Tohidul: timecourse effect of Seasol on gene expression

Source codes: https://github.com/markziemann/tohidul_rnaseq

Background

Here we have n=3 control (H2O; "H") and n=3 seaweed based fertiliser treatment ("80"). Arabidopsis RNA samples. Reads underwent quality trimming using Skewer (Jiang et al, 2014). I mapped the reads to the Arabidopsis transcriptome (TAIR10/Ensembl47) using Kallisto (Bray et al, 2016). Expression counts were loaded into R and then DE analysis was performed with DESeq2 (Love et al, 2014). Enrichment analysis was performed using Plant Reactome genesets with the Mitch package (Kaspi & Ziemann 2020).

suppressPackageStartupMessages({
    library("reshape2")
    library("gplots")
    library("DESeq2")
    library("mitch")
})

Load data

Here we load the data in from the aligner.

tmp <- read.table("3col.tsv")
x <- as.data.frame(acast(tmp, V2~V1, value.var="V3"))
x$gene <- sapply(strsplit(rownames(x),"\\."),"[[",1)
xx <- aggregate(. ~ gene, x, sum)
rownames(xx) <- xx$gene
xx$gene = NULL

Sample sheet

ss <- data.frame(colnames(xx))
rownames(ss) <- ss[,1]
ss$trt <- as.numeric(grepl("ANDP",ss[,1]))
ss[,1]=NULL

MDS

MDS is just like PCA. The more similar (correlated) the data sets are the closer they will appear on the scatterplot.

plot(cmdscale(dist(t(xx))), xlab="Coordinate 1", ylab="Coordinate 2", 
  type = "n" , main="MDS plot")
text(cmdscale(dist(t(xx))), labels=colnames(xx) )

Split data into timepoints

ss0 <- ss[grep("-0",rownames(ss)),,drop=FALSE]
ss1 <- ss[grep("-1",rownames(ss)),,drop=FALSE]
ss3 <- ss[grep("-3",rownames(ss)),,drop=FALSE]
ss5 <- ss[grep("-5",rownames(ss)),,drop=FALSE]
xx0 <- xx[,grep("-0",colnames(xx))]
xx1 <- xx[,grep("-1",colnames(xx))]
xx3 <- xx[,grep("-3",colnames(xx))]
xx5 <- xx[,grep("-5",colnames(xx))]
xx0 <- xx0[which(rowMeans(xx0)>10),]
xx1 <- xx1[which(rowMeans(xx1)>10),]
xx3 <- xx3[which(rowMeans(xx3)>10),]
xx5 <- xx5[which(rowMeans(xx5)>10),]

DE

Here, were using DESeq2 to perform differential expression analysis at the different time points. Enrichment analysis is performed at the end using mitch with all 4 timepoints.

Time=0

ss <- ss0
y <- xx0

y <- round(y)
dds <- DESeqDataSetFromMatrix(countData=y, colData = ss, design = ~ trt)

## converting counts to integer mode

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

dds <- DESeq(dds)

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

de <- DESeq2::results(dds)
de <- de[order(de$pvalue),]
head(de)

## log2 fold change (MLE): trt 
## Wald test p-value: trt 
## DataFrame with 6 rows and 6 columns
##            baseMean log2FoldChange     lfcSE      stat      pvalue        padj
##           <numeric>      <numeric> <numeric> <numeric>   <numeric>   <numeric>
## AT1G13290  2081.316       -7.73328  0.529423 -14.60700 2.53451e-48 5.35110e-44
## AT5G52640  3849.294       -5.50748  0.475893 -11.57293 5.65184e-31 5.96636e-27
## AT3G12580  5741.498       -5.38270  0.549237  -9.80032 1.12228e-22 7.89825e-19
## AT1G51410   114.947       -7.79115  0.864563  -9.01166 2.02953e-19 1.07124e-15
## AT5G37550   629.490       -4.95259  0.576224  -8.59491 8.33277e-18 3.51860e-14
## AT5G02245   158.419      -11.61553  1.385322  -8.38472 5.08476e-17 1.78924e-13

write.table(de,file="t0.tsv",sep="\t",quote=FALSE)
de0<-de

# define up and down-regulated gene lists
de0_up <- rownames(subset(de, log2FoldChange>0 & padj<0.05 ))
de0_dn <- rownames(subset(de, log2FoldChange<0 & padj<0.05 ))
str(de0_up)

##  chr [1:2] "AT3G01205" "AT4G08015"

str(de0_dn)

##  chr [1:46] "AT1G13290" "AT5G52640" "AT3G12580" "AT1G51410" "AT5G37550" ...

# MA plot
sig <-subset(de, padj < 0.05 )
GENESUP <- length(de0_up)
GENESDN <- length(de0_dn)
SUBHEADER = paste(GENESUP, "up, ", GENESDN, "down")
ns <-subset(de, padj > 0.05 )
plot(log2(de$baseMean),de$log2FoldChange, 
     xlab="log2 basemean", ylab="log2 foldchange",
     pch=19, cex=0.5, col="dark gray",
     main="smear plot")
points(log2(sig$baseMean),sig$log2FoldChange,
       pch=19, cex=0.5, col="red")
mtext(SUBHEADER)

# heatmap
yn <- y/colSums(y)*1000000
yf <- yn[which(rownames(yn) %in% rownames(de)[1:50]),]

mycols <- gsub("0","yellow",ss$trt)
mycols <- gsub("1","orange",mycols)

colfunc <- colorRampPalette(c("blue", "white", "red"))
heatmap.2(  as.matrix(yf), col=colfunc(25),scale="row", ColSideColors =mycols ,trace="none",
    margins = c(10,10), cexRow=0.6, main="Top 50 genes by p-val")
mtext("yellow=ctrl, orange=trt")

Time=1

ss <- ss1
y <- xx1

y <- round(y)
dds <- DESeqDataSetFromMatrix(countData=y, colData = ss, design = ~ trt)

## converting counts to integer mode

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

dds <- DESeq(dds)

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

de <- DESeq2::results(dds)
de <- de[order(de$pvalue),]
head(de)

## log2 fold change (MLE): trt 
## Wald test p-value: trt 
## DataFrame with 6 rows and 6 columns
##            baseMean log2FoldChange     lfcSE      stat      pvalue        padj
##           <numeric>      <numeric> <numeric> <numeric>   <numeric>   <numeric>
## AT5G01210  1303.013      -10.32959  0.603150 -17.12607 9.48498e-66 2.12672e-61
## AT3G22240  3741.196       -2.37339  0.162260 -14.62708 1.88712e-48 2.11565e-44
## AT5G38200   585.818       -3.78238  0.291339 -12.98277 1.53229e-38 1.14524e-34
## AT5G25460  9924.824       -1.66104  0.173018  -9.60040 7.96332e-22 4.46384e-18
## AT1G61560  1084.515       -1.71889  0.197606  -8.69855 3.36154e-18 1.50745e-14
## AT1G08100  1241.409        2.63910  0.316135   8.34800 6.94311e-17 2.59464e-13

write.table(de,file="t1.tsv",sep="\t",quote=FALSE)
de1<-de

# define up and down-regulated gene lists
de1_up <- rownames(subset(de, log2FoldChange>0 & padj<0.05 ))
de1_dn <- rownames(subset(de, log2FoldChange<0 & padj<0.05 ))
str(de1_up)

##  chr [1:53] "AT1G08100" "AT4G01630" "AT3G45060" "AT1G11050" "AT4G01870" ...

str(de1_dn)

##  chr [1:79] "AT5G01210" "AT3G22240" "AT5G38200" "AT5G25460" "AT1G61560" ...

# MA plot
sig <-subset(de, padj < 0.05 )
GENESUP <- length(de1_up)
GENESDN <- length(de1_dn)
SUBHEADER = paste(GENESUP, "up, ", GENESDN, "down")
ns <-subset(de, padj > 0.05 )
plot(log2(de$baseMean),de$log2FoldChange,
     xlab="log2 basemean", ylab="log2 foldchange",
     pch=19, cex=0.5, col="dark gray",
     main="smear plot")
points(log2(sig$baseMean),sig$log2FoldChange,
       pch=19, cex=0.5, col="red")
mtext(SUBHEADER)

# heatmap
yn <- y/colSums(y)*1000000
yf <- yn[which(rownames(yn) %in% rownames(de)[1:50]),]

mycols <- gsub("0","yellow",ss$trt)
mycols <- gsub("1","orange",mycols)

colfunc <- colorRampPalette(c("blue", "white", "red"))
heatmap.2(  as.matrix(yf), col=colfunc(25),scale="row", ColSideColors =mycols ,trace="none",
    margins = c(10,10), cexRow=0.6, main="Top 50 genes by p-val")
mtext("yellow=ctrl, orange=trt")

Time=3

ss <- ss3
y <- xx3

y <- round(y)
dds <- DESeqDataSetFromMatrix(countData=y, colData = ss, design = ~ trt)

## converting counts to integer mode

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

dds <- DESeq(dds)

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

de <- DESeq2::results(dds)
de <- de[order(de$pvalue),]
head(de)

## log2 fold change (MLE): trt 
## Wald test p-value: trt 
## DataFrame with 6 rows and 6 columns
##            baseMean log2FoldChange     lfcSE      stat      pvalue        padj
##           <numeric>      <numeric> <numeric> <numeric>   <numeric>   <numeric>
## AT3G24290   562.818        2.15876  0.197847  10.91127 1.01824e-27 2.29663e-23
## AT4G13420 19380.086       -3.17171  0.305653 -10.37683 3.16102e-25 3.56484e-21
## AT5G38910   438.621       -4.96761  0.506140  -9.81469 9.73341e-23 7.31790e-19
## AT2G05440  2736.674       -2.17518  0.252982  -8.59816 8.10009e-18 4.56744e-14
## AT1G18100   460.405       -2.79818  0.343798  -8.13902 3.98500e-16 1.79763e-12
## AT5G38200   446.594       -3.05357  0.383870  -7.95468 1.79592e-15 6.75117e-12

write.table(de,file="t3.tsv",sep="\t",quote=FALSE)
de3<-de

# define up and down-regulated gene lists
de3_up <- rownames(subset(de, log2FoldChange>0 & padj<0.05 ))
de3_dn <- rownames(subset(de, log2FoldChange<0 & padj<0.05 ))
str(de3_up)

##  chr [1:66] "AT3G24290" "AT2G16018" "AT1G51410" "AT3G22050" "AT5G09595" ...

str(de3_dn)

##  chr [1:92] "AT4G13420" "AT5G38910" "AT2G05440" "AT1G18100" "AT5G38200" ...

# MA plot
sig <-subset(de, padj < 0.05 )
GENESUP <- length(de3_up)
GENESDN <- length(de3_dn)
SUBHEADER = paste(GENESUP, "up, ", GENESDN, "down")
ns <-subset(de, padj > 0.05 )
plot(log2(de$baseMean),de$log2FoldChange,
     xlab="log2 basemean", ylab="log2 foldchange",
     pch=19, cex=0.5, col="dark gray",
     main="smear plot")
points(log2(sig$baseMean),sig$log2FoldChange,
       pch=19, cex=0.5, col="red")
mtext(SUBHEADER)

# heatmap
yn <- y/colSums(y)*1000000
yf <- yn[which(rownames(yn) %in% rownames(de)[1:50]),]

mycols <- gsub("0","yellow",ss$trt)
mycols <- gsub("1","orange",mycols)

colfunc <- colorRampPalette(c("blue", "white", "red"))
heatmap.2(  as.matrix(yf), col=colfunc(25),scale="row", ColSideColors =mycols ,trace="none",
    margins = c(10,10), cexRow=0.6, main="Top 50 genes by p-val")
mtext("yellow=ctrl, orange=trt")

Time=5

ss <- ss5
y <- xx5

y <- round(y)
dds <- DESeqDataSetFromMatrix(countData=y, colData = ss, design = ~ trt)

## converting counts to integer mode

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

dds <- DESeq(dds)

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

de <- DESeq2::results(dds)
de <- de[order(de$pvalue),]
head(de)

## log2 fold change (MLE): trt 
## Wald test p-value: trt 
## DataFrame with 6 rows and 6 columns
##            baseMean log2FoldChange     lfcSE      stat      pvalue        padj
##           <numeric>      <numeric> <numeric> <numeric>   <numeric>   <numeric>
## AT3G04615  177.6955       -9.92596  1.338789  -7.41413 1.22423e-13 2.74913e-09
## AT1G13290  431.1310        4.54168  0.651076   6.97565 3.04452e-12 2.97741e-08
## AT4G11430 5042.6050       -3.22807  0.465276  -6.93797 3.97766e-12 2.97741e-08
## AT1G17232   90.6113        6.29700  1.168051   5.39103 7.00542e-08 3.93284e-04
## AT5G38910  690.9549       -3.45829  0.749850  -4.61198 3.98857e-06 1.76261e-02
## ATCG01200   31.8335        8.48648  1.854024   4.57733 4.70951e-06 1.76261e-02

write.table(de,file="t5.tsv",sep="\t",quote=FALSE)
de5<-de

# define up and down-regulated gene lists
de5_up <- rownames(subset(de, log2FoldChange>0 & padj<0.05 ))
de5_dn <- rownames(subset(de, log2FoldChange<0 & padj<0.05 ))
str(de5_up)

##  chr [1:4] "AT1G13290" "AT1G17232" "ATCG01200" "AT4G36570"

str(de5_dn)

##  chr [1:5] "AT3G04615" "AT4G11430" "AT5G38910" "AT2G14265" "AT1G62540"

# MA plot
sig <-subset(de, padj < 0.05 )
GENESUP <- length(de5_up)
GENESDN <- length(de5_dn)
SUBHEADER = paste(GENESUP, "up, ", GENESDN, "down")
ns <-subset(de, padj > 0.05 )
plot(log2(de$baseMean),de$log2FoldChange,
     xlab="log2 basemean", ylab="log2 foldchange",
     pch=19, cex=0.5, col="dark gray",
     main="smear plot")
points(log2(sig$baseMean),sig$log2FoldChange,
       pch=19, cex=0.5, col="red")
mtext(SUBHEADER)

# heatmap
yn <- y/colSums(y)*1000000
yf <- yn[which(rownames(yn) %in% rownames(de)[1:50]),]

mycols <- gsub("0","yellow",ss$trt)
mycols <- gsub("1","orange",mycols)

colfunc <- colorRampPalette(c("blue", "white", "red"))
heatmap.2(  as.matrix(yf), col=colfunc(25),scale="row", ColSideColors =mycols ,trace="none",
    margins = c(10,10), cexRow=0.6, main="Top 50 genes by p-val")
mtext("yellow=ctrl, orange=trt")

DEG summary

Here is a heatmap of all the differentially expressed genes. It looks a bit random.

head(xx)

##            ANDP-0R1  ANDP-0R2  ANDP-0R3  ANDP-1R1  ANDP-1R2  ANDP-1R3  ANDP-3R1
## AT1G01010  713.0000  215.0000 1123.0000  924.0000 1203.0000  956.0000  959.0000
## AT1G01020  560.7159  417.9205  626.9648  645.3107  481.6385  625.1463  242.6268
## AT1G01030  204.0000   74.0000  305.0000  302.0000  510.0000  303.0000  237.0001
## AT1G01040 3218.9960 1783.0010 3816.0020 3266.0050 3079.0000 3738.0040 2665.0020
## at1g01046    7.0000    0.0000    0.0000    7.0000    0.0000    0.0000   21.0000
## AT1G01050 3749.0000 1930.0000 5533.0000 4416.0000 4610.0000 5583.3800 4278.0000
##            ANDP-3R2  ANDP-3R3  ANDP-5R1  ANDP-5R2  ANDP-5R3    H-0R1     H-0R2
## AT1G01010  592.0000  833.0000 1245.0000 2432.0000 1234.0000 130.0000  141.0000
## AT1G01020  268.1491  474.9948  308.7638  273.4157  542.0385 132.8582  229.2556
## AT1G01030  125.0000  167.0000  205.0000  120.0000  262.0002  24.0000  104.0000
## AT1G01040 1858.0010 3870.9960 4594.0000 3151.9950 5143.0000 564.0000  830.0000
## at1g01046   13.0000    6.0000    5.0000    6.0000   31.0000   0.0000    5.0000
## AT1G01050 2524.0000 3223.0011 4270.2800 3530.7600 4420.3100 757.0000 1086.0000
##               H-0R3     H-1R1     H-1R2     H-1R3     H-3R1     H-3R2     H-3R3
## AT1G01010  335.0000  616.0000  603.0000  325.0000 1249.0000  804.0000 1002.0000
## AT1G01020  175.3078  430.6219  386.7882  229.1482  612.6377  571.7053  608.1417
## AT1G01030  137.0000  222.0000  249.0001  175.0000  400.0000  172.0000  254.0000
## AT1G01040 1391.9981 2320.0000 3064.9964 2236.0000 4576.0800 4947.0000 5773.9980
## at1g01046    9.0000   16.0000   10.0000    0.0000   11.0000   23.0000   23.0000
## AT1G01050 1706.0000 3966.0000 5411.0000 3186.0000 5300.9300 5202.3100 4451.0000
##               H-5R1     H-5R2     H-5R3
## AT1G01010 2687.0000 2601.0000  972.0000
## AT1G01020  675.3855  454.9859  413.3085
## AT1G01030  293.0000  229.0004  271.0000
## AT1G01040 4961.0030 3467.0000 4833.9990
## at1g01046    0.0000   22.0000   19.0000
## AT1G01050 5144.0000 4075.0000 3889.0035

degs <- unique(c(de0_up,de0_dn,de1_up,de1_dn,de3_up,de3_dn,de5_up,de5_dn))
xxx <- xx/colSums(xx)*1e6
deg_mx <- as.matrix(xxx[which(rownames(xxx) %in% degs),])
heatmap.2(deg_mx,col=colfunc(25),trace="none",scale="row",margin=c(10,10))

Pathway analysis

Mapman pathways last modified in 2012 and used in the previous RNA-seq analysis.

genesets <- gmt_import("../ref/Ath_AGI_LOCUS_TAIR10_Aug2012.txt.gmt")
gt <- read.table("../ref/Arabidopsis_thaliana.TAIR10.46.geneaccession2symbol.tsv",
    fill=TRUE) 

l <- list("T0"=de0,"T1"=de1,"T3"=de3,"T5"=de5)

m <- mitch_import(x=l,DEtype="deseq2",geneTable=gt)

## Note: Mean no. genes in input = 22227.5

## Note: no. genes in output = 20425

## Note: estimated proportion of input genes in output = 0.919

capture.output(
    res <- mitch_calc(x=m,genesets=genesets,priority="significance")
    , file = "/dev/null", append = FALSE,
    type = c("output", "message"), split = FALSE)

## Note: When prioritising by significance (ie: small 
##             p-values), large effect sizes might be missed.

top <- subset(res$enrichment_result,p.adjustMANOVA<0.05)
head(top,30)

##                                                                                  set
## 86        NOT_ASSIGNED_NO_ONTOLOGY_PENTATRICOPEPTIDE_(PPR)_REPEAT-CONTAINING_PROTEIN
## 220                              SIGNALLING_RECEPTOR_KINASES_LEUCINE_RICH_REPEAT_III
## 123                                                  PROTEIN_SYNTHESIS_RIBOSOMAL_RNA
## 84                                    NOT_ASSIGNED_NO_ONTOLOGY_GLYCINE_RICH_PROTEINS
## 140                        PS_LIGHTREACTION_PHOTOSYSTEM_II_PSII_POLYPEPTIDE_SUBUNITS
## 70                                     MISC_UDP_GLUCOSYL_AND_GLUCORONYL_TRANSFERASES
## 141                          PS_LIGHTREACTION_PHOTOSYSTEM_I_PSI_POLYPEPTIDE_SUBUNITS
## 81                            NOT_ASSIGNED_NO_ONTOLOGY_DC1_DOMAIN_CONTAINING_PROTEIN
## 24                                                 DNA_SYNTHESIS/CHROMATIN_STRUCTURE
## 11                                             CELL_WALL_CELL_WALL_PROTEINS_AGPS_AGP
## 7                                                                  CELL_ORGANISATION
## 89                                                              NOT_ASSIGNED_UNKNOWN
## 35                                   HORMONE_METABOLISM_ETHYLENE_SIGNAL_TRANSDUCTION
## 104                                                 PROTEIN_DEGRADATION_UBIQUITIN_E2
## 135                                  PROTEIN_TARGETING_SECRETORY_PATHWAY_UNSPECIFIED
## 8                                                             CELL_VESICLE_TRANSPORT
## 194          RNA_REGULATION_OF_TRANSCRIPTION_WRKY_DOMAIN_TRANSCRIPTION_FACTOR_FAMILY
## 60                                                   MISC_GLUTATHIONE_S_TRANSFERASES
## 151                                                      RNA_PROCESSING_RNA_HELICASE
## 258                                                               TRANSPORT_SULPHATE
## 207                                                            SIGNALLING_G-PROTEINS
## 159                                   RNA_REGULATION_OF_TRANSCRIPTION_AUX/IAA_FAMILY
## 129                                                    PROTEIN_TARGETING_CHLOROPLAST
## 106                                            PROTEIN_DEGRADATION_UBIQUITIN_E3_RING
## 63                                                   MISC_MYROSINASES-LECTIN-JACALIN
## 9                                                      CELL_WALL_CELLULOSE_SYNTHESIS
## 98                                                     PROTEIN_DEGRADATION_AUTOPHAGY
## 68  MISC_PROTEASE_INHIBITOR/SEED_STORAGE/LIPID_TRANSFER_PROTEIN_(LTP)_FAMILY_PROTEIN
## 75   MITOCHONDRIAL_ELECTRON_TRANSPORT_/_ATP_SYNTHESIS_NADH-DH_LOCALISATION_NOT_CLEAR
## 235                                                       STRESS_ABIOTIC_UNSPECIFIED
##     setSize      pMANOVA         s.T0         s.T1        s.T3         s.T5
## 86      415 5.064691e-23  0.111891283 -0.003978974 -0.23675367  0.139407164
## 220      39 5.902518e-10 -0.271885935 -0.110052902  0.04710623  0.574212569
## 123      14 4.284723e-09 -0.705781896 -0.675028171 -0.44945653 -0.073237820
## 84       52 6.796541e-09 -0.054295089 -0.266381976 -0.44730016 -0.188851006
## 140      42 7.225847e-09 -0.115717346 -0.274072932 -0.53779410 -0.037750880
## 70      151 8.277358e-09 -0.107894690  0.057274283  0.25568062  0.140191300
## 141      18 1.382413e-08  0.008085461 -0.282130859 -0.86323320  0.038331074
## 81       86 1.854708e-08  0.096756489 -0.185498818  0.18437370  0.287461253
## 24      200 3.312227e-08  0.083382447  0.052652658 -0.16594808  0.163816069
## 11       39 9.592934e-07 -0.136815864  0.048160251  0.25080812  0.456057073
## 7       337 1.900105e-06  0.052436933  0.093070313  0.08898266  0.123456199
## 89     4338 3.451802e-06  0.015697996 -0.025819080  0.04059088 -0.009828344
## 35       28 2.054090e-05 -0.220914840  0.281837665 -0.38114499 -0.156066368
## 104      37 3.524432e-05 -0.219373877 -0.052778794  0.22662509 -0.351011989
## 135      71 3.783071e-05  0.103602849  0.120907819  0.31325676  0.082141864
## 8       152 3.851236e-05  0.039892312 -0.002936236  0.22401587  0.072192208
## 194      56 7.685623e-05 -0.111731413  0.277361256  0.02472244 -0.236537666
## 60       49 1.730053e-04 -0.139447770  0.087185404 -0.05604633 -0.353082458
## 151      33 2.022123e-04  0.123090457  0.078869313 -0.42094048  0.105115494
## 258      11 2.124207e-04 -0.065587787 -0.361970840 -0.44425840 -0.610276370
## 207     216 2.362314e-04 -0.020869415 -0.023319735  0.17701180  0.018502482
## 159      24 3.033956e-04  0.200786726  0.230752577  0.40464520 -0.226680065
## 129      36 3.108494e-04  0.027757342  0.134239050 -0.40119400 -0.055402968
## 106     347 3.572864e-04 -0.083153224 -0.014176699  0.05859454 -0.096585851
## 63       41 3.757564e-04 -0.101614849 -0.184647452 -0.31976299 -0.193379791
## 9        12 4.079189e-04 -0.060900733  0.289701008  0.55954539  0.454995999
## 98       22 4.624817e-04  0.124455851  0.115388557  0.32853903 -0.408972834
## 68       56 5.028287e-04 -0.152526949 -0.202113856  0.04690075  0.232480380
## 75       31 5.341428e-04 -0.210631210 -0.403613333 -0.14112626  0.052279766
## 235      83 7.183803e-04 -0.079473533 -0.267397384 -0.01893051 -0.023778923
##             p.T0         p.T1         p.T3         p.T5     s.dist         SD
## 86  9.298188e-05 8.894863e-01 1.285942e-16 1.117072e-06 0.29668527 0.17126416
## 220 3.301052e-03 2.343551e-01 6.107413e-01 5.377476e-10 0.64650806 0.36680921
## 123 4.800734e-06 1.220242e-05 3.591953e-03 6.351827e-01 1.07757416 0.29173835
## 84  4.982512e-01 8.901735e-04 2.385253e-08 1.848487e-02 0.55646154 0.16408269
## 140 1.944471e-01 2.115895e-03 1.611112e-09 6.720825e-01 0.61575490 0.22074575
## 70  2.214271e-02 2.245769e-01 5.850156e-08 2.950074e-03 0.31614520 0.15291407
## 141 9.526447e-01 3.823763e-02 2.247749e-10 7.783011e-01 0.90901267 0.41808397
## 81  1.209461e-01 2.945082e-03 3.122902e-03 4.055914e-06 0.40049877 0.20306754
## 24  4.211332e-02 1.993734e-01 5.221298e-05 6.510541e-05 0.25317861 0.14096914
## 11  1.392940e-01 6.027813e-01 6.720389e-03 8.245554e-07 0.54030617 0.25585477
## 7   9.824158e-02 3.336554e-03 5.016482e-03 9.884612e-05 0.18593295 0.02909587
## 89  1.120034e-01 8.947827e-03 3.954684e-05 3.197386e-01 0.05154869 0.02915905
## 35  4.304148e-02 9.841010e-03 4.809219e-04 1.529067e-01 0.54576923 0.28352034
## 104 2.093458e-02 5.785368e-01 1.705592e-02 2.197301e-04 0.47484613 0.24911038
## 135 1.312085e-01 7.815703e-02 5.000573e-06 2.314300e-01 0.36087302 0.10670435
## 8   3.960780e-01 9.501939e-01 1.871091e-06 1.245857e-01 0.23873592 0.09873408
## 194 1.481305e-01 3.301356e-04 7.489830e-01 2.199834e-03 0.38206602 0.22018263
## 60  9.128831e-02 2.910834e-01 4.973455e-01 1.898931e-05 0.39351669 0.18406062
## 151 2.210683e-01 4.330029e-01 2.844622e-05 2.960273e-01 0.45783379 0.26227877
## 258 7.064343e-01 3.763694e-02 1.072819e-02 4.563684e-04 0.83971865 0.22801843
## 207 5.972207e-01 5.548939e-01 7.362723e-06 6.394472e-01 0.18070656 0.09474534
## 159 8.862669e-02 5.036842e-02 5.996002e-04 5.457015e-02 0.55559306 0.26820995
## 129 7.732005e-01 1.633868e-01 3.096042e-05 5.651398e-01 0.42757070 0.23174651
## 106 7.812211e-03 6.502061e-01 6.088037e-02 2.002343e-03 0.14098789 0.07141341
## 63  2.602586e-01 4.077974e-02 3.954384e-04 3.215156e-02 0.42902724 0.09000417
## 9   7.149059e-01 8.227193e-02 7.892333e-04 6.347745e-03 0.77958192 0.27158384
## 98  3.122605e-01 3.488260e-01 7.637356e-03 8.970638e-04 0.55136236 0.31498546
## 68  4.835485e-02 8.892479e-03 5.438278e-01 2.618454e-03 0.34693125 0.19911913
## 75  4.238807e-02 1.003130e-04 1.738658e-01 6.144330e-01 0.47949872 0.18826822
## 235 2.107638e-01 2.536422e-05 7.656323e-01 7.080773e-01 0.28060863 0.11661617
##     p.adjustMANOVA
## 86    1.316820e-20
## 220   7.673273e-08
## 123   3.586855e-07
## 84    3.586855e-07
## 140   3.586855e-07
## 70    3.586855e-07
## 141   5.134675e-07
## 81    6.027803e-07
## 24    9.568656e-07
## 11    2.494163e-05
## 7     4.491157e-05
## 89    7.478904e-05
## 35    4.108181e-04
## 104   6.258259e-04
## 135   6.258259e-04
## 8     6.258259e-04
## 194   1.175448e-03
## 60    2.498966e-03
## 151   2.761469e-03
## 258   2.761469e-03
## 207   2.924770e-03
## 159   3.513949e-03
## 129   3.513949e-03
## 106   3.870603e-03
## 63    3.907867e-03
## 9     4.079189e-03
## 98    4.453527e-03
## 68    4.669124e-03
## 75    4.788866e-03
## 235   6.225963e-03

rownames(top) <- top[,1]
top <- top[,4:7]
colfunc <- colorRampPalette(c("blue", "white", "red"))
colnames(top) <- gsub("s\\.","",colnames(top))

heatmap.2(  as.matrix(top), col=colfunc(25), 
    scale="none",Colv=FALSE,trace="none", dendrogram="row",
    margins = c(10,30), cexCol=0.5 , cexRow=0.5, main="Top genes sets")

unlink("mapman_report.html")
capture.output(
    mitch_report(res,outfile=paste("mapman_report.html"))
    , file = "/dev/null", append = FALSE,
    type = c("output", "message"), split = FALSE)

## Dataset saved as " /tmp/Rtmpn52aep/mapman_report.RData ".

## 
## 
## processing file: mitch.Rmd

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output file: /mnt/bfx4/tohidul/timecourse2/dge/mitch.knit.md
## 
## 
## Output created: /tmp/Rtmpn52aep/mitch_report.html

Experimental DE

Is it possible identify differences between treated and untreated plants correcting for timepoint.

ss <- data.frame(colnames(xx))
rownames(ss) <- ss[,1]
ss$trt <- as.numeric(grepl("ANDP",ss[,1]))
ss$time <- sapply(strsplit(rownames(ss),"-"),"[[",2)
ss$time <- sapply(strsplit(ss$time,""),"[[",1)
ss$time <- as.numeric(ss$time)
ss[,1]=NULL

y <- round(xx)
dds <- DESeqDataSetFromMatrix(countData=y, colData = ss, design = ~ time + trt)

## converting counts to integer mode

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

dds <- DESeq(dds)

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

de <- DESeq2::results(dds)
de <- de[order(de$pvalue),]
head(de,20)

## log2 fold change (MLE): trt 
## Wald test p-value: trt 
## DataFrame with 20 rows and 6 columns
##            baseMean log2FoldChange     lfcSE      stat      pvalue        padj
##           <numeric>      <numeric> <numeric> <numeric>   <numeric>   <numeric>
## AT4G19760    69.047      -5.593301  1.029799  -5.43145 5.58981e-08 0.000891266
## AT5G40010   489.492      -1.722468  0.320263  -5.37830 7.51933e-08 0.000891266
## AT4G11430  2704.240      -2.160705  0.441423  -4.89486 9.83738e-07 0.007773497
## AT1G08430   937.865      -2.124806  0.456284  -4.65676 3.21223e-06 0.016526806
## AT1G05000  1535.141      -0.748338  0.161894  -4.62238 3.79365e-06 0.016526806
## ...             ...            ...       ...       ...         ...         ...
## AT4G22610  280.4254      -2.083354  0.523345  -3.98084 6.86717e-05    0.099309
## AT1G18980 3916.1722      -0.803453  0.202606  -3.96559 7.32148e-05    0.099309
## AT5G48430  507.2619       1.169484  0.295432   3.95855 7.54055e-05    0.099309
## AT1G51340 1100.6447      -0.687134  0.175346  -3.91873 8.90166e-05    0.111065
## AT2G27120   10.4281      -4.328410  1.108496  -3.90476 9.43191e-05    0.111796

# MA plot
sig <-subset(de, padj < 0.05 )
GENESUP <- length(up)
GENESDN <- length(dn)
SUBHEADER = paste(GENESUP, "up, ", GENESDN, "down")
ns <-subset(de, padj > 0.05 )
plot(log2(de$baseMean),de$log2FoldChange,
     xlab="log2 basemean", ylab="log2 foldchange",
     pch=19, cex=0.5, col="dark gray",
     main="smear plot")
points(log2(sig$baseMean),sig$log2FoldChange,
       pch=19, cex=0.5, col="red")
mtext(SUBHEADER)

# heatmap
yn <- y/colSums(y)*1000000
yf <- yn[which(rownames(yn) %in% rownames(de)[1:50]),]

mycols <- gsub("0","yellow",ss$trt)
mycols <- gsub("1","orange",mycols)

colfunc <- colorRampPalette(c("blue", "white", "red"))
heatmap.2(  as.matrix(yf), col=colfunc(25),scale="row", ColSideColors =mycols ,trace="none", 
    margins = c(10,10), cexRow=0.6, main="Top 50 genes by p-val")
mtext("yellow=ctrl, orange=trt")

Exclude time = 0. This looks a lot better.

ss <- ss[which(ss$time!=0),]
y <- round(xx)
y <- y[,grep("-0R",colnames(y),invert=T)]
dds <- DESeqDataSetFromMatrix(countData=y, colData = ss, design = ~ time + trt)

## converting counts to integer mode

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

dds <- DESeq(dds)

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

de <- DESeq2::results(dds)
de <- de[order(de$pvalue),]
head(de,20)

## log2 fold change (MLE): trt 
## Wald test p-value: trt 
## DataFrame with 20 rows and 6 columns
##             baseMean log2FoldChange     lfcSE      stat      pvalue        padj
##            <numeric>      <numeric> <numeric> <numeric>   <numeric>   <numeric>
## AT5G38910  516.48341       -3.86851  0.439297  -8.80613 1.29536e-18 3.17906e-14
## AT3G02565    7.25178      -21.66341  2.557671  -8.46997 2.45448e-17 3.01189e-13
## AT1G21280   38.33933      -24.14791  2.965025  -8.14425 3.81634e-16 3.12202e-12
## AT5G03185   13.76226      -23.41036  2.996329  -7.81301 5.58367e-15 3.42586e-11
## AT1G61560 2206.79235       -1.18517  0.170364  -6.95667 3.48417e-12 1.71017e-08
## ...              ...            ...       ...       ...         ...         ...
## AT2G46750   1119.069      -1.340502  0.223692  -5.99264 2.06463e-09 3.16688e-06
## AT4G21680    627.616       2.264159  0.386264   5.86169 4.58180e-09 6.61450e-06
## AT5G53590   2211.109      -0.716135  0.125751  -5.69488 1.23457e-08 1.67637e-05
## AT3G62270   5457.533      -0.683444  0.120334  -5.67958 1.35024e-08 1.67637e-05
## AT1G18100    472.492      -1.697066  0.298907  -5.67758 1.36612e-08 1.67637e-05

# MA plot
sig <-subset(de, padj < 0.05 )
GENESUP <- length(up)
GENESDN <- length(dn)
SUBHEADER = paste(GENESUP, "up, ", GENESDN, "down")
ns <-subset(de, padj > 0.05 )
plot(log2(de$baseMean),de$log2FoldChange,
     xlab="log2 basemean", ylab="log2 foldchange",
     pch=19, cex=0.5, col="dark gray",
     main="smear plot")
points(log2(sig$baseMean),sig$log2FoldChange,
       pch=19, cex=0.5, col="red")
mtext(SUBHEADER)

# heatmap
yn <- y/colSums(y)*1000000
yf <- yn[which(rownames(yn) %in% rownames(de)[1:50]),]

mycols <- gsub("0","yellow",ss$trt)
mycols <- gsub("1","orange",mycols)

colfunc <- colorRampPalette(c("blue", "white", "red"))
heatmap.2(  as.matrix(yf), col=colfunc(25),scale="row", ColSideColors =mycols ,trace="none",
    margins = c(10,10), cexRow=0.6, main="Top 50 genes by p-val")
mtext("yellow=ctrl, orange=trt")

# enrichment

m <- mitch_import(as.data.frame(de),DEtype="DESeq2", geneTable=gt)

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

## Note: Mean no. genes in input = 32833

## Note: no. genes in output = 26844

## Note: estimated proportion of input genes in output = 0.818

capture.output(
    res <- mitch_calc(x=m,genesets=genesets,priority="significance")
    , file = "/dev/null", append = FALSE,
    type = c("output", "message"), split = FALSE)

## Note: When prioritising by significance (ie: small
##             p-values), large effect sizes might be missed.

top <- subset(res$enrichment_result,p.adjustANOVA<0.05)
head(top,30)

##                                                                                 set
## 9                                                                 CELL_ORGANISATION
## 152                                 PROTEIN_SYNTHESIS_TRANSFER_RNA_NUCLEUS_TRNA-PRO
## 168                       PS_LIGHTREACTION_PHOTOSYSTEM_II_PSII_POLYPEPTIDE_SUBUNITS
## 83                                    MISC_UDP_GLUCOSYL_AND_GLUCORONYL_TRANSFERASES
## 169                         PS_LIGHTREACTION_PHOTOSYSTEM_I_PSI_POLYPEPTIDE_SUBUNITS
## 10                                                           CELL_VESICLE_TRANSPORT
## 13                                            CELL_WALL_CELL_WALL_PROTEINS_AGPS_AGP
## 141                                                 PROTEIN_SYNTHESIS_RIBOSOMAL_RNA
## 163                                 PROTEIN_TARGETING_SECRETORY_PATHWAY_UNSPECIFIED
## 97                                   NOT_ASSIGNED_NO_ONTOLOGY_GLYCINE_RICH_PROTEINS
## 254                             SIGNALLING_RECEPTOR_KINASES_LEUCINE_RICH_REPEAT_III
## 76                                                  MISC_MYROSINASES-LECTIN-JACALIN
## 166                                                        PS_LIGHTREACTION_NADH_DH
## 11                                                    CELL_WALL_CELLULOSE_SYNTHESIS
## 14                                                CELL_WALL_CELL_WALL_PROTEINS_HRGP
## 167                                          PS_LIGHTREACTION_PHOTOSYSTEM_II_LHC-II
## 87                       MITOCHONDRIAL_ELECTRON_TRANSPORT_/_ATP_SYNTHESIS_F1-ATPASE
## 184               RNA_REGULATION_OF_TRANSCRIPTION_ARF,_AUXIN_RESPONSE_FACTOR_FAMILY
## 295                                                              TRANSPORT_SULPHATE
## 12                                 CELL_WALL_CELLULOSE_SYNTHESIS_CELLULOSE_SYNTHASE
## 236                                             SECONDARY_METABOLISM_SIMPLE_PHENOLS
## 86          MITOCHONDRIAL_ELECTRON_TRANSPORT_/_ATP_SYNTHESIS_CYTOCHROME_C_REDUCTASE
## 165                         PS_LIGHTREACTION_CYCLIC_ELECTRON_FLOW-CHLORORESPIRATION
## 94                           NOT_ASSIGNED_NO_ONTOLOGY_DC1_DOMAIN_CONTAINING_PROTEIN
## 88  MITOCHONDRIAL_ELECTRON_TRANSPORT_/_ATP_SYNTHESIS_NADH-DH_LOCALISATION_NOT_CLEAR
## 284                      TRANSPORT_METABOLITE_TRANSPORTERS_AT_THE_ENVELOPE_MEMBRANE
## 99   NOT_ASSIGNED_NO_ONTOLOGY_LATE_EMBRYOGENESIS_ABUNDANT_DOMAIN-CONTAINING_PROTEIN
## 132                                    PROTEIN_POSTRANSLATIONAL_MODIFICATION_KINASE
##     setSize       pANOVA     s.dist p.adjustANOVA
## 9       374 4.066446e-09  0.1767731  8.590106e-07
## 152      48 5.784583e-09  0.4855428  8.590106e-07
## 168      45 9.078989e-08 -0.4602452  8.988199e-06
## 83      171 8.294038e-07  0.2182477  6.158323e-05
## 169      18 3.219571e-06 -0.6337550  1.912425e-04
## 10      171 1.137391e-05  0.1943848  5.630085e-04
## 13       42 1.480581e-05  0.3861386  6.281893e-04
## 141      15 2.698769e-05 -0.6258278  9.729214e-04
## 163      80 2.948247e-05  0.2700232  9.729214e-04
## 97       76 6.436930e-05 -0.2650027  1.911768e-03
## 254      44 8.067406e-05  0.3433887  2.178200e-03
## 76       56 1.653858e-04 -0.2908898  4.093299e-03
## 166      12 1.979078e-04 -0.6203662  4.521433e-03
## 11       14 2.797598e-04  0.5607422  5.934904e-03
## 14       16 6.166044e-04 -0.4943576  1.220877e-02
## 167      14 7.347597e-04 -0.5210745  1.363898e-02
## 87       21 1.147778e-03 -0.4097958  1.920640e-02
## 184      17 1.164024e-03  0.4548688  1.920640e-02
## 295      12 1.826408e-03 -0.5196097  2.733842e-02
## 12       28 1.840971e-03  0.3400102  2.733842e-02
## 236      18 2.341695e-03  0.4142333  3.311826e-02
## 86       11 2.731481e-03 -0.5217016  3.568347e-02
## 165      12 2.844955e-03 -0.4974284  3.568347e-02
## 94      112 2.883513e-03  0.1629007  3.568347e-02
## 88       34 3.232905e-03 -0.2917414  3.827107e-02
## 284      26 3.350329e-03  0.3323170  3.827107e-02
## 99       10 4.018865e-03 -0.5252888  4.360700e-02
## 132      22 4.111097e-03  0.3533463  4.360700e-02

bp <- top$s.dist
names(bp) <- top$set

bp <- bp[order(bp)]
par(mar=c(5,35,5,0))
barplot(bp,horiz=TRUE,las=2,xlab="S score",cex.names=0.7,main="pathway enrichment")
mtext("Top sets FDR<0.05")

unlink("mapman_report2.html")
capture.output(
    mitch_report(res,outfile=paste("mapman_report2.html"))
    , file = "/dev/null", append = FALSE,
    type = c("output", "message"), split = FALSE)

## Dataset saved as " /tmp/Rtmpn52aep/mapman_report2.RData ".

## 
## 
## processing file: mitch.Rmd

## Contour plot does not apply to unidimensional analysis.

## output file: /mnt/bfx4/tohidul/timecourse2/dge/mitch.knit.md

## 
## Output created: /tmp/Rtmpn52aep/mitch_report.html

Session information

So you know what version of R and packages was used.

sessionInfo()

## R version 4.0.2 (2020-06-22)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
## 
## 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       
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] pkgload_1.1.0               GGally_2.0.0               
##  [3] ggplot2_3.3.2               beeswarm_0.2.3             
##  [5] gtools_3.8.2                tibble_3.0.3               
##  [7] dplyr_1.0.2                 echarts4r_0.3.2            
##  [9] mitch_1.0.8                 DESeq2_1.28.1              
## [11] SummarizedExperiment_1.18.2 DelayedArray_0.14.1        
## [13] matrixStats_0.57.0          Biobase_2.48.0             
## [15] GenomicRanges_1.40.0        GenomeInfoDb_1.24.2        
## [17] IRanges_2.22.2              S4Vectors_0.26.1           
## [19] BiocGenerics_0.34.0         gplots_3.1.0               
## [21] reshape2_1.4.4             
## 
## loaded via a namespace (and not attached):
##  [1] bitops_1.0-6           bit64_4.0.5            RColorBrewer_1.1-2    
##  [4] progress_1.2.2         rprojroot_1.3-2        tools_4.0.2           
##  [7] backports_1.1.10       R6_2.4.1               KernSmooth_2.23-17    
## [10] DBI_1.1.0              colorspace_1.4-1       withr_2.3.0           
## [13] prettyunits_1.1.1      tidyselect_1.1.0       gridExtra_2.3         
## [16] bit_4.0.4              compiler_4.0.2         desc_1.2.0            
## [19] labeling_0.3           caTools_1.18.0         scales_1.1.1          
## [22] genefilter_1.70.0      stringr_1.4.0          digest_0.6.25         
## [25] rmarkdown_2.4          XVector_0.28.0         pkgconfig_2.0.3       
## [28] htmltools_0.5.0        fastmap_1.0.1          highr_0.8             
## [31] htmlwidgets_1.5.2      rlang_0.4.7            RSQLite_2.2.1         
## [34] shiny_1.5.0            farver_2.0.3           generics_0.0.2        
## [37] jsonlite_1.7.1         BiocParallel_1.22.0    RCurl_1.98-1.2        
## [40] magrittr_1.5           GenomeInfoDbData_1.2.3 Matrix_1.2-18         
## [43] Rcpp_1.0.5             munsell_0.5.0          lifecycle_0.2.0       
## [46] stringi_1.5.3          yaml_2.2.1             MASS_7.3-53           
## [49] zlibbioc_1.34.0        plyr_1.8.6             grid_4.0.2            
## [52] blob_1.2.1             promises_1.1.1         crayon_1.3.4          
## [55] lattice_0.20-41        splines_4.0.2          annotate_1.66.0       
## [58] hms_0.5.3              locfit_1.5-9.4         knitr_1.30            
## [61] pillar_1.4.6           geneplotter_1.66.0     XML_3.99-0.5          
## [64] glue_1.4.2             evaluate_0.14          vctrs_0.3.4           
## [67] httpuv_1.5.4           testthat_2.3.2         gtable_0.3.0          
## [70] purrr_0.3.4            reshape_0.8.8          assertthat_0.2.1      
## [73] xfun_0.18              mime_0.9               xtable_1.8-4          
## [76] later_1.1.0.1          survival_3.2-7         pbmcapply_1.5.0       
## [79] AnnotationDbi_1.50.3   memoise_1.1.0          ellipsis_0.3.1

References

Bray NL, Pimentel H, Melsted P, Pachter L. Near-optimal probabilistic RNA-seq quantification [published correction appears in Nat Biotechnol. 2016 Aug 9;34(8):888]. Nat Biotechnol. 2016;34(5):525-527. doi:10.1038/nbt.3519

Jiang H, Lei R, Ding SW, Zhu S. Skewer: a fast and accurate adapter trimmer for next-generation sequencing paired-end reads. BMC Bioinformatics. 2014;15:182. Published 2014 Jun 12. doi:10.1186/1471-2105-15-182

Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. doi:10.1186/s13059-014-0550-8

Kaspi A, Ziemann M. mitch: multi-contrast pathway enrichment for multi-omics and single-cell profiling data. BMC Genomics. 2020;21(1):447. Published 2020 Jun 29. doi:10.1186/s12864-020-06856-9