
ElbowPlot(mono)
mono <- FindNeighbors(mono, dims = 1:4)## Computing nearest neighbor graph## Computing SNNmono <- FindClusters(mono, algorithm = 3, resolution = 0.3, verbose = FALSE)
mono <- RunUMAP(mono, dims = 1:4)## 14:13:28 UMAP embedding parameters a = 0.9922 b = 1.112## 14:13:28 Read 24224 rows and found 4 numeric columns## 14:13:28 Using Annoy for neighbor search, n_neighbors = 30## 14:13:28 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 14:13:30 Writing NN index file to temp file /tmp/RtmpRdHgON/file11d0d47c64da0b
## 14:13:30 Searching Annoy index using 1 thread, search_k = 3000
## 14:13:39 Annoy recall = 100%
## 14:13:41 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 14:13:43 Initializing from normalized Laplacian + noise (using RSpectra)
## 14:13:44 Commencing optimization for 200 epochs, with 793206 positive edges
## 14:13:44 Using rng type: pcg
## 14:13:51 Optimization finishedDimPlot(mono, reduction = "umap", label=TRUE)
DimPlot(mono, group.by="monaco_fine_annotation" , reduction = "umap", label=TRUE)
DimPlot(mono, group.by="sample" , reduction = "umap", label=TRUE)
Cell counts
Most cells are classified as monocytes.
cc <- table(meta_inf[,c("sample","monaco_broad_annotation")])
cc %>% kbl(caption="cell counts") %>% kable_paper("hover", full_width = F)| Basophils | Dendritic cells | Monocytes | |
|---|---|---|---|
| alv_active1 | 0 | 0 | 774 |
| alv_active2 | 0 | 3 | 543 |
| alv_active3 | 0 | 1 | 540 |
| alv_bystander1 | 0 | 1 | 2458 |
| alv_bystander2 | 0 | 0 | 1948 |
| alv_bystander3 | 0 | 1 | 1936 |
| alv_latent1 | 0 | 1 | 301 |
| alv_latent2 | 0 | 0 | 99 |
| alv_latent3 | 0 | 1 | 137 |
| alv_mock1 | 0 | 1 | 883 |
| alv_mock2 | 0 | 0 | 649 |
| alv_mock3 | 0 | 3 | 1031 |
| mdm_active1 | 0 | 3 | 599 |
| mdm_active2 | 0 | 0 | 414 |
| mdm_active3 | 0 | 2 | 305 |
| mdm_active4 | 0 | 0 | 401 |
| mdm_bystander1 | 0 | 12 | 1845 |
| mdm_bystander2 | 0 | 8 | 1957 |
| mdm_bystander3 | 0 | 19 | 490 |
| mdm_bystander4 | 0 | 1 | 1495 |
| mdm_latent1 | 1 | 11 | 160 |
| mdm_latent2 | 0 | 8 | 187 |
| mdm_latent3 | 0 | 3 | 72 |
| mdm_latent4 | 0 | 0 | 23 |
| mdm_mock1 | 0 | 1 | 691 |
| mdm_mock2 | 0 | 1 | 549 |
| mdm_mock3 | 0 | 2 | 135 |
| mdm_mock4 | 0 | 0 | 775 |
| react6 | 0 | 3 | 2827 |
tcc <- t(cc)
pctcc <- apply(tcc,2,function(x) { x/sum(x)*100} )
pctcc %>% kbl(caption="cell proportions") %>% kable_paper("hover", full_width = F)| alv_active1 | alv_active2 | alv_active3 | alv_bystander1 | alv_bystander2 | alv_bystander3 | alv_latent1 | alv_latent2 | alv_latent3 | alv_mock1 | alv_mock2 | alv_mock3 | mdm_active1 | mdm_active2 | mdm_active3 | mdm_active4 | mdm_bystander1 | mdm_bystander2 | mdm_bystander3 | mdm_bystander4 | mdm_latent1 | mdm_latent2 | mdm_latent3 | mdm_latent4 | mdm_mock1 | mdm_mock2 | mdm_mock3 | mdm_mock4 | react6 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Basophils | 0 | 0.0000000 | 0.0000000 | 0.0000000 | 0 | 0.0000000 | 0.0000000 | 0 | 0.0000000 | 0.0000000 | 0 | 0.0000000 | 0.0000000 | 0 | 0.0000000 | 0 | 0.0000000 | 0.0000000 | 0.000000 | 0.0000000 | 0.5813953 | 0.000000 | 0 | 0 | 0.0000000 | 0.0000000 | 0.000000 | 0 | 0.0000000 |
| Dendritic cells | 0 | 0.5494505 | 0.1848429 | 0.0406669 | 0 | 0.0516262 | 0.3311258 | 0 | 0.7246377 | 0.1131222 | 0 | 0.2901354 | 0.4983389 | 0 | 0.6514658 | 0 | 0.6462036 | 0.4071247 | 3.732809 | 0.0668449 | 6.3953488 | 4.102564 | 4 | 0 | 0.1445087 | 0.1818182 | 1.459854 | 0 | 0.1060071 |
| Monocytes | 100 | 99.4505495 | 99.8151571 | 99.9593331 | 100 | 99.9483738 | 99.6688742 | 100 | 99.2753623 | 99.8868778 | 100 | 99.7098646 | 99.5016611 | 100 | 99.3485342 | 100 | 99.3537964 | 99.5928753 | 96.267191 | 99.9331551 | 93.0232558 | 95.897436 | 96 | 100 | 99.8554913 | 99.8181818 | 98.540146 | 100 | 99.8939929 |
Focus on MOCK vs ACTIVE - exclude latent and bystander
focus <- meta_inf[grep("latent",rownames(meta_inf),invert=TRUE),]
focus <- focus[grep("bystander",rownames(focus),invert=TRUE),]
focus_mdm <- focus[grep("mdm",rownames(focus)),]
focus_alv <- focus[grep("alv",rownames(focus)),]
mono_focus_mdm <- mono[,which(colnames(mono) %in% rownames(focus_mdm))]
mono_focus_alv <- mono[,which(colnames(mono) %in% rownames(focus_alv))]
# mono_focus_mdm
mono_focus_mdm <- FindVariableFeatures(mono_focus_mdm, selection.method = "vst", nfeatures = 2000)## Finding variable features for layer countsmono_focus_mdm <- RunPCA(mono_focus_mdm, features = VariableFeatures(object = mono_focus_mdm))## Warning: The following 36 features requested have zero variance; running
## reduction without them: CCL3, PDE7B, NEAT1, KCNIP1, BX664727.3, ARL4C,
## BX284613.2, HIST1H3J, ANXA1, NCALD, SLC51A, CPEB2, PRMT9, MGST1, ABCG1, EPSTI1,
## PPP2R3A, PRKCA, CU638689.5, DIAPH2-AS1, RGS20, CST7, ARMC9, AC013799.1,
## AC137770.1, GRAMD1B, RRAS2, PPP1R12B, AC124017.1, ENTPD1, GTDC1, LINC00511,
## AC024901.1, AC006001.2, PRKG2, SCLT1## Warning in PrepDR5(object = object, features = features, layer = layer, : The
## following features were not available: HIST1H2BJ, ENG, OASL, CENPA, SSBP3,
## MT-ND5, CCND2, TVP23A, PCBP3, LINC00885, SCAI, AL009177.1, AC013287.1,
## AL355388.1, IFT80, PIWIL2, AC106793.1, AL358944.1, C16orf89, KLF12, AC108134.2,
## SETD2, C5AR2, PPP6R2, FANCM, TTK, HIF1A-AS3, TNRC6C, STXBP1, ZZEF1, HCAR2,
## GPR155, MARCH1, SLC2A14, RPS4Y1, IPCEF1, AC064805.2, PNPLA7, AL353759.1, KLRG1,
## PDIA4, TENM1, EIF2B3, LRRK2, TNFRSF4, RASIP1, ZBTB41, AGPAT5, LPL, RAP1GDS1,
## NUP93, LINC02853, CLPX, FAM118B, ESR2, MT-ATP6, SH3GL1, ATP6V0A2, CWC22, PSME2,
## PAWR, GYG2, AC034213.1, AL157911.1, AC007091.1, LINC02585, AC007364.1,
## AL357873.1, P2RY12, AC025262.1, NR2F1-AS1, ANKRD34B, PCA3, CLIC5, DUSP16, CREM,
## AL360015.1, IFT140, AURKC, CCNB2, ASTL, CHRNB4, PKIA-AS1, WWP1, SF3B3,
## TMEM220-AS1, DOK5, RAB10, LDHA, STAT5B, AC005740.5, MT-ND2, PARD3, LYPD1,
## AL355388.3, AP000462.1, AC072022.2, ZNF276, HLA-C, AC093766.1, BTBD2, FAM184A,
## IFI6, PDGFD, SRRM2-AS1, SIGLEC10, SRGN, SEMA6D, AC108879.1, MT-CO3, AC133550.2,
## MAP4K1, CPLX1, LINC01054, PACS1, OBI1-AS1, MAP3K15, ACTR3C, AL021917.1, LRRC9,
## RASSF4, TMEM212-AS1, AC105001.1, AL031658.1, CDK19, SF3B1, MT-CYB, AP000446.1,
## TTC21B-AS1, JAKMIP3, VGLL3, RGS18, CHRM3, DAPK2, AC099552.1, UBASH3B, ANK3,
## AC006427.2, AC079062.1, ACMSD, ZNF385D-AS1, CFAP52, SETBP1, MRC2, BCL2A1,
## ZFPM2-AS1, COX5B, MT-ND1, CTHRC1, AL592295.3, AC100849.2, TRIM71, CCDC7,
## AC078923.1, DPEP3, CARD11, AC240274.1, AC112236.3, ZDHHC17, IGSF6, AC089983.1,
## P2RY13, AC092490.1, MANF, GRM7, FDXACB1, NLRP4, APCDD1L, FHOD3, AC092811.1,
## MMP28, AC009315.1, AHR, KIF21A, CRISPLD2, TFG, AC007389.1, GNG4, RORA, ARC,
## SAMD11, GAS1RR, MIR155HG, DARS, CLEC4C, TRIM37, SLC44A5, AC114977.1,
## AC019117.1, CEP112, TUBB3, ADRA2B, TPTE2, STPG2, GTSF1, CORIN, CEP135,
## AL132775.1, SNAP25-AS1, AL049828.1, DNAH2, AL391807.1, PRRT2, TEX15, OR8D1,
## SOAT2, MCF2L-AS1, AC005393.1, SESN3, DHX8, VIPR1, NWD1, TNS3, GSN, OR3A2,
## HIST1H2AC, AKR7A2, ALDH1A1, TMEM45A, SRGAP3, AC084809.2, CPE, SNHG12,
## AC026333.3, AC073050.1, LINC01600, LCTL, AC019330.1, AC073569.1, ATP6V0D2,
## TACC3, GFRA2, MAPK8, LRRIQ4, LINC00649, RUFY2, CD93, ATF3, AC011365.1, GAL3ST4,
## Z99758.1, CREG2, U62317.4, SCAPER, LYPLAL1-DT, LNX1, AC096570.1, AC016074.2,
## ERO1A, AC009264.1, ENPEP, CNDP1, SIGLEC7, GLIPR1, OSBPL6, AL136418.1,
## HIST1H2BG, SULF2, MNAT1, AL589740.1, TUBB4B, PAX8-AS1, TASP1, ABCA13,
## LINC00237, AC020743.2, TSPAN8, AC015849.1, AL078602.1, CACNA2D3, LINC01917,
## LINC01855, Z94721.1, MAPK13, FKBP1B, AC021231.1, PRTG, GAK, RALGAPA2, RTKN2,
## AL445584.2, LINC02208, PODN, PDE11A, DPF1, ACTB, RPS6KA6, MCCC2, TFRC,
## AC005264.1, TMEM71, KDM6A, HIST2H2BE, EPHA6, COL8A2, LINC00958, ZBTB46,
## AC011476.3, SCFD2, GLRX, C11orf45, MLLT3, RAP2C-AS1, LGALSL, LINC01353, SOX15,
## KIAA0825, CD72, NPRL3, LINC01891, AL645929.2, MMD2, LINC02449, MAP1A, OXSR1,
## AC104459.1, SLC26A7, ZHX2, AC021086.1, SPAG7, CALR, AC090630.1, HERPUD1, ITGA4,
## AC087683.3, AC114763.1, ZFP36L1, SPEG, LINC01191, RFX3, RNF24, STAG1,
## AC117473.1, MGAT5, TUBB4A, MRVI1, CKMT1A, AC073475.1, LY96, SPTLC3, KIAA1841,
## AL031710.2, NYX, LGALS3, PTPRG-AS1, CHM, TMEM131L, MCM9, JAKMIP2-AS1, BACH1,
## AP000829.1, CFAP69, CLDN4, RHCE, PRSS3, AC010834.2, AL356379.2, MDH1, HCAR3,
## ADORA2B, MARCH3, LINC02015, CALM3, PROCR, LILRB2, IGF2R, SOCS3, NETO2,
## TMEM72-AS1, RNF180, E2F1, TNIK, P3H2, AC008115.2, SPIB, PLBD1, AF131216.1,
## AC103726.2, NALCN, AC008555.2, AC008655.2, AL672032.1, AP002793.1, AL110292.1,
## AC098588.3, UBE2T, PIP4K2C, PCLO, AC097654.1, MKX, AL121772.1, AC020637.1,
## LINC00571, LINC02112, KCNJ1, AL136298.1, TRAF2, BMPR1B, AFF1, SPIRE1, PAFAH1B1,
## CASP1, LINC02851, CNIH3, EXD2, PTPRB, SNX10, ITPR2, KIF20B, AC129803.1, NFKB1,
## BUB1, SOS1, ADAMTS10, PLXNC1, SGO1, BFSP2, AL365295.2, AHCYL2, PKN2, ZC4H2,
## HSP90AA1, FAM135A, XKR9, CDT1, PRORP, LINC01762, ITM2A, NEGR1, ANGPTL4,
## AC079584.1, LINC00987, SLC7A8, PFKFB3, LPP, STUM, FAM110B, QKI, FILIP1L, DYM,
## MYADM, ADIRF, TXNIP, ARRDC3-AS1, LINC02798, AL139246.1, AL137076.1, AL158839.1,
## AL589765.6, C4BPA, C1orf229, AC114810.1, AL133243.1, AC009229.3, AC012358.1,
## LINC01104, AC063944.3, AC092902.6, AC128709.2, AC021220.2, ADAMTS3, RBM46,
## AC116351.1, AC011374.3, LINC02533, LINC02571, Z84484.1, MRAP2, FUT9,
## AL357992.1, AL078582.1, AC002480.2, TRIM74, DEFB136, PRDM14, CALB1, AF178030.1,
## AF235103.1, CNTFR-AS1, AL353764.1, TMEM246-AS1, PPP3R2, AL356481.2, AL731559.1,
## AL121748.1, LINC02625, SYCE1, OR10A4, LINC02751, SAA4, AC013714.1, AC024940.1,
## AC012464.3, AC063943.1, C1QTNF9-AS1, SMAD9-IT1, LINC00563, AL161717.1,
## CLYBL-AS2, AL442125.2, KLHL33, CMA1, AL163953.1, AC104938.1, AC048382.1,
## AC091167.5, BAIAP3, AL133297.1, AC106820.2, NPIPB8, AC012186.2, AC092378.1,
## AC129507.2, RAI1-AS1, AC007952.6, AC004448.3, AC243773.2, KRT19, AC105094.2,
## OR7E24, ANGPTL8, LINC01858, AC022150.2, AL021396.1, LINC01747, CU634019.5,
## NUDT11, LINC00266-4P, PLA2G5, BDNF-AS, KCNA2, AC021546.1, LINC00407,
## AC246817.1, GNG7, DNAH3, GMDS, LINC02398, AL096794.1, AL390800.1, AC083836.1,
## NELL2, UFL1-AS1, PRH1, SUCLG2-AS1, ITGA9, GLUL, LINC01572, SPOCK3, POF1B, FAIM,
## SPATA5, TIMP4, CAMK2D, ELANE, PCNX2, ABCA1, TOM1L2, SLC41A2, HCRTR2, ST3GAL3,
## MCTP2, DUSP5, MARF1, COBL, PRDM1, CD200R1, AC092134.3, CPXM2, EDA, CASC2, PKP2,
## TTLL7, GPAT3, PLAA, PRAG1, DMXL2, AL009179.1, SH3GL2, AL158068.2, AC005736.1,
## CYP4F22, LINC01098, EFL1, RERE, CRELD2, ZNF609, STAU2, EMP1, AC024084.1, RHPN2,
## MPDZ, TYW1B, LINC02457, RAB7B.## PC_ 1
## Positive: PRDX6, TUBA1B, C15orf48, FABP3, S100A10, TUBA1A, TXN, S100A9, CRABP2, CHI3L1
## SLC35F1, RGCC, ACTG1, ACAT2, MMP9, FBP1, TUBA1C, GAL, HPGDS, LDHB
## BLVRB, HAMP, MYL9, SPP1, UCHL1, CCNA1, TMEM176B, LINC02244, TMEM114, TMEM176A
## Negative: FHIT, RAD51B, FMN1, ARL15, MALAT1, FTX, AL035446.2, MBD5, EXOC4, SLC22A15
## ZFAND3, SNTB1, FNDC3B, GMDS-DT, COP1, VTI1A, PDE4D, JMJD1C, DENND1A, VPS13B
## TRIO, DOCK4, SBF2, SMYD3, FRMD4B, DOCK3, COG5, TBC1D8, REV3L, ZBTB20
## PC_ 2
## Positive: HLA-DRB1, LYZ, PTGDS, CD74, HLA-DRA, HLA-DPA1, CFD, HLA-DPB1, KCNMA1, CEBPD
## RCBTB2, CYP1B1, GCLC, NCAPH, RNASE6, TGFBI, SIPA1L1, S100A4, CRIP1, HLA-DRB5
## MNDA, ATP8B4, ATG7, CA2, HLA-DQB1, ALOX5AP, MRC1, DUSP2, VAMP5, RAPGEF1
## Negative: HIV-Gagp17, HIV-Polp15p31, HIV-BaLEnv, HIV-Polprot, HIV-Vif, HIV-LTRU5, HIV-Gagp2p7, GDF15, PSAT1, CYSTM1
## HIV-Gagp1Pol, BEX2, HIV-EGFP, TCEA1, G0S2, HIV-TatEx2Rev, SNCA, PHGDH, HIV-Vpu, B4GALT5
## OCIAD2, SLAMF9, HIV-TatEx1, HIV-LTRR, SUPV3L1, UGCG, RAB38, GARS, S100A10, NMRK2
## PC_ 3
## Positive: BTG1, MARCKS, CD14, G0S2, TGFBI, FUCA1, MS4A6A, CLEC4E, SEMA4A, CXCR4
## VMO1, TIMP1, MEF2C, HIF1A, CEBPD, MS4A7, MAFB, P2RY8, RNASE1, FPR3
## TCF4, MPEG1, PDK4, CTSC, CD163, HLA-DQA2, HIV-Gagp17, ST8SIA4, SDS, SELENOP
## Negative: RGCC, NCAPH, CRABP2, CHI3L1, TM4SF19, LINC01010, MREG, LINC02244, GPC4, AC015660.2
## TMEM114, FNIP2, NUMB, ACAT2, PSD3, TCTEX1D2, LRCH1, SLC28A3, PLEK, ST3GAL6
## AC005280.2, ANO5, DOCK3, FBP1, AC092353.2, CSF1, ASAP1, GCLC, FDX1, TXNRD1
## PC_ 4
## Positive: HES2, CCPG1, LINC02244, NUPR1, CARD16, RAB6B, LINC01010, S100P, PSAT1, CPD
## PTGDS, CLGN, CLU, BEX2, NIBAN1, SYNGR1, PHGDH, TDRD3, PLEKHA5, QPCT
## NMB, RETREG1, PCDH19, C5orf17, GAS5, SUPV3L1, TCEA1, PDE4D, CLEC12A, SEL1L3
## Negative: AC078850.1, SLC35F1, INSIG1, TUBA1B, CCL7, ACTG1, LINC01091, CD36, FABP4, C3
## MGLL, TPM4, TIMP3, PHLDA1, CD300LB, CSF1, TMEM176A, CADM1, TUBB, ALCAM
## HSP90B1, IL1RN, TNFRSF9, LIMA1, TPM2, SDS, IL18BP, PLEK, IL4I1, MACC1
## PC_ 5
## Positive: PCLAF, STMN1, TYMS, CTSL, CENPF, MKI67, CEP55, CDKN3, BIRC5, KIF4A
## DLGAP5, PTTG1, RAD51AP1, CENPM, TK1, PRC1, PLEKHA5, CDK1, HMMR, CENPK
## CLSPN, CCNA2, NUSAP1, SHCBP1, TPX2, ASPM, BUB1B, TUBB, MYBL2, NCAPG
## Negative: HIV-Vif, HIV-Gagp2p7, HIV-Polp15p31, HIV-Polprot, HIV-Gagp17, HIV-Gagp1Pol, HIV-BaLEnv, HIV-LTRU5, HIV-TatEx1, HIV-Vpu
## HIV-TatEx2Rev, HIV-LTRR, HIV-EGFP, HIV-Nef, HIV-EnvStart, HIV-Vpr, CHI3L1, HES1, GCLC, PLCL1
## SPRED1, IL1RN, HES4, ISG15, MX1, CDKN1A, DUSP2, ST6GALNAC3, ADK, MDM2DimHeatmap(mono_focus_mdm, dims = 1:2, cells = 500, balanced = TRUE)
DimHeatmap(mono_focus_mdm, dims = 3:4, cells = 500, balanced = TRUE)
ElbowPlot(mono_focus_mdm)
mono_focus_mdm <- FindNeighbors(mono_focus_mdm, dims = 1:4)## Computing nearest neighbor graph## Computing SNNmono_focus_mdm <- FindClusters(mono_focus_mdm, algorithm = 3, resolution = 0.3, verbose = FALSE)
mono_focus_mdm <- RunUMAP(mono_focus_mdm, dims = 1:4)## 14:14:06 UMAP embedding parameters a = 0.9922 b = 1.112## 14:14:06 Read 3869 rows and found 4 numeric columns## 14:14:06 Using Annoy for neighbor search, n_neighbors = 30## 14:14:06 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 14:14:06 Writing NN index file to temp file /tmp/RtmpRdHgON/file11d0d4211d0110
## 14:14:06 Searching Annoy index using 1 thread, search_k = 3000
## 14:14:07 Annoy recall = 100%
## 14:14:09 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 14:14:11 Initializing from normalized Laplacian + noise (using RSpectra)
## 14:14:11 Commencing optimization for 500 epochs, with 135842 positive edges
## 14:14:11 Using rng type: pcg
## 14:14:14 Optimization finishedDimPlot(mono_focus_mdm, reduction = "umap", label=TRUE)
DimPlot(mono_focus_mdm, group.by="monaco_fine_annotation" , reduction = "umap", label=TRUE)
DimPlot(mono_focus_mdm, group.by="sample" , reduction = "umap", label=TRUE)
# mono_focus_alv
mono_focus_alv <- FindVariableFeatures(mono_focus_alv, selection.method = "vst", nfeatures = 2000)## Finding variable features for layer countsmono_focus_alv <- RunPCA(mono_focus_alv, features = VariableFeatures(object = mono_focus_alv))## Warning: The following 35 features requested have zero variance; running
## reduction without them: MT1X, HAMP, APOA1, MT2A, INHBA, ID3, ISM1, ANKRD66,
## PKD1L1, AL591135.2, LINC00607, TXN, AC087857.1, CABCOCO1, LINGO1, PPP1R17,
## LINC02073, AC093916.1, SAMSN1, AC009292.2, IGSF23, LINC00639, PRKCG, KRTAP10-4,
## SIK3, CHD9, MED13L, AC104041.1, FAM13B, PLAT, AC016152.1, AC069410.1,
## AC130650.2, LINC01276, NME5## Warning in PrepDR5(object = object, features = features, layer = layer, : The
## following features were not available: LINC00964, NUP210L, CPNE8, IFI6,
## AC083837.1, SMS, TMIGD1, HCAR2, AC011396.2, CD28, AP002075.1, SLC6A16, TEKT5,
## EMP3, MIF, AL096794.1, PPP1R1C, ACVR2A, LYPD1, EDIL3, AC022031.2, EFCAB7,
## LINC02068, IQCA1, SPINK6, TENM4, COL25A1, HIST1H2BG, C11orf49, SRL, AC092821.3,
## EXO1, IL6, DNAH12, DNAJC9, ACTN2, SLC7A14-AS1, AC135050.3, MRPS6, NPTX2,
## LINC02269, ERAP2, KIAA1755, GNLY, AC076968.2, AC026391.1, AC013391.2, INO80,
## ADAM22, LINC00589, DSG2, MS4A5, AC207130.1, PLAAT3, AC005740.5, BTNL8,
## AC084740.1, MSRB3, ARMC8, SMCHD1, CNTNAP2, BANK1, COL27A1, TEK, GCH1, SCFD1,
## CD226, GPRC5D, AHCTF1, PMAIP1, CTSW, DCSTAMP, TRIM2, TUBB4A, NWD1, LGALS1,
## GIGYF2, MARCKSL1, CH25H, BRMS1L, RAD51C, RFX3-AS1, AL353596.1, KIF6, SHCBP1L,
## LPCAT1, RLF, LINC00350, NEURL3, MIR4300HG, AC104596.1, IGF2R, LINC02789,
## AC019131.1, ADCY3, TMEM236, AL161646.1, CR1, SVEP1, USP10, ASAP3, PTPN2, ZC3H8,
## LINC01344, TNFAIP3, STK32B, YJEFN3, CTSZ, XDH, AC010834.2, CCDC85A, RFTN2,
## DYNC1H1, AC079313.2, MECP2, AC009435.1, LINC00299, SHANK2, AC005264.1,
## AC084809.2, SPOCK1, RPH3AL, ABCB1, LINC01862, SH3TC2, CCNC, NEK4, ZPLD1,
## LINC00511, NDRG1, SPACA3, URB1, PSME2, CFAP74, XPO5, CCDC57, PPEF1, CSTB,
## AC007785.1, HSF2BP, PTX3, MFSD11, POU6F2, SLC2A5, ABRAXAS2, AC063944.1, STS,
## FBXO4, NCMAP, PAX8-AS1, MBOAT4, ANGPT1, RXFP1, FCRLB, RAB7B, SMAD1-AS2,
## AL353595.1, AC125421.1, PHACTR1, LINC02466, SOX6, LDHA, AL672032.1, LINC00842,
## TEPP, EML2-AS1, ERI2, GAPLINC, GRID2, SYT12, TIMELESS, AKAP6, ANOS1, CKAP5,
## WDR90, GOLGA7B, HNRNPM, LGALS3, WIPF3, NRG1, MID2, AC007100.1, NBAT1, C2orf72,
## FSD1, GRHL2, GLIPR1, NRIP3, TMEM37, AC092957.1, SOAT2, HCAR3, LINC01414,
## AC011346.1, INKA2-AS1, FO393415.3, UPK1A-AS1, AC073569.2, AL513166.1, GTF2IRD2,
## IPCEF1, PMPCA, DHRS9, C22orf34, AC124852.1, RFX8, ELOC, PTPRB, HRH2, HMOX1,
## GATA2, SEMA6D, CLDN18, GNRHR, LMNB1-DT, COBL, SLC23A2, AC041005.1, ZNF385D-AS1,
## NNMT, AOC1, EAF2, AC107081.2, PDE1C, CLEC7A, SERINC2, CNR2, LCP1, IL17RB, RGS7,
## SPRY4-AS1, DACH1, AL592295.3, SVOP, STPG2, AL662884.3, AC022035.1, SLC44A5,
## AC006441.4, LINC00609, HLTF-AS1, KIAA0825, MOSPD1, SLC4A8, NDRG2, FAF1, PLCH1,
## RELN, LINC01299, MTFMT, SLC6A7, HIP1, WDFY4, KIF21A, NCR3, MAS1, LINC01999,
## AC007001.1, TNC, CLSTN2, PLXDC1, AC004949.1, CD5L, TNR, AL360015.1, LINGO2,
## TMEM132C, ZNF157, NEIL3, LINC01358, ADM, SFTPD-AS1, MIR3142HG, MRC2, MIR2052HG,
## HSPA1B, ULBP2, GLUL, AC068228.3, DZIP1, AC025430.1, TPSAB1, SLC12A3,
## AC092718.1, COL4A5, AC006460.1, VIPR1, SLC9A2, TDRD9, ABLIM1, ZSCAN5A, SIK2,
## SPSB1, YLPM1, SCIN, AC116563.1, NRCAM, CRIM1, BPIFC, SLA, GALNT14, MSH4, CRIPT,
## AL354811.2, UST, NIPAL4, CTXND1, ELOVL5, LINC00378, AL049828.1, KDR, CNGB1,
## Z99758.1, AC010997.3, AC109454.3, AL354733.2, NSG2, GPAT3, SHROOM4, RASSF4,
## FOXM1, LINC00571, TNIK, OASL, RBL1, POU2F2, EML4, IKZF3, CDHR3, CLPB,
## AL645933.3, PARD3, AL136456.1, PTP4A2, CSMD2, AHCYL2, NES, AC093898.1, MARCH1,
## ARHGAP6, SLC4A1, PAWR, AC110491.2, DPYS, PARN, ABCG2, CYP4F22, ITGA2,
## SH3TC2-DT, CHODL, GM2A, GINS2, CHDH, IGSF6, BRCA1, MEP1A, SRGAP3, AC084816.1,
## TROAP, LINC01900, APOM, COL8A2, RALGAPA2, AC005753.2, AC015908.2, STXBP5L,
## DNAJC1, FBXW7, AC099489.1, TMEM45A, FCMR, FRRS1, AC002454.1, ARL9, C1orf143,
## FAXC, ATP1B2, PDLIM4, AC016831.7, EMP1, RBPJ, ANKH, AC117453.1, CHAC1, KCP,
## SNAI3, FHAD1, DENND2A, TNFRSF12A, MX2, KDM7A, AC108066.1, SLC23A1, AL109930.1,
## SH3BP5, CENPU, KCNA2, AC011893.1, AIM2, TBC1D24, ATP1B1, AP000812.1, S100A6,
## JAML, MS4A4A, TGFB3, LINC00973, ING3, SOX5, MCAM, RBM47, AC246817.1, PCLO,
## PCDH15, TNFRSF11A, SNX10, ACTB, SCFD2, LINC02109, CHD5, AC093307.1, TSGA13,
## C11orf45, RHOD, AC007529.2, AC008443.9, AMPD1, CEP126, ITSN2, KCNJ1, CD1E,
## AL359220.1, RNF212, GNAI1, AC093010.2, TEX49, SPOPL, LINC02777, PRRX2,
## SEPTIN4-AS1, LIPG, HIST1H2AC, LINC02698, SDC2, CNTNAP5, SULT6B1, STXBP6,
## PPP1R16B, CFAP57, LINC01800, SLC22A2, EXOSC10, LINC02752, AC024901.1, ASPH,
## ZNF431, BICD1, DEGS2, GALNTL6, AC079742.1, MEI4, LINC01924, AMPD3, MB21D2,
## LINC01572, PLTP, ITPR2, SAMD12, EFNA2, HTRA4, XKR9, AL713998.1, AC016587.1,
## SLC35F3, EOGT, CDC5L, LINC00519, AC113137.1, ARSF, LIN28B-AS1, RASL10A, FCER1G,
## LINC00894, SYT10, RBPJL, AC007381.1, STMND1, AC006333.1, CNGA4, GLCCI1, TCEA3,
## LINC01739, AL355981.1, MOBP, AC079298.3, AC097487.1, AC137810.1, AL357146.1,
## TMEM213, AL136119.1, AC087897.2, AL160035.1, LINC01198, AC090515.6, AC018618.1,
## MAP1A, NR1H3, DNAH2, BX004807.1, NR6A1, IARS, TMEM131L, SYNE1, AL645465.1,
## BCL2A1, SH3PXD2B, AC099560.1, LPP, AL591845.1, HPN, KLB, SKA3, CPEB2-DT,
## INPP4B, ELF5, STUM, LMO4, NANOS1, ASTN2, STX4, LINC02805, GNGT1, HIST1H1C,
## AC010343.3, TYW1B, ACSBG2, TRMT5, LDLRAD4, SSH2, SLC25A23, LPAR1, AP001496.1,
## SERPINA1, HIST2H2BE, FCHO2, CDH12, AC011476.3, PAX5, GALNT18, FA2H, SDSL,
## ABCA1, CFI, LINC01948, IAPP, WASF3-AS1, AC130456.2, ROBO4, AC087280.2, IL3RA,
## DAO, AC073091.4, FUT2, GFOD1, AC055855.2, LINC02742, ZNF609, LINC01933, CORO1A,
## CLSTN3, LIMCH1, NABP1, FBXO43, DOCK2, ASMT, TREM1, CTNNA3, GRXCR2, AP000424.1,
## CNIH3, IGF1R, AC005280.1, PPP1R14C, GLT1D1, AC068587.4, AP001636.3, ERBB4,
## MIR155HG, ERLEC1, AGPAT4, GRAMD2A, ADGRL4, AC239860.4, LHCGR, AC103563.9,
## CCDC141, NECTIN3-AS1, AC010307.4, ZBED9, AC120498.4, OPRD1, SCG2, AC145146.1,
## AC068305.2, TMEM233, HECW1, NCAM2, SLAMF7, PRAG1, AL731563.2, FRMD6-AS2, GRIK5,
## AC021851.2, CDT1, WDR54, MYO16-AS1, LMCD1-AS1, AC096531.2, MAP1B, OR10G3,
## NUDT10, CIDEC, SSPO, FAM107B, RBM11, BARD1, EGFL7, MARCH3, SLC30A10, TFRC,
## PKIB, DENND5B, SPIRE1, AC079163.2, AHRR, ZNF543, QKI.## PC_ 1
## Positive: RASAL2, DOCK3, AC092353.2, TMEM117, DPYD, CPEB2, LINC01374, LRMDA, ASAP1, PLXDC2
## NEAT1, FMNL2, TPRG1, LRCH1, ATG7, ARL15, MALAT1, MITF, ATXN1, MAML2
## RAPGEF1, DENND1B, NUMB, EXOC4, ELMO1, FHIT, ST3GAL6, VWA8, ZNF438, PPARG
## Negative: GAPDH, H2AFZ, BLVRB, GSTP1, NUPR1, MMP9, FABP4, SAT1, CARD16, ALDH2
## STMN1, CFD, CYSTM1, FAH, PRDX6, MARCKS, CD74, PSAT1, IFITM3, HLA-DPA1
## GDF15, PTGDS, PHGDH, SELENOP, LINC02244, BTG1, TUBA1B, TMEM176B, GCHFR, TUBB
## PC_ 2
## Positive: LYZ, SLC8A1, MRC1, RCBTB2, FCGR3A, CTSC, NRP1, HLA-DPA1, CFD, AL356124.1
## TRPS1, ME1, RARRES1, HLA-DRA, ATP8B4, PDGFC, ARHGAP15, SELENOP, XYLT1, ZEB2
## FCHSD2, HLA-DPB1, SLCO2B1, DOCK4, AIF1, CD74, ALDH2, KCNMA1, CAMK1D, CCDC102B
## Negative: HIV-Nef, HIV-TatEx1, HIV-LTRU5, HIV-BaLEnv, HIV-LTRR, HIV-Polprot, HIV-Gagp17, HIV-Polp15p31, CCL22, HIV-EnvStart
## HIV-Vpr, HIV-Gagp1Pol, HIV-TatEx2Rev, IL6R-AS1, AL157912.1, GAL, HIV-Vif, HIV-Vpu, HIV-Gagp2p7, SLC20A1
## HIV-EGFP, DUSP13, IL1RN, TRIM54, CYTOR, MIR4435-2HG, MYL9, NMRK2, GPC3, RHOF
## PC_ 3
## Positive: CTSL, BCAT1, FDX1, MARCO, S100A9, DNASE2B, CCDC26, B4GALT5, TPM4, SAT1
## CD164, FMN1, FAH, UGCG, TXNRD1, PLEKHA5, SCD, STMN1, HES2, FABP4
## BCL11A, BLVRB, SNCA, SEL1L2, FRMD4A, QPCT, NUPR1, SLC11A1, SNTB1, CLMP
## Negative: CRIP1, MMP7, CLU, GCLC, HIV-TatEx1, RNF128, HIV-Nef, PTGDS, HIV-LTRR, CDKN2A
## KCNMA1, RTN1, HIV-Vpr, DUSP2, LINC02345, C1QTNF4, HIV-Gagp1Pol, IL1RN, TMEM176B, TIMP3
## HIV-EnvStart, CYP1B1, S100A4, ALOX5AP, VAMP5, LINC02408, RAMP1, AC067751.1, FGL2, HIV-Polprot
## PC_ 4
## Positive: NCAPH, LINC02244, AC015660.2, SYNGR1, RGS20, DUSP2, PTGDS, TMEM114, C2orf92, NIPAL2
## LINC01010, TRIM54, TM4SF19, MGST1, PSD3, SLC28A3, AC005280.2, ACAT2, SERTAD2, MICAL3
## MT1E, ANO5, ZNF366, BX664727.3, RGS16, TGM2, OCSTAMP, TDRD3, AL157886.1, SPP1
## Negative: HIV-TatEx1, HIV-LTRR, HIV-Vpr, HIV-Nef, HIV-Polprot, HIV-Gagp1Pol, HIV-EnvStart, HIV-BaLEnv, HIV-Vif, HIV-Polp15p31
## HIV-Gagp17, HIV-LTRU5, HIV-Vpu, HIV-Gagp2p7, HIV-TatEx2Rev, AIF1, HIV-EGFP, FPR3, CTSB, MARCKS
## MRC1, C1QA, CTSC, IL7R, CCL2, LGMN, OLR1, ALOX5, TNFSF13B, COLEC12
## PC_ 5
## Positive: CLSPN, SHCBP1, TYMS, DIAPH3, TK1, PCLAF, RRM2, HELLS, FAM111B, ESCO2
## CENPK, MYBL2, MKI67, CENPM, CIT, CDK1, ACTG1, NCAPG, CCNA2, ATAD2
## TOP2A, TUBB, KIF11, CEP55, HMMR, DTL, KNL1, TUBA1B, BIRC5, CENPF
## Negative: AC008591.1, XIST, QPCT, AC020656.1, GCHFR, GPX3, AC012668.3, LINC01340, MIR646HG, ST6GAL1
## LINC01708, LINC02320, AL136317.2, NMB, OSBP2, KCNMB2-AS1, LIX1-AS1, SKAP1, CFD, NIPAL2
## CCDC26, PDE4D, LINC00923, TMTC1, CPD, BX664727.3, GPRIN3, AP000331.1, RARRES1, GPAT2DimHeatmap(mono_focus_alv, dims = 1:2, cells = 500, balanced = TRUE)
DimHeatmap(mono_focus_alv, dims = 3:4, cells = 500, balanced = TRUE)
ElbowPlot(mono_focus_alv)
mono_focus_alv <- FindNeighbors(mono_focus_alv, dims = 1:4)## Computing nearest neighbor graph
## Computing SNNmono_focus_alv <- FindClusters(mono_focus_alv, algorithm = 3, resolution = 0.3, verbose = FALSE)
mono_focus_alv <- RunUMAP(mono_focus_alv, dims = 1:4)## 14:14:25 UMAP embedding parameters a = 0.9922 b = 1.112
## 14:14:25 Read 4420 rows and found 4 numeric columns
## 14:14:25 Using Annoy for neighbor search, n_neighbors = 30
## 14:14:25 Building Annoy index with metric = cosine, n_trees = 50
## 0% 10 20 30 40 50 60 70 80 90 100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 14:14:25 Writing NN index file to temp file /tmp/RtmpRdHgON/file11d0d4278f8c24
## 14:14:25 Searching Annoy index using 1 thread, search_k = 3000
## 14:14:27 Annoy recall = 100%
## 14:14:28 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 14:14:30 Initializing from normalized Laplacian + noise (using RSpectra)
## 14:14:30 Commencing optimization for 500 epochs, with 154158 positive edges
## 14:14:30 Using rng type: pcg
## 14:14:34 Optimization finishedDimPlot(mono_focus_alv, reduction = "umap", label=TRUE)
DimPlot(mono_focus_alv, group.by="monaco_fine_annotation" , reduction = "umap", label=TRUE)
DimPlot(mono_focus_alv, group.by="sample" , reduction = "umap", label=TRUE)
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")## Found more than one class "package_version" in cache; using the first, from namespace 'SeuratObject'## Also defined by 'alabaster.base'## Found more than one class "package_version" in cache; using the first, from namespace 'SeuratObject'## Also defined by 'alabaster.base'## Found more than one class "package_version" in cache; using the first, from namespace 'SeuratObject'## Also defined by 'alabaster.base'## Found more than one class "package_version" in cache; using the first, from namespace 'SeuratObject'## Also defined by 'alabaster.base'## Found more than one class "package_version" in cache; using the first, from namespace 'SeuratObject'## Also defined by 'alabaster.base'## Found more than one class "package_version" in cache; using the first, from namespace 'SeuratObject'## Also defined by 'alabaster.base'head(colData(sce),2)## DataFrame with 2 rows and 13 columns
## orig.ident nCount_RNA nFeature_RNA
## <factor> <numeric> <integer>
## mdm_mock1|AAACGAATCACATACG mac 72761 7103
## mdm_mock1|AAACGCTCATCAGCGC mac 49143 6282
## cell sample RNA_snn_res.0.5
## <character> <character> <factor>
## mdm_mock1|AAACGAATCACATACG mdm_mock1|AAACGAATCA.. mdm_mock1 5
## mdm_mock1|AAACGCTCATCAGCGC mdm_mock1|AAACGCTCAT.. mdm_mock1 12
## seurat_clusters cell_barcode
## <factor> <character>
## mdm_mock1|AAACGAATCACATACG 5 mdm_mock1|AAACGAATCA..
## mdm_mock1|AAACGCTCATCAGCGC 12 mdm_mock1|AAACGCTCAT..
## monaco_broad_annotation monaco_broad_pruned_labels
## <character> <character>
## mdm_mock1|AAACGAATCACATACG Monocytes Monocytes
## mdm_mock1|AAACGCTCATCAGCGC Monocytes Monocytes
## monaco_fine_annotation monaco_fine_pruned_labels
## <character> <character>
## mdm_mock1|AAACGAATCACATACG Classical monocytes Classical monocytes
## mdm_mock1|AAACGCTCATCAGCGC Classical monocytes Classical monocytes
## ident
## <factor>
## mdm_mock1|AAACGAATCACATACG 5
## mdm_mock1|AAACGCTCATCAGCGC 12colnames(colData(sce))## [1] "orig.ident" "nCount_RNA"
## [3] "nFeature_RNA" "cell"
## [5] "sample" "RNA_snn_res.0.5"
## [7] "seurat_clusters" "cell_barcode"
## [9] "monaco_broad_annotation" "monaco_broad_pruned_labels"
## [11] "monaco_fine_annotation" "monaco_fine_pruned_labels"
## [13] "ident"#muscat library
pb <- aggregateData(sce,
assay = "counts", fun = "sum",
by = c("monaco_broad_annotation", "sample"))
# one sheet per subpopulation
assayNames(pb)## [1] "Basophils" "Dendritic cells" "Monocytes"t(head(assay(pb)))## HIV-LTRR HIV-LTRU5 HIV-Gagp17 HIV-Gagp24 HIV-Gagp2p7
## alv_active1 0 0 0 0 0
## alv_active2 0 0 0 0 0
## alv_active3 0 0 0 0 0
## alv_bystander1 0 0 0 0 0
## alv_bystander2 0 0 0 0 0
## alv_bystander3 0 0 0 0 0
## alv_latent1 0 0 0 0 0
## alv_latent2 0 0 0 0 0
## alv_latent3 0 0 0 0 0
## alv_mock1 0 0 0 0 0
## alv_mock2 0 0 0 0 0
## alv_mock3 0 0 0 0 0
## mdm_active1 0 0 0 0 0
## mdm_active2 0 0 0 0 0
## mdm_active3 0 0 0 0 0
## mdm_active4 0 0 0 0 0
## mdm_bystander1 0 0 0 0 0
## mdm_bystander2 0 0 0 0 0
## mdm_bystander3 0 0 0 0 0
## mdm_bystander4 0 0 0 0 0
## mdm_latent1 0 0 0 0 0
## mdm_latent2 0 0 0 0 0
## mdm_latent3 0 0 0 0 0
## mdm_latent4 0 0 0 0 0
## mdm_mock1 0 0 0 0 0
## mdm_mock2 0 0 0 0 0
## mdm_mock3 0 0 0 0 0
## mdm_mock4 0 0 0 0 0
## react6 0 0 0 0 0
## HIV-Gagp1Pol
## alv_active1 0
## alv_active2 0
## alv_active3 0
## alv_bystander1 0
## alv_bystander2 0
## alv_bystander3 0
## alv_latent1 0
## alv_latent2 0
## alv_latent3 0
## alv_mock1 0
## alv_mock2 0
## alv_mock3 0
## mdm_active1 0
## mdm_active2 0
## mdm_active3 0
## mdm_active4 0
## mdm_bystander1 0
## mdm_bystander2 0
## mdm_bystander3 0
## mdm_bystander4 0
## mdm_latent1 0
## mdm_latent2 0
## mdm_latent3 0
## mdm_latent4 0
## mdm_mock1 0
## mdm_mock2 0
## mdm_mock3 0
## mdm_mock4 0
## react6 0plotMDS(assays(pb)[[3]], main="Monocytes" )
par(mfrow=c(2,3))
dump <- lapply(1:length(assays(pb)) , function(i) {
cellname=names(assays(pb))[i]
plotMDS(assays(pb)[[i]],cex=1,pch=19,main=paste(cellname),labels=colnames(assays(pb)[[1]]))
})## Warning in plotMDS.default(assays(pb)[[i]], cex = 1, pch = 19, main =
## paste(cellname), : dimension 2 is degenerate or all zeropar(mfrow=c(1,1))
DE Prep
pbmono <- assays(pb)[["Monocytes"]]
head(pbmono)## alv_active1 alv_active2 alv_active3 alv_bystander1 alv_bystander2
## HIV-LTRR 4391 3316 2601 111 134
## HIV-LTRU5 162741 127498 102452 2085 2885
## HIV-Gagp17 32789 27176 17079 106 162
## HIV-Gagp24 0 0 0 0 0
## HIV-Gagp2p7 1201 1242 744 16 26
## HIV-Gagp1Pol 2100 2334 1592 26 50
## alv_bystander3 alv_latent1 alv_latent2 alv_latent3 alv_mock1
## HIV-LTRR 138 249 261 411 31
## HIV-LTRU5 2848 10994 10224 17207 753
## HIV-Gagp17 183 2306 1784 2576 106
## HIV-Gagp24 0 0 0 0 0
## HIV-Gagp2p7 17 69 104 121 2
## HIV-Gagp1Pol 42 129 163 210 6
## alv_mock2 alv_mock3 mdm_active1 mdm_active2 mdm_active3
## HIV-LTRR 52 200 2251 1482 1476
## HIV-LTRU5 1311 7206 100998 71868 94108
## HIV-Gagp17 178 1530 38092 23541 40568
## HIV-Gagp24 0 0 0 0 0
## HIV-Gagp2p7 7 52 1462 1021 2365
## HIV-Gagp1Pol 21 94 2021 1375 3032
## mdm_active4 mdm_bystander1 mdm_bystander2 mdm_bystander3
## HIV-LTRR 1648 103 147 26
## HIV-LTRU5 56478 1425 1946 449
## HIV-Gagp17 15110 255 254 57
## HIV-Gagp24 0 0 0 0
## HIV-Gagp2p7 414 23 32 3
## HIV-Gagp1Pol 785 20 61 16
## mdm_bystander4 mdm_latent1 mdm_latent2 mdm_latent3 mdm_latent4
## HIV-LTRR 41 119 142 37 48
## HIV-LTRU5 725 5359 6710 2150 1644
## HIV-Gagp17 61 1927 2077 566 534
## HIV-Gagp24 0 0 0 0 0
## HIV-Gagp2p7 8 51 108 37 12
## HIV-Gagp1Pol 10 83 129 63 24
## mdm_mock1 mdm_mock2 mdm_mock3 mdm_mock4 react6
## HIV-LTRR 30 37 9 39 256
## HIV-LTRU5 486 685 106 1119 7461
## HIV-Gagp17 117 253 37 159 596
## HIV-Gagp24 0 0 0 0 0
## HIV-Gagp2p7 5 8 1 5 21
## HIV-Gagp1Pol 7 14 3 13 39dim(pbmono)## [1] 36622 29hiv <- pbmono[1:2,]
pbmono <- pbmono[3:nrow(pbmono),]Gene sets
Gene ontology.
#go <- gmt_import("c5.go.v2023.2.Hs.symbols.gmt")
#names(go) <- gsub("_"," ",names(go))
#wget https://ziemann-lab.net/public/tmp/go_2024-11.gmt
go <- gmt_import("go_2024-11.gmt")DE Mock
Compare MDM mock vs Alv mock.
DE5 Mock MDM (ctrl) vs Mock AlvMDM (case)
head(pbmdm)## mdm_active1 mdm_active2 mdm_active3 mdm_active4 mdm_bystander1
## HIV-Gagp17 38092 23541 40568 15110 255
## HIV-Gagp24 0 0 0 0 0
## HIV-Gagp2p7 1462 1021 2365 414 23
## HIV-Gagp1Pol 2021 1375 3032 785 20
## HIV-Polprot 27388 18583 44857 9126 331
## HIV-Polp15p31 75686 55267 105649 14984 1033
## mdm_bystander2 mdm_bystander3 mdm_bystander4 mdm_latent1
## HIV-Gagp17 254 57 61 1927
## HIV-Gagp24 0 0 0 0
## HIV-Gagp2p7 32 3 8 51
## HIV-Gagp1Pol 61 16 10 83
## HIV-Polprot 492 181 81 1383
## HIV-Polp15p31 1505 413 181 3589
## mdm_latent2 mdm_latent3 mdm_latent4 mdm_mock1 mdm_mock2 mdm_mock3
## HIV-Gagp17 2077 566 534 117 253 37
## HIV-Gagp24 0 0 0 0 0 0
## HIV-Gagp2p7 108 37 12 5 8 1
## HIV-Gagp1Pol 129 63 24 7 14 3
## HIV-Polprot 1587 877 250 136 196 47
## HIV-Polp15p31 5077 2425 441 341 550 101
## mdm_mock4
## HIV-Gagp17 159
## HIV-Gagp24 0
## HIV-Gagp2p7 5
## HIV-Gagp1Pol 13
## HIV-Polprot 146
## HIV-Polp15p31 257head(pbalv)## alv_active1 alv_active2 alv_active3 alv_bystander1 alv_bystander2
## HIV-Gagp17 32789 27176 17079 106 162
## HIV-Gagp24 0 0 0 0 0
## HIV-Gagp2p7 1201 1242 744 16 26
## HIV-Gagp1Pol 2100 2334 1592 26 50
## HIV-Polprot 23710 30544 21871 208 515
## HIV-Polp15p31 38437 59592 41124 476 1203
## alv_bystander3 alv_latent1 alv_latent2 alv_latent3 alv_mock1
## HIV-Gagp17 183 2306 1784 2576 106
## HIV-Gagp24 0 0 0 0 0
## HIV-Gagp2p7 17 69 104 121 2
## HIV-Gagp1Pol 42 129 163 210 6
## HIV-Polprot 534 1465 2065 3280 95
## HIV-Polp15p31 1151 2414 4070 5631 164
## alv_mock2 alv_mock3
## HIV-Gagp17 178 1530
## HIV-Gagp24 0 0
## HIV-Gagp2p7 7 52
## HIV-Gagp1Pol 21 94
## HIV-Polprot 230 1596
## HIV-Polp15p31 360 2804mock <- cbind( pbmdm[,grep("mock",colnames(pbmdm))],
pbalv[,grep("mock",colnames(pbalv))] )
head(mock)## mdm_mock1 mdm_mock2 mdm_mock3 mdm_mock4 alv_mock1 alv_mock2
## HIV-Gagp17 117 253 37 159 106 178
## HIV-Gagp24 0 0 0 0 0 0
## HIV-Gagp2p7 5 8 1 5 2 7
## HIV-Gagp1Pol 7 14 3 13 6 21
## HIV-Polprot 136 196 47 146 95 230
## HIV-Polp15p31 341 550 101 257 164 360
## alv_mock3
## HIV-Gagp17 1530
## HIV-Gagp24 0
## HIV-Gagp2p7 52
## HIV-Gagp1Pol 94
## HIV-Polprot 1596
## HIV-Polp15p31 2804mockf <- mock[which(rowMeans(mock)>=10),]
head(mockf)## mdm_mock1 mdm_mock2 mdm_mock3 mdm_mock4 alv_mock1 alv_mock2
## HIV-Gagp17 117 253 37 159 106 178
## HIV-Gagp2p7 5 8 1 5 2 7
## HIV-Gagp1Pol 7 14 3 13 6 21
## HIV-Polprot 136 196 47 146 95 230
## HIV-Polp15p31 341 550 101 257 164 360
## HIV-Vif 15 45 8 17 10 33
## alv_mock3
## HIV-Gagp17 1530
## HIV-Gagp2p7 52
## HIV-Gagp1Pol 94
## HIV-Polprot 1596
## HIV-Polp15p31 2804
## HIV-Vif 162colSums(mockf)## mdm_mock1 mdm_mock2 mdm_mock3 mdm_mock4 alv_mock1 alv_mock2 alv_mock3
## 28548322 20540758 7023535 20633808 20209667 24560365 33149165desmock <- as.data.frame(grepl("alv",colnames(mockf)))
colnames(desmock) <- "case"
plot(cmdscale(dist(t(mockf))), xlab="Coordinate 1", ylab="Coordinate 2",
type = "p",pch=19,col="gray",cex=2)
text(cmdscale(dist(t(mockf))), labels=colnames(mockf) )
dds <- DESeqDataSetFromMatrix(countData = mockf , colData = desmock, design = ~ case)## converting counts to integer moderes <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
de <- as.data.frame(zz[order(zz$pvalue),])
demock <- de
write.table(demock,"demockf.tsv",sep="\t")
nrow(subset(demock,padj<0.05 & log2FoldChange>0))## [1] 890nrow(subset(demock,padj<0.05 & log2FoldChange<0))## [1] 1317head(subset(demock,padj<0.05 & log2FoldChange>0),10)[,1:6] %>%
kbl(caption="Top upregulated genes in Alv cells compared to MDM") %>%
kable_paper("hover", full_width = F)| baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | |
|---|---|---|---|---|---|---|
| STAC | 411.32660 | 6.160050 | 0.4031531 | 15.279676 | 0 | 0 |
| PPP1R16A | 301.58230 | 2.616665 | 0.1799476 | 14.541256 | 0 | 0 |
| SPRED1 | 1277.40147 | 2.756665 | 0.2789790 | 9.881264 | 0 | 0 |
| FANCE | 139.95617 | 2.513477 | 0.2613868 | 9.615929 | 0 | 0 |
| PPP1R13B | 224.15035 | 2.295557 | 0.2433500 | 9.433149 | 0 | 0 |
| PPARD | 673.81261 | 1.768911 | 0.1914837 | 9.237919 | 0 | 0 |
| RGS16 | 135.58276 | 2.850967 | 0.3157553 | 9.029037 | 0 | 0 |
| TXNRD3 | 124.06561 | 1.937977 | 0.2300826 | 8.422961 | 0 | 0 |
| SMIM1 | 78.28049 | 3.015942 | 0.3611540 | 8.350848 | 0 | 0 |
| RAB40B | 175.67465 | 2.549723 | 0.3054759 | 8.346725 | 0 | 0 |
head(subset(demock, log2FoldChange<0),10)[,1:6] %>%
kbl(caption="Top downregulated genes in Alv cells compared to MDM") %>%
kable_paper("hover", full_width = F)| baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | |
|---|---|---|---|---|---|---|
| PLIN2 | 7536.75693 | -1.756264 | 0.1082373 | -16.226051 | 0 | 0 |
| OLFML2B | 334.87303 | -4.478105 | 0.3001465 | -14.919728 | 0 | 0 |
| STMN1 | 2557.73767 | -3.563794 | 0.2633830 | -13.530842 | 0 | 0 |
| MCUB | 378.71322 | -1.972595 | 0.1700001 | -11.603491 | 0 | 0 |
| FABP4 | 47047.92410 | -3.900944 | 0.3954489 | -9.864598 | 0 | 0 |
| C12orf75 | 70.89386 | -3.884104 | 0.3972731 | -9.776913 | 0 | 0 |
| SELENOW | 4343.79011 | -1.808964 | 0.1933682 | -9.355026 | 0 | 0 |
| CPA6 | 131.12764 | -2.615639 | 0.2953175 | -8.857042 | 0 | 0 |
| CST7 | 120.81091 | -4.333456 | 0.4897891 | -8.847596 | 0 | 0 |
| MME | 2273.35794 | -2.538423 | 0.3010091 | -8.433045 | 0 | 0 |
demockm <- mitch_import(de,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 = 15053## Note: no. genes in output = 15053## Note: estimated proportion of input genes in output = 1mresmock <- mitch_calc(demockm,genesets=go,minsetsize=5,cores=4,priority="effect")## Note: Enrichments with large effect sizes may not be
## statistically significant.res <- mresmock$enrichment_result
mitchtbl <- mresmock$enrichment_result
goid <- sapply(strsplit(mitchtbl$set," "),"[[",1)
mysplit <- strsplit(mitchtbl$set," ")
mysplit <- lapply(mysplit, function(x) { x[2:length(x)] } )
godescription <- unlist(lapply(mysplit, function(x) { paste(x,collapse=" ") } ))
em <- data.frame(goid,godescription,mitchtbl$pANOVA,mitchtbl$p.adjustANOVA,sign(mitchtbl$s.dist),mitchtbl$s.dist)
colnames(em) <- c("GO.ID","Description","p.Val","FDR","Phenotype","ES")
write.table(em,"demock_em.tsv",row.names = FALSE, quote=FALSE,sep="\t")
res <- subset(res,p.adjustANOVA<0.05)
resup <- subset(res,s.dist>0)
resdn <- subset(res,s.dist<0)
s <- c(head(resup$s.dist,10), head(resdn$s.dist,10))
names(s) <- c(head(resup$set,10),head(resdn$set,10))
s <- s[order(s)]
cols <- gsub("1","red",gsub("-1","blue",as.character(sign(s))))
par(mar=c(5,27,3,1))
barplot(abs(s),las=1,horiz=TRUE,col=cols,xlab="ES",cex.names=0.8,main="")
if (! file.exists("mock_Mdm_vs_Alv.html") ) {
mitch_report(mresmock,outfile="mock_Mdm_vs_Alv.html")
}## Dataset saved as " /tmp/RtmpRdHgON/mock_Mdm_vs_Alv.rds ".##
##
## processing file: mitch.Rmd## 1/36
## 2/36 [checklibraries]
## 3/36
## 4/36 [peek]
## 5/36
## 6/36 [metrics]
## 7/36
## 8/36 [scatterplot]## 9/36
## 10/36 [contourplot]
## 11/36
## 12/36 [input_geneset_metrics1]
## 13/36
## 14/36 [input_geneset_metrics2]## 15/36
## 16/36 [input_geneset_metrics3]## 17/36
## 18/36 [echart1d]
## 19/36 [echart2d]
## 20/36
## 21/36 [heatmap]
## 22/36
## 23/36 [effectsize]
## 24/36
## 25/36 [results_table]
## 26/36
## 27/36 [results_table_complete]
## 28/36
## 29/36 [detailed_geneset_reports1d]## 30/36
## 31/36 [detailed_geneset_reports2d]
## 32/36
## 33/36 [network]## 34/36
## 35/36 [session_info]
## 36/36## output file: /scratch/hearps/macrophage/mitch.knit.md## /usr/bin/pandoc +RTS -K512m -RTS /scratch/hearps/macrophage/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/RtmpRdHgON/mitch_report.html --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/latex-div.lua --self-contained --variable bs3=TRUE --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=bootstrap --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /tmp/RtmpRdHgON/rmarkdown-str11d0d45b2ec7f.html##
## Output created: /tmp/RtmpRdHgON/mitch_report.html## [1] TRUEnetworkplot(mresmock,FDR=0.05,n_sets=20)
network_genes(mresmock,FDR=0.05,n_sets=20)## [[1]]
## [[1]]$`UP genesets.GO:0000978 RNA polymerase II cis-regulatory region sequence-specific DNA binding`
## [1] "PPARD" "PITX1" "NFATC3" "ZNF366" "SKI" "RBPJ"
## [7] "TCF7L2" "EGR2" "DLX3" "STAT6" "ESR1" "RARA"
## [13] "ZBTB7B" "HIVEP3" "AR" "MAF" "VDR" "KLF9"
## [19] "VAX2" "CALCOCO1" "SNAI3" "TEAD3" "CEBPA" "HDAC4"
## [25] "NACC2" "HIC2" "POU3F1" "KLF16" "ZBTB8A" "IKZF2"
## [31] "NR2F6" "ZNF362" "DNMT3A" "E2F3" "TFCP2" "MXD4"
## [37] "ZNF678" "ZNF76" "MITF" "NLRC5" "KLF4" "SREBF2"
## [43] "ZNF711" "ZNF808" "MEF2A" "TEF" "POU2F2" "NFYC"
## [49] "ZFHX3" "ZBTB7C" "RXRA" "NFATC1" "RREB1" "KDM2B"
## [55] "MLXIPL" "BCL6" "DMTF1" "MED1" "ZNF692" "GFI1"
## [61] "TFE3" "FOXK2" "IKZF5" "TP63" "NR3C1" "ZNF646"
## [67] "SKIL" "GRHL1" "ZNF585A" "MZF1" "ZNF347" "TFEB"
## [73] "ZNF573" "ZNF514" "ZNF761" "TARDBP" "IKZF1" "ZFAT"
## [79] "ZNF623" "ZNF470" "CHD7" "JUND" "ZNF121" "MAFK"
##
## [[1]]$`UP genesets.GO:0000981 DNA-binding transcription factor activity, RNA polymerase II-specific`
## [1] "PPARD" "PITX1" "GTF2I" "NFATC3" "SKI" "RBPJ" "TCF7L2" "EGR2"
## [9] "DLX3" "STAT6" "ESR1" "RARA" "ZBTB7B" "HIVEP3" "AR" "MAF"
## [17] "VDR" "KLF9" "VAX2" "SNAI3" "TEAD3" "CEBPA" "NACC2" "POU3F1"
## [25] "KLF16" "ZBTB8A" "NR2F6" "NPAS1" "E2F3" "TFCP2" "MXD4" "ZBTB46"
## [33] "ZNF678" "ZNF76" "MITF" "ERF" "MYRF" "KLF4" "SREBF2" "ZNF711"
## [41] "ZNF79" "PKNOX1" "MEF2A" "TEF" "POU2F2" "NFYC" "ZFHX3" "ZBTB7C"
## [49] "RXRA" "NFATC1" "MLXIPL" "ZBTB40" "DMTF1" "ZNF768" "SCX" "TFE3"
## [57] "FOXK2" "TP63" "NR3C1" "ZNF646" "SKIL" "GRHL1" "ZGLP1" "ZNF316"
## [65] "MZF1" "CASZ1" "ESRRA" "ZNF347" "TFEB" "ZNF573" "ZNF514" "ZNF440"
## [73] "ZNF672" "ELF5" "CREB5" "ZNF761" "ZBTB42" "ZFAT" "ZNF623" "ZNF470"
## [81] "JUND" "CUX1" "ZNF121" "MAFK"
##
## [[1]]$`UP genesets.GO:0003222 ventricular trabecula myocardium morphogenesis`
## [1] "TGFBR1" "RBPJ" "ENG" "TGFB2" "HEG1" "MED1" "CHD7"
##
## [[1]]$`UP genesets.GO:0005096 GTPase activator activity`
## [1] "RGS16" "ABR" "RAP1GAP" "FAM13B" "ASAP1" "TBC1D1"
## [7] "RGS12" "RANGAP1" "ARHGAP31" "ACAP3" "RANBP2" "ARHGAP25"
## [13] "SGSM2" "ARAP1" "RASAL2" "RGS18" "ARHGAP10" "ARHGAP18"
## [19] "ARHGEF1" "DEPDC5" "RASGRP3" "LLGL1" "VPS9D1" "SH3BP1"
## [25] "TBC1D12" "RGS3" "TBC1D9B" "AGAP1" "RACGAP1" "TBC1D22B"
## [31] "SRGAP3" "TBCD" "TBC1D2B" "RASA4B" "SIPA1" "ARRB1"
## [37] "TBC1D4" "AGAP2" "ARHGAP44" "RIN2" "RABEP2" "ARHGAP1"
## [43] "ARHGAP22" "RASA4" "TBC1D10C" "RAP1GAP2" "ARHGAP20" "SGSM3"
## [49] "TBC1D14" "AGFG2"
##
## [[1]]$`UP genesets.GO:0005524 ATP binding`
## [1] "NEK6" "TRPV1" "ABCB9" "ABCA6" "TGFBR1" "SHPK"
## [7] "VWA8" "MYO1F" "ENTPD1" "MERTK" "DCLK3" "MAP3K5"
## [13] "ATP2B2" "ABCA2" "MYO1E" "PDK2" "MINK1" "GCLC"
## [19] "CAMK1" "CHD9" "MARK2" "TTLL12" "ADCY7" "NOD1"
## [25] "PIK3CD" "MARK3" "FGFR1" "ATP2A3" "ITPKB" "KCNJ1"
## [31] "PRKACB" "PRKCA" "PRAG1" "ABCD1" "AKT1" "TAP2"
## [37] "MAP3K11" "MTHFD1" "PRKCD" "EEF2K" "MYO6" "DGKH"
## [43] "PI4KA" "DDX6" "COQ8A" "DMPK" "IKBKE" "NPR1"
## [49] "KIF17" "SNRK" "TRIB1" "TOP2B" "TTLL11" "KIFC2"
## [55] "GRK2" "PEX6" "PIKFYVE" "IPPK" "ATP2B3" "ITPK1"
## [61] "TTL" "DCLK2" "ADCK1" "CHKA" "NLRC5" "NWD1"
## [67] "ALPK1" "PI4K2A" "ACSS2" "PIK3CG" "ABCB8" "KIF1B"
## [73] "EIF4G1" "DAPK1" "HK3" "CLCN7" "CSNK1G2" "PTK2B"
## [79] "ABCA9" "RPS6KL1" "GAK" "ACSF3" "MLKL" "ADK"
## [85] "SMG1" "MYLK" "TPK1" "SLC12A4" "CDK17" "HSPA14"
## [91] "DCAF1" "ULK2" "XYLB" "DGKZ" "CARNS1" "TGFBR2"
## [97] "MCM3" "LMTK2" "QRSL1" "ATR" "GATC" "ADCY10"
## [103] "DYRK1A" "PTK6" "AGAP2" "AFG3L2" "CHEK2" "ACTR3B"
## [109] "KATNAL2" "MYO19" "MCM5" "ROR1" "ABCC1" "NLK"
## [115] "ABL2" "RNF213" "MAPKAPK3" "FARSA" "PMVK" "IGF1R"
## [121] "CSK" "PIK3C2B" "ABCC4" "LRRK1" "IP6K1" "DHX38"
## [127] "MKKS" "DSTYK" "CAMK2B" "DHX34" "MOV10" "ABCC10"
## [133] "CAMKK1" "BBS10" "BMPR2" "MYO1C" "PFKL" "LIG1"
## [139] "AKT2" "OPLAH" "LRGUK" "BTK" "ASCC3" "FARS2"
## [145] "AAK1" "NLRP2" "MYH11" "ADCK2" "PSKH1" "PDPK1"
## [151] "MYO15A" "PIK3R4" "PIK3C3" "DDX10" "CHD7" "MAP3K14"
## [157] "IKBKB" "CDK6" "MAP3K3" "ARAF"
##
## [[1]]$`UP genesets.GO:0006338 chromatin remodeling`
## [1] "NEK6" "ENTPD1" "MERTK" "DPF3" "DCLK3" "ESR1"
## [7] "PTPRA" "MINK1" "CHD9" "MARK2" "RB1" "DUSP2"
## [13] "DUSP7" "MARK3" "FGFR1" "PWWP2A" "PRKCA" "HDAC4"
## [19] "AKT1" "BCOR" "PRKCD" "KAT2B" "DDX6" "DMPK"
## [25] "SNRK" "BAP1" "PHLPP1" "PTPRC" "SETD3" "BRPF1"
## [31] "PIKFYVE" "PTPN13" "PTPN22" "DUSP19" "DCLK2" "CHKA"
## [37] "ALPK1" "CDC25B" "PIK3CG" "MBD3" "DAPK1" "UBASH3B"
## [43] "CSNK1G2" "PTK2B" "RPS6KL1" "GAK" "PPM1M" "SMG1"
## [49] "PADI2" "PTPRO" "PER2" "DCAF1" "ULK2" "PTPN7"
## [55] "KDM2B" "MCM3" "LMTK2" "ATR" "DYRK1A" "PPP3CA"
## [61] "PTK6" "AFG3L2" "CHEK2" "ALPL" "EPM2A" "MCM5"
## [67] "TP63" "ABL2" "RNF213" "MAPKAPK3" "IGF1R" "CSK"
## [73] "LRRK1" "DHX38" "DSTYK" "DHX34" "YEATS2" "MYO1C"
## [79] "PPM1N" "PTPN18" "GATAD2A" "AKT2" "BTK" "ASCC3"
## [85] "KDM4B" "AAK1" "PSKH1" "KDM4C" "PDPK1" "PTPRM"
## [91] "PIK3R4" "HCFC2" "DDX10" "CHD7" "IKBKB" "ARAF"
##
## [[1]]$`UP genesets.GO:0032012 regulation of ARF protein signal transduction`
## [1] "CYTH4" "GBF1" "PSD4"
##
## [[1]]$`UP genesets.GO:0046872 metal ion binding`
## [1] "STAC" "TRPV1" "ADAM12" "CBFA2T3" "TGFBR1"
## [6] "SMAD6" "ASAP1" "FGD5" "PDLIM7" "ZNF366"
## [11] "STAC2" "MICAL3" "KCNC3" "CSGALNACT1" "EGR2"
## [16] "RNF128" "SLC30A3" "SPON2" "ATP2B2" "ZBTB7B"
## [21] "HIVEP3" "PARP12" "LONRF3" "SH3RF2" "VAV1"
## [26] "WTIP" "KLF9" "ACAP3" "CALCOCO1" "CHSY3"
## [31] "ADCY7" "HDAC7" "ZC3H7B" "NTHL1" "ATP2A3"
## [36] "RANBP2" "TRIP6" "SOBP" "FURIN" "TAP2"
## [41] "PRKCD" "CLEC7A" "ZC3H12B" "GALNT12" "DGKH"
## [46] "ZNF618" "TRERF1" "HIC2" "TATDN2" "DPYD"
## [51] "ARAP1" "ZNF804A" "MICALL2" "SMAD7" "PHOSPHO1"
## [56] "KLF16" "ZBTB8A" "IKZF2" "F13A1" "OVCH1"
## [61] "KCNK6" "DMPK" "FRRS1" "MBNL1" "TOP2B"
## [66] "CHFR" "TTLL11" "ZNF362" "PHLPP1" "SLC30A1"
## [71] "XYLT1" "DNMT3A" "BRPF1" "TNFRSF11A" "PLEKHF1"
## [76] "ABAT" "RNF40" "ATP2B3" "ZNF592" "ADARB1"
## [81] "ZBTB46" "ZNF678" "KAT6B" "ZNF76" "MSL2"
## [86] "LIN54" "RNF166" "PHYHD1" "ZNF711" "MGRN1"
## [91] "FGD6" "ZNF79" "FHL3" "B4GALT2" "KCNK13"
## [96] "IRF2BPL" "SMPD4" "GNA11" "ZNF808" "NDUFS1"
## [101] "ASXL2" "ZC3H4" "TNS1" "AGAP1" "ITGA11"
## [106] "RACGAP1" "EXT1" "ADK" "PDE4A" "SMG1"
## [111] "MYLK" "PDSS1" "ANKFY1" "FYCO1" "USP4"
## [116] "ZBTB7C" "SLC12A6" "STAC3" "RASA4B" "TMEM129"
## [121] "ITGAM" "MT1F" "NLN" "DAGLA" "OGDH"
## [126] "CYB561D1" "RREB1" "BCL6" "CNOT6" "LNPK"
## [131] "DGKZ" "PDE8B" "DBR1" "GTF2E1" "LIMD2"
## [136] "USP2" "CARNS1" "TGFBR2" "ZBTB40" "CEPT1"
## [141] "ADAM15" "ZNF768" "ZNF692" "POLD1" "AGAP2"
## [146] "CHEK2" "MIPEP" "GALNT18" "GFI1" "ZNF41"
## [151] "PAN2" "FLYWCH1" "TP63" "ZNF646" "HMOX1"
## [156] "PHRF1" "RNF213" "BAK1" "ADAM17" "CSK"
## [161] "UNC13D" "ZNF585A" "ZNF316" "MZF1" "RASA4"
## [166] "LRRK1" "CASZ1" "CYBB" "RAPSN" "ZNF347"
## [171] "CYB561A3" "PDLIM2" "ZNF573" "ZNF514" "ZNF440"
## [176] "RNF121" "BMPR2" "THOP1" "ZNF672" "TCF20"
## [181] "PFKL" "ALKBH5" "MAN2C1" "LIG1" "GATAD2A"
## [186] "AKT2" "RNF168" "BTK" "KDM4B" "CREB5"
## [191] "PPM1D" "PRORP" "MBNL3" "ZNF761" "ZBTB42"
## [196] "LPP" "CARD9" "NUDT22" "IKZF1" "ZFAT"
## [201] "ZNF623" "ZNF470" "AGFG2" "ZNF121" "MAP3K3"
## [206] "ARAF" "ABLIM1"
##
## [[1]]$`UP genesets.GO:0051056 regulation of small GTPase mediated signal transduction`
## [1] "ABR" "RAP1GAP" "FAM13B" "FGD5" "ARHGEF3" "VAV1"
## [7] "ARHGAP31" "ARHGEF17" "ARAP1" "ARHGAP10" "ARHGAP18" "ARHGEF1"
## [13] "TIAM1" "SH3BP1" "RACGAP1" "SRGAP3" "SIPA1" "ARHGAP44"
## [19] "ARHGAP1" "ARHGAP22" "ARHGEF40" "RAP1GAP2" "ARHGAP20"
##
## [[1]]$`UP genesets.GO:0071467 cellular response to pH`
## [1] "HVCN1" "GPR68" "GPLD1" "GNA11"
##
## [[1]]$`DOWN genesets.GO:0002181 cytoplasmic translation`
## [1] "RPL7A" "RPL17" "RPS27A" "RPL8" "RPS7" "RPL28" "RPL6"
## [8] "RPL18" "RPL5" "RPL9" "RPL24" "RPS3A" "RPS3" "RACK1"
## [15] "RPL11" "RPL14" "RPS19" "RPS25" "RPL15" "RPL22" "RPL35A"
## [22] "RPS10" "RPL13" "RPS15A" "RPS8" "RPL26" "RPL19" "RPLP1"
## [29] "RPL10" "RPL27" "RPL12" "RPS13" "RPS14" "RPL29" "RPL21"
## [36] "RPLP0" "RPL7" "RPS23" "RPS5" "RPL3" "RPS12" "RPS11"
## [43] "RPL37" "RPS15" "RPL39" "RPS6" "RPL30" "RPL18A" "RPL13A"
## [50] "RPL23" "RPS4X" "RPL23A" "RPS20" "RPL34" "RPL32" "DRG1"
## [57] "FAU" "RPS9" "UBA52" "RPS24" "RPL36A" "RPL4" "RPL35"
## [64] "RPL41" "RPS17" "RPL31" "RPS2" "RPS16" "RPS18" "RPS27"
## [71] "FTSJ1" "RPL27A" "RPL36" "RPS28" "RPLP2" "RWDD1" "RPL22L1"
## [78] "RPSA" "RPL37A" "RPS29" "RPL38" "RPL26L1" "ZC3H15" "RPS21"
## [85] "RPS26" "DRG2" "RPL10A"
##
## [[1]]$`DOWN genesets.GO:0002503 peptide antigen assembly with MHC class II protein complex`
## [1] "HLA-DQA2" "HLA-DOA" "HLA-DMB" "HLA-DMA" "HLA-DQA1" "HLA-DPB1"
## [7] "B2M" "HLA-DRA" "HLA-DQB1" "HLA-DPA1" "HLA-DRB5" "HLA-DRB1"
##
## [[1]]$`DOWN genesets.GO:0003735 structural constituent of ribosome`
## [1] "RPL7A" "RPL17" "RPS27A" "RPL8" "RPS7" "RPL28" "RPL6"
## [8] "RPL18" "RPL5" "RPL9" "RPL24" "RPS3A" "RPS3" "RPL11"
## [15] "RPL14" "MRPL9" "RPS19" "RPS25" "RPL15" "RPL22" "RPL35A"
## [22] "RPS10" "RPL13" "RPS15A" "RPS8" "RPL26" "RPL19" "RPS27L"
## [29] "RPLP1" "RPL10" "RPL27" "MRPL18" "MRPL43" "RPL12" "RPS13"
## [36] "RPS14" "RPL29" "RPL21" "RPLP0" "RPL7" "RPS23" "RSL24D1"
## [43] "RPS5" "RPL3" "RPS12" "MRPL17" "RPS11" "RPL37" "RPS15"
## [50] "MRPS16" "RPL39" "RPS6" "RPL30" "MRPS18C" "RPL18A" "RPL13A"
## [57] "RPL23" "RPS4X" "RPL23A" "MRPS23" "RPS20" "MRPS2" "RPL34"
## [64] "RPL32" "FAU" "RPS9" "MRPS6" "MRPS22" "MRPS15" "UBA52"
## [71] "RPS24" "RPL36AL" "SRBD1" "RPL36A" "RPL4" "RPL39L" "MRPL36"
## [78] "RPL35" "MRPS21" "RPL41" "MRPS30" "MRPS14" "RPS17" "RPL31"
## [85] "MRPL13" "RPS2" "MRPL10" "RPS16" "MRPL46" "RPS18" "RPS27"
## [92] "MRPL51" "MRPL52" "MRPL30" "MRPL21" "MRPS18B" "RPL27A" "MRPL22"
## [99] "MRPL24" "MRPL12" "MRPS31" "MRPL3" "RPL36" "MRPL23" "RPL7L1"
## [106] "MRPL49" "MRPS12" "MRPL28" "RPS28" "RPLP2" "MRPS24" "MRPS18A"
## [113] "MRPL2" "RPL22L1" "MRPL1" "RPSA" "MRPL41" "MRPL55" "MRPL54"
## [120] "MRPS35" "RPL37A" "RPS29" "MRPL16" "MRPL47" "MRPL27" "MRPS33"
## [127] "MRPS34" "MRPL20" "MRPL14" "RPL38" "RPL26L1" "MRPS9" "MRPL42"
## [134] "MRPS11" "MRPS25" "MRPL4" "MRPL33" "RPS21" "MRPL35" "MRPL19"
## [141] "MRPS7" "MRPL57" "MRPL15" "RPS26" "MRPL34" "MRPL32" "MRPS5"
## [148] "RPS4Y1" "MRPL45" "MRPL11" "RPL10A" "MRPS17" "DAP3"
##
## [[1]]$`DOWN genesets.GO:0005687 U4 snRNP`
## [1] "SNRPD3" "SNRPD2" "SNRPG" "SNRPE" "PRPF31" "SNRPN" "DDX39B" "SNRPD1"
## [9] "SNRPF" "SNRPB"
##
## [[1]]$`DOWN genesets.GO:0005839 proteasome core complex`
## [1] "PSMB2" "PSMA2" "PSMA6" "PSMB1" "PSMA3" "PSMB3" "PSMA7" "PSMA4"
## [9] "PSMA1" "PSMB4" "PSMF1" "PSMB5" "PSMB7" "PSMA5" "PSMB10" "PSMB8"
## [17] "PSMB9" "PSMB6"
##
## [[1]]$`DOWN genesets.GO:0006412 translation`
## [1] "RPL7A" "RPL17" "RPS27A" "RPL8" "RPS7" "RPL28"
## [7] "RPL6" "RPL18" "RPL5" "NACA" "RPL9" "RPL24"
## [13] "RPS3A" "RPS3" "RPL11" "RPL14" "MRPL9" "RPS19"
## [19] "RPS25" "RPL15" "RPL22" "RPL35A" "RPS10" "RPL13"
## [25] "RPS15A" "EEF1A1" "RPS8" "RPL26" "RPL19" "RPS27L"
## [31] "RPLP1" "RPL10" "RPL27" "MRPL18" "MRPL43" "RPL12"
## [37] "RPS13" "RPS14" "RPL29" "RPL21" "RPLP0" "RPL7"
## [43] "GSPT2" "RPS23" "RSL24D1" "RPS5" "RPL3" "RPS12"
## [49] "RPS11" "RPL37" "RPS15" "PABPC4" "MRPS16" "RPL39"
## [55] "RPS6" "RPL30" "MRPS18C" "RPL18A" "RPL13A" "RPL23"
## [61] "RPS4X" "RPL23A" "PSTK" "RPS20" "RPL34" "RPL32"
## [67] "FAU" "EIF4EBP2" "RPS9" "MRPS6" "MRPS15" "UBA52"
## [73] "RPS24" "EEF1E1" "RPL36AL" "SRBD1" "RPL36A" "RPL4"
## [79] "RPL39L" "MRPL36" "RPL35" "MRPS21" "YARS2" "RPL41"
## [85] "MRPS14" "RPS6KB2" "RPS17" "RPL31" "MRPL13" "RPS2"
## [91] "MRPL10" "COPS5" "RPS16" "RPS18" "RPS27" "MRPL51"
## [97] "FARSB" "METTL17" "RMND1" "MRPL52" "MRPS18B" "AIMP1"
## [103] "RPL27A" "MRPL22" "MRPL24" "MRPL12" "MRPL3" "RPL36"
## [109] "MRPL23" "MRPS12" "MRPL28" "RPS28" "RPLP2" "MRRF"
## [115] "DHPS" "MRPS18A" "EIF2AK2" "EIF4ENIF1" "RPSA" "MRPL41"
## [121] "MRPL55" "AGO2" "RPL37A" "RPS29" "ABCF1" "MRPL27"
## [127] "MRPS33" "CPEB4" "RPL38" "GEMIN5" "MRPL42" "MRPS11"
## [133] "CPEB1" "LARP4" "RPS21" "RRBP1" "MRPL35" "GUF1"
## [139] "MRPS7" "RPS26" "IGF2BP3" "MRPL34" "MRPL32" "MRPS5"
## [145] "HBS1L" "GSPT1" "RPS4Y1" "MRPL11" "AIMP2" "HARS2"
## [151] "PAIP2" "RPL10A" "MRPS17"
##
## [[1]]$`DOWN genesets.GO:0015935 small ribosomal subunit`
## [1] "RPS27A" "RACK1" "RPS25" "RPS6" "RPS4X" "FAU" "MRPS6" "RPS24"
## [9] "RPS16" "RPS18" "RPS28" "RPS29" "RPS21" "RPS26"
##
## [[1]]$`DOWN genesets.GO:0019731 antibacterial humoral response`
## [1] "DEFB1" "CAMP" "ANG" "BPI" "RNASE4" "B2M" "RPL39" "FAU"
## [9] "HLA-A" "RNASE6" "APP" "ADM" "PLA2G6" "HLA-E" "WFDC2" "WFDC3"
##
## [[1]]$`DOWN genesets.GO:0019773 proteasome core complex, alpha-subunit complex`
## [1] "PSMA2" "PSMA6" "PSMA3" "PSMA7" "PSMA4" "PSMA1" "PSMA5"
##
## [[1]]$`DOWN genesets.GO:0019886 antigen processing and presentation of exogenous peptide antigen via MHC class II`
## [1] "IFI30" "CTSL" "HLA-DQA2" "HLA-DOA" "HLA-DMB" "HLA-DMA"
## [7] "FCGR2B" "LGMN" "HLA-DQA1" "HLA-DPB1" "B2M" "FCER1G"
## [13] "CTSS" "HLA-DRA" "HLA-DQB1" "CD74" "HLA-DPA1" "UNC93B1"
## [19] "CTSF" "CTSD" "TRAF6" "CTSV" "HLA-DRB5" "HLA-DRB1"
##
## [[1]]$`DOWN genesets.GO:0022625 cytosolic large ribosomal subunit`
## [1] "RPL7A" "RPL17" "RPL8" "RPL28" "RPL6" "RPL18" "RPL5"
## [8] "RPL9" "RPL24" "RPL11" "RPL14" "RPL15" "RPL22" "RPL35A"
## [15] "RPL13" "RPL26" "RPL19" "RPLP1" "RPL10" "RPL27" "RPL12"
## [22] "RPL29" "RPL21" "RPLP0" "RPL7" "RPL3" "RPL37" "RPL39"
## [29] "ZCCHC17" "RPL30" "RPL18A" "RPL13A" "RPL23" "RPL23A" "RPL34"
## [36] "RPL32" "UBA52" "RPL36AL" "RPL36A" "RPL4" "RPL39L" "RPL35"
## [43] "RPL41" "RPL31" "RPL27A" "RPL36" "RPL7L1" "RPLP2" "RPL37A"
## [50] "RPL38" "RPL26L1" "RPL10A"
##
## [[1]]$`DOWN genesets.GO:0022626 cytosolic ribosome`
## [1] "RPL7A" "RPL17" "RPS27A" "RPL8" "RPS7" "RPL28" "RPL6"
## [8] "RPL18" "RPL5" "RPL9" "RPL24" "RPS3A" "RPS3" "RPL11"
## [15] "RPL14" "RPS19" "RPS25" "RPL15" "RPL22" "RPL35A" "RPS10"
## [22] "PELO" "RPL13" "RPS15A" "EEF1A1" "RPS8" "RPL26" "RPL19"
## [29] "RPLP1" "RPL10" "RPL27" "RPL12" "RPS13" "RPS14" "RPL29"
## [36] "RPL21" "RPLP0" "RPL7" "RPS23" "RPS5" "RPL3" "RPS12"
## [43] "RPS11" "RPL37" "RPS15" "RPL39" "RPS6" "RPL30" "RPL18A"
## [50] "EIF2AK4" "RPL13A" "RPL23" "RPS4X" "RPL23A" "RPS20" "RPL34"
## [57] "RPL32" "FAU" "RPS9" "UBA52" "RPS24" "RPL36A" "RPL4"
## [64] "RPL35" "ABCE1" "RPS17" "RPL31" "RNF10" "RPS2" "RPS16"
## [71] "RPS18" "METAP1" "RPS27" "NEMF" "ZNF598" "RPL27A" "RPL36"
## [78] "LTN1" "USP10" "RPSA" "RPL37A" "APOD" "ETF1" "ASCC2"
## [85] "RPL38" "RNF25" "RPS21" "RNF14" "HBS1L" "GSPT1" "RPL10A"
##
## [[1]]$`DOWN genesets.GO:0022627 cytosolic small ribosomal subunit`
## [1] "RPS27A" "RPS7" "RPS3A" "RPS3" "RACK1" "RPS19" "RPS25" "RPS10"
## [9] "RPS15A" "RPS8" "RPS27L" "RPS13" "RPS14" "RPS23" "RPS5" "RPS12"
## [17] "RPS11" "RPS15" "RPS6" "RPS4X" "RPS20" "FAU" "RPS9" "RPS24"
## [25] "RPS17" "RPS2" "RPS16" "RPS18" "RPS27" "RPS28" "RPSA" "RPS29"
## [33] "DHX29" "DDX3X" "EIF2A" "LARP4" "RPS21" "RPS26" "RPS4Y1"
##
## [[1]]$`DOWN genesets.GO:0030881 beta-2-microglobulin binding`
## [1] "MICA" "FCGRT" "CD1D" "HFE" "HLA-F" "HLA-A" "MR1" "HLA-E"
##
## [[1]]$`DOWN genesets.GO:0042605 peptide antigen binding`
## [1] "HLA-DQA2" "HLA-DOA" "FCGRT" "HLA-DMB" "HLA-DMA" "HFE"
## [7] "SLC7A5" "HLA-B" "HLA-DQA1" "HLA-DPB1" "B2M" "HLA-DRA"
## [13] "HLA-F" "HLA-DQB1" "HLA-A" "HLA-DPA1" "TAPBP" "HLA-C"
## [19] "HLA-E" "HLA-DRB5" "HLA-G" "TAP1" "MAML1" "HLA-DRB1"
## [25] "CD209" "DHCR24" "SLC7A8"
##
## [[1]]$`DOWN genesets.GO:0042613 MHC class II protein complex`
## [1] "HLA-DQA2" "HLA-DOA" "HLA-DMB" "HLA-DMA" "HLA-DQA1" "HLA-DPB1"
## [7] "B2M" "HLA-DRA" "HLA-DQB1" "CD74" "HLA-DPA1" "HLA-DRB5"
## [13] "HLA-DRB1"
##
## [[1]]$`DOWN genesets.GO:0071011 precatalytic spliceosome`
## [1] "SNRPD3" "SNRPD2" "SF3B4" "RBMX2" "SF3B2" "SNRPG" "SNRPE"
## [8] "PRPF31" "LSM8" "WBP4" "SMU1" "SNRPD1" "SF3B5" "LSM2"
## [15] "LSM3" "PHF5A" "SNU13" "PRPF38A" "PRPF38B"
##
## [[1]]$`DOWN genesets.GO:0097435 supramolecular fiber organization`
## [1] "SNCA" "MFAP4" "BID" "FKBP1A" "MFAP5" "BASP1"
## [7] "HSP90AB1" "B4GALT7" "CST3" "BAX"
##
## [[1]]$`DOWN genesets.GO:0097542 ciliary tip`
## [1] "KIF3A" "IFT22" "ARMC9" "DYNLRB1" "WDR19" "IFT20"
## [7] "KIF3C" "WDR35" "IFT43" "DYNLL1" "DYNLRB2" "KIFAP3"
## [13] "DYNC2LI1" "IFT57" "DYNC2H1" "CLUAP1" "TRAF3IP1" "TTC21B"
## [19] "IFT52" "IFT88" "CYLD" "IFT81" "IFT140" "DYNLL2"
## [25] "IFT172" "IFT27" "CDKL5" "SUFU" "IFT80" "IFT74"
## [31] "IFT46" "ULK3" "KIF3B"
##
## [[1]]$`DOWN genesets.GO:1904645 response to amyloid-beta`
## [1] "MMP12" "FYN" "MMP2" "HDAC2" "PRNP" "MMP9" "CACNA1A"
## [8] "AGER"Session information
For reproducibility.
save.image("scanalysis_demock.Rdata")
sessionInfo()## R version 4.5.0 (2025-04-11)
## 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 LAPACK version 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] gtools_3.9.5 future_1.40.0
## [3] gplots_3.2.0 limma_3.64.0
## [5] SingleR_2.10.0 celldex_1.18.0
## [7] harmony_1.2.3 Rcpp_1.0.14
## [9] mitch_1.20.0 DESeq2_1.48.0
## [11] muscat_1.22.0 beeswarm_0.4.0
## [13] stringi_1.8.7 SingleCellExperiment_1.30.0
## [15] SummarizedExperiment_1.38.0 Biobase_2.68.0
## [17] GenomicRanges_1.60.0 GenomeInfoDb_1.44.0
## [19] IRanges_2.42.0 S4Vectors_0.46.0
## [21] BiocGenerics_0.54.0 generics_0.1.3
## [23] MatrixGenerics_1.20.0 matrixStats_1.5.0
## [25] hdf5r_1.3.12 Seurat_5.3.0
## [27] SeuratObject_5.1.0 sp_2.2-0
## [29] plyr_1.8.9 ggplot2_3.5.2
## [31] kableExtra_1.4.0
##
## loaded via a namespace (and not attached):
## [1] dichromat_2.0-0.1 progress_1.2.3
## [3] goftest_1.2-3 HDF5Array_1.36.0
## [5] Biostrings_2.76.0 vctrs_0.6.5
## [7] spatstat.random_3.3-3 digest_0.6.37
## [9] png_0.1-8 corpcor_1.6.10
## [11] shape_1.4.6.1 gypsum_1.4.0
## [13] ggrepel_0.9.6 echarts4r_0.4.5
## [15] deldir_2.0-4 parallelly_1.43.0
## [17] MASS_7.3-65 reshape2_1.4.4
## [19] httpuv_1.6.16 foreach_1.5.2
## [21] withr_3.0.2 xfun_0.52
## [23] survival_3.8-3 memoise_2.0.1.9000
## [25] ggbeeswarm_0.7.2 systemfonts_1.2.2
## [27] zoo_1.8-14 GlobalOptions_0.1.2
## [29] pbapply_1.7-2 prettyunits_1.2.0
## [31] GGally_2.2.1 KEGGREST_1.48.0
## [33] promises_1.3.2 httr_1.4.7
## [35] globals_0.17.0 fitdistrplus_1.2-2
## [37] rhdf5filters_1.20.0 rhdf5_2.52.0
## [39] rstudioapi_0.17.1 UCSC.utils_1.4.0
## [41] miniUI_0.1.2 curl_6.2.2
## [43] h5mread_1.0.0 ScaledMatrix_1.16.0
## [45] polyclip_1.10-7 GenomeInfoDbData_1.2.14
## [47] ExperimentHub_2.16.0 SparseArray_1.8.0
## [49] xtable_1.8-4 stringr_1.5.1
## [51] doParallel_1.0.17 evaluate_1.0.3
## [53] S4Arrays_1.8.0 BiocFileCache_2.16.0
## [55] hms_1.1.3 irlba_2.3.5.1
## [57] colorspace_2.1-1 filelock_1.0.3
## [59] ROCR_1.0-11 reticulate_1.42.0
## [61] spatstat.data_3.1-6 magrittr_2.0.3
## [63] lmtest_0.9-40 later_1.4.2
## [65] viridis_0.6.5 lattice_0.22-7
## [67] spatstat.geom_3.3-6 future.apply_1.11.3
## [69] scattermore_1.2 scuttle_1.18.0
## [71] cowplot_1.1.3 RcppAnnoy_0.0.22
## [73] pillar_1.10.2 nlme_3.1-168
## [75] iterators_1.0.14 caTools_1.18.3
## [77] compiler_4.5.0 beachmat_2.24.0
## [79] RSpectra_0.16-2 tensor_1.5
## [81] minqa_1.2.8 crayon_1.5.3
## [83] abind_1.4-8 scater_1.36.0
## [85] blme_1.0-6 locfit_1.5-9.12
## [87] bit_4.6.0 dplyr_1.1.4
## [89] codetools_0.2-20 BiocSingular_1.24.0
## [91] bslib_0.9.0 alabaster.ranges_1.8.0
## [93] GetoptLong_1.0.5 plotly_4.10.4
## [95] remaCor_0.0.18 mime_0.13
## [97] splines_4.5.0 circlize_0.4.16
## [99] fastDummies_1.7.5 dbplyr_2.5.0
## [101] sparseMatrixStats_1.20.0 knitr_1.50
## [103] blob_1.2.4 clue_0.3-66
## [105] BiocVersion_3.21.1 lme4_1.1-37
## [107] listenv_0.9.1 DelayedMatrixStats_1.30.0
## [109] Rdpack_2.6.4 tibble_3.2.1
## [111] Matrix_1.7-3 statmod_1.5.0
## [113] svglite_2.1.3 fANCOVA_0.6-1
## [115] pkgconfig_2.0.3 network_1.19.0
## [117] tools_4.5.0 cachem_1.1.0
## [119] RhpcBLASctl_0.23-42 rbibutils_2.3
## [121] RSQLite_2.3.9 viridisLite_0.4.2
## [123] DBI_1.2.3 numDeriv_2016.8-1.1
## [125] fastmap_1.2.0 rmarkdown_2.29
## [127] scales_1.4.0 grid_4.5.0
## [129] ica_1.0-3 broom_1.0.8
## [131] AnnotationHub_3.16.0 sass_0.4.10
## [133] patchwork_1.3.0 coda_0.19-4.1
## [135] BiocManager_1.30.25 ggstats_0.9.0
## [137] dotCall64_1.2 RANN_2.6.2
## [139] alabaster.schemas_1.8.0 farver_2.1.2
## [141] reformulas_0.4.0 aod_1.3.3
## [143] mgcv_1.9-3 yaml_2.3.10
## [145] cli_3.6.5 purrr_1.0.4
## [147] lifecycle_1.0.4 uwot_0.2.3
## [149] glmmTMB_1.1.11 mvtnorm_1.3-3
## [151] backports_1.5.0 BiocParallel_1.42.0
## [153] gtable_0.3.6 rjson_0.2.23
## [155] ggridges_0.5.6 progressr_0.15.1
## [157] jsonlite_2.0.0 edgeR_4.6.1
## [159] RcppHNSW_0.6.0 bitops_1.0-9
## [161] bit64_4.6.0-1 Rtsne_0.17
## [163] alabaster.matrix_1.8.0 spatstat.utils_3.1-3
## [165] BiocNeighbors_2.2.0 alabaster.se_1.8.0
## [167] jquerylib_0.1.4 spatstat.univar_3.1-2
## [169] pbkrtest_0.5.4 lazyeval_0.2.2
## [171] alabaster.base_1.8.0 shiny_1.10.0
## [173] htmltools_0.5.8.1 sctransform_0.4.1
## [175] rappdirs_0.3.3 glue_1.8.0
## [177] spam_2.11-1 httr2_1.1.2
## [179] XVector_0.48.0 gridExtra_2.3
## [181] EnvStats_3.1.0 boot_1.3-31
## [183] igraph_2.1.4 variancePartition_1.38.0
## [185] TMB_1.9.17 R6_2.6.1
## [187] tidyr_1.3.1 labeling_0.4.3
## [189] cluster_2.1.8.1 Rhdf5lib_1.30.0
## [191] nloptr_2.2.1 statnet.common_4.11.0
## [193] DelayedArray_0.34.1 tidyselect_1.2.1
## [195] vipor_0.4.7 xml2_1.3.8
## [197] AnnotationDbi_1.70.0 rsvd_1.0.5
## [199] KernSmooth_2.23-26 data.table_1.17.0
## [201] htmlwidgets_1.6.4 ComplexHeatmap_2.24.0
## [203] RColorBrewer_1.1-3 rlang_1.1.6
## [205] spatstat.sparse_3.1-0 spatstat.explore_3.4-2
## [207] lmerTest_3.1-3