RNAseq processing (1457)

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  1. construct DESeqDataSet from Matrix

    library("AnnotationDbi")
library("clusterProfiler")
library("ReactomePA")
library("org.Hs.eg.db")
library(DESeq2)
library(gplots)
library(ggplot2)
library(ggrepel)
setwd("/home/jhuang/DATA/Data_Samira_RNAseq/results/featureCounts/")

#cut -f2- merged_gene_counts.txt > merged_gene_counts_2.txt
d.raw<- read.delim2("merged_gene_counts.txt",sep="\t", header=TRUE, row.names=1)
colnames(d.raw)<- c("gene_name","TCR9_r3","X1457_r1","mock_r3","M2_r2","delta9H_r1","delta9H_r3","M2_r3","X1457_r2","M10_r3","TCR9_r1","M1_r1","TCR9_r2","M1_r2","X1457_r3","M10_r2","M2_r1","mock_r2","delta9H_r2","M1_r3","mock_r1","M10_r1")
col_order <- c("gene_name","mock_r1","mock_r2","mock_r3","M1_r1","M1_r2","M1_r3","M2_r1","M2_r2","M2_r3","M10_r1","M10_r2","M10_r3","X1457_r1","X1457_r2","X1457_r3","TCR9_r1","TCR9_r2","TCR9_r3","delta9H_r1","delta9H_r2","delta9H_r3")
reordered.raw <- d.raw[,col_order]
reordered.raw$gene_name <- NULL

#NOTE that we should d instead of d.raw!!!!!!
d <- reordered.raw[rowSums(reordered.raw>3)>2,]

condition = as.factor(c("mock","mock","mock","M1","M1","M1","M2","M2","M2","M10","M10","M10","X1457","X1457","X1457","TCR9","TCR9","TCR9","delta9H","delta9H","delta9H"))
ids = as.factor(c("mock_r1","mock_r2","mock_r3","M1_r1","M1_r2","M1_r3","M2_r1","M2_r2","M2_r3","M10_r1","M10_r2","M10_r3","X1457_r1","X1457_r2","X1457_r3","TCR9_r1","TCR9_r2","TCR9_r3","delta9H_r1","delta9H_r2","delta9H_r3"))
replicate = as.factor(c("r1","r2","r3","r1","r2","r3","r1","r2","r3","r1","r2","r3","r1","r2","r3","r1","r2","r3","r1","r2","r3"))

#Note that we need d
cData = data.frame(row.names=colnames(d), condition=condition, replicate=replicate, ids=ids)
dds<-DESeqDataSetFromMatrix(countData=d, colData=cData, design=~condition)  #batch+

> sizeFactors(dds)
NULL
> dds <- estimateSizeFactors(dds)
> sizeFactors(dds)
   mock_r1    mock_r2    mock_r3      M1_r1      M1_r2      M1_r3      M2_r1 
 1.1510294  1.0108629  1.2046637  0.9219507  1.2912217  1.0233951  1.1781932 
     M2_r2      M2_r3     M10_r1     M10_r2     M10_r3   X1457_r1   X1457_r2 
 1.0286656  1.1274057  0.8521032  0.9723604  0.7937256  0.8869522  1.0276279 
  X1457_r3    TCR9_r1    TCR9_r2    TCR9_r3 delta9H_r1 delta9H_r2 delta9H_r3 
 0.8798504  1.4702299  0.9617160  1.2175588  0.7935592  0.8016998  1.0166897 

bamCoverage --bam ./STAR/V_8_2_4_p600_d3_DonorIAligned.sortedByCoord.out.bam -o bigWigs/ctrl_d3_DonorI_norm.bw --binSize 10 --scaleFactor 0.8182619037059573 --effectiveGenomeSize 2913022398
bamCoverage --bam ./STAR/V_8_2_3_p600_d3_DonorIIAligned.sortedByCoord.out.bam -o bigWigs/ctrl_d3_DonorII_norm.bw --binSize 10 --scaleFactor 1.230524791752137 --effectiveGenomeSize 2913022398
bamCoverage --bam ./STAR/V_8_2_4_p600_d8_DonorIAligned.sortedByCoord.out.bam -o bigWigs/ctrl_d8_DonorI_norm.bw --binSize 10 --scaleFactor 0.9406161731990421 --effectiveGenomeSize 2913022398
bamCoverage --bam ./STAR/V_8_2_3_p600_d8_DonorIIAligned.sortedByCoord.out.bam -o bigWigs/ctrl_d8_DonorII_norm.bw --binSize 10 --scaleFactor 1.5944164810367278 --effectiveGenomeSize 2913022398

bamCoverage --bam ./STAR/V_8_4_2_p602_d3_DonorIAligned.sortedByCoord.out.bam -o bigWigs/LT_d3_DonorI_norm.bw --binSize 10 --scaleFactor 1.1691469144166922 --effectiveGenomeSize 2913022398
bamCoverage --bam ./STAR/V_8_4_2_p602_d3_DonorIIAligned.sortedByCoord.out.bam -o bigWigs/LT_d3_DonorII_norm.bw --binSize 10 --scaleFactor 0.9627956504743693 --effectiveGenomeSize 2913022398
bamCoverage --bam ./STAR/V_8_4_2_p602_d8_DonorIAligned.sortedByCoord.out.bam -o bigWigs/LT_d8_DonorI_norm.bw --binSize 10 --scaleFactor 0.9685710322875091 --effectiveGenomeSize 2913022398
bamCoverage --bam ./STAR/V_8_4_1_p602_d8_DonorIIAligned.sortedByCoord.out.bam -o bigWigs/LT_d8_DonorII_norm.bw --binSize 10 --scaleFactor 0.7369838699288324 --effectiveGenomeSize 2913022398

bamCoverage --bam ./STAR/V_8_2_4_p605_d3_DonorIAligned.sortedByCoord.out.bam -o bigWigs/LTtr_d3_DonorI_norm.bw --binSize 10 --scaleFactor 0.7650745897995206 --effectiveGenomeSize 2913022398
bamCoverage --bam ./STAR/V_8_2_3_p605_d3_DonorIIAligned.sortedByCoord.out.bam -o bigWigs/LTtr_d3_DonorII_norm.bw --binSize 10 --scaleFactor 1.2072732417906324 --effectiveGenomeSize 2913022398
bamCoverage --bam ./STAR/V_8_2_4_p605_d8_DonorIAligned.sortedByCoord.out.bam -o bigWigs/LTtr_d8_DonorI_norm.bw --binSize 10 --scaleFactor 0.617050461769713 --effectiveGenomeSize 2913022398
bamCoverage --bam ./STAR/V_8_2_3_p605_d8_DonorIIAligned.sortedByCoord.out.bam -o bigWigs/LTtr_d8_DonorII_norm.bw --binSize 10 --scaleFactor 1.1972841763570858 --effectiveGenomeSize 2913022398

bamCoverage --bam ./STAR/K331A_DonorIAligned.sortedByCoord.out.bam -o bigWigs/K331A_DonorI_norm.bw --binSize 10 --scaleFactor 0.5914211756222816 --effectiveGenomeSize 2913022398
bamCoverage --bam ./STAR/K331A_DonorIIAligned.sortedByCoord.out.bam -o bigWigs/K331A_DonorII_norm.bw --binSize 10 --scaleFactor 1.6631219993121327 --effectiveGenomeSize 2913022398

rld <- rlogTransformation(dds)

  2. PCA plots pca_1457

    library(ggplot2)
svg("pca6.svg")
data <- plotPCA(rld, intgroup=c("condition", "replicate"), returnData=TRUE)
percentVar <- round(100 * attr(data, "percentVar"))
ggplot(data, aes(PC1, PC2, color=condition, shape=replicate)) +
  geom_point(size=3) +
  scale_color_manual(values = c("mock" = "grey",
                                "M1"="#1f78b4")) +
  xlab(paste0("PC1: ",percentVar[1],"% variance")) +
  ylab(paste0("PC2: ",percentVar[2],"% variance")) +
  coord_fixed()
dev.off()

png("pca.png", width=800, height=800)
data <- plotPCA(rld, intgroup=c("condition", "replicate"), returnData=TRUE)
percentVar <- round(100 * attr(data, "percentVar"))
ggplot(data, aes(PC1, PC2, color=condition, shape=replicate)) +
  geom_point(size=3) +
  scale_color_manual(values = c("mock" = "darkgrey",
                                "M1"="#a14a1a", "M2"="#33a02c", "M10"="#1f78b4", "X1457"="#e31a1c", "TCR9"="orange", "delta9H"="purple")) +
  xlab(paste0("PC1: ",percentVar[1],"% variance")) +
  ylab(paste0("PC2: ",percentVar[2],"% variance")) +
  coord_fixed()
dev.off()

png("pca2.png", 1200, 800)
plotPCA(rld, intgroup=c("condition"))
dev.off()

data <- plotPCA(rld, intgroup=c("condition", "donor"), returnData=TRUE)
write.csv(data, file="plotPCA_data.csv")
#calculate all PCs including PC3 with the following codes
library(genefilter)
ntop <- 500
rv <- rowVars(assay(rld))
select <- order(rv, decreasing = TRUE)[seq_len(min(ntop, length(rv)))]
mat <- t( assay(rld)[select, ] )
pc <- prcomp(mat)
pc$x[,1:3]
#df_pc <- data.frame(pc$x[,1:3])
df_pc <- data.frame(pc$x)
identical(rownames(data), rownames(df_pc)) #-->TRUE
## define the desired order of row names
#desired_order <- rownames(data)
## sort the data frame by the desired order of row names
#df <- df[match(desired_order, rownames(df_pc)), ]
data$PC1 <- NULL
data$PC2 <- NULL
merged_df <- merge(data, df_pc, by = "row.names")
#merged_df <- merged_df[, -1]
row.names(merged_df) <- merged_df$Row.names
merged_df$Row.names <- NULL  # remove the "name" column
merged_df$name <- NULL
merged_df <- merged_df[, c("PC1","PC2","PC3","PC4","PC5","PC6","PC7","PC8","group","condition","donor")]
#results in 26PCs: merged_df <- merged_df[, c("PC1","PC2","PC3","PC4","PC5","PC6","PC7","PC8","group","condition","donor")]
write.csv(merged_df, file="merged_df_8PCs.csv")
#> summary(pc)    #0.5657 0.2195 0.1347  --> 0.57 0.22 0.13
#Importance of components:
#                           PC1     PC2    PC3    PC4     PC5     PC6     PC7
#Standard deviation     19.4051 12.0878 9.4683 4.5569 4.01016 3.00610 2.71918
#Proportion of Variance  0.5657  0.2195 0.1347 0.0312 0.02416 0.01358 0.01111
#Cumulative Proportion   0.5657  0.7853 0.9200 0.9512 0.97531 0.98889 1.00000

  3. volcano plots X1457_vs_mock

    dds$condition <- relevel(dds$condition, "mock")
dds = DESeq(dds, betaPrior=FALSE)
resultsNames(dds)
clist <- c("M1_vs_mock","M2_vs_mock","M10_vs_mock", "delta9H_vs_mock","TCR9_vs_mock","X1457_vs_mock")

dds$condition <- relevel(dds$condition, "X1457")
dds = DESeq(dds, betaPrior=FALSE)
resultsNames(dds)
clist <- c("M10_vs_X1457", "delta9H_vs_X1457")

library(biomaRt)
listEnsembl()
listMarts()
ensembl <- useEnsembl(biomart = "ensembl", dataset = "hsapiens_gene_ensembl", version="104")
datasets <- listDatasets(ensembl)

listEnsemblArchives()
attributes = listAttributes(ensembl)
attributes[1:25,]

library(dplyr)
library(tidyverse)

for (i in clist) {
#i<-clist[1]
#i<-"M1_vs_mock"
  contrast = paste("condition", i, sep="_")
  res = results(dds, name=contrast)
  res <- res[!is.na(res$log2FoldChange),]
  #geness <- AnnotationDbi::select(edb86, keys = rownames(res), keytype = "GENEID", columns = c("ENTREZID","EXONID","GENEBIOTYPE","GENEID","GENENAME","PROTEINDOMAINSOURCE","PROTEINID","SEQNAME","SEQSTRAND","SYMBOL","TXBIOTYPE","TXID","TXNAME","UNIPROTID"))
  #geness <- AnnotationDbi::select(edb86, keys = rownames(res), keytype = "GENEID", columns = c("GENEID", "ENTREZID", "SYMBOL", "GENENAME","GENEBIOTYPE","TXBIOTYPE","SEQSTRAND","UNIPROTID"))
  # In the ENSEMBL-database, GENEID is ENSEMBL-ID.
  #geness <- AnnotationDbi::select(edb86, keys = rownames(res), keytype = "GENEID", columns = c("GENEID", "SYMBOL", "GENEBIOTYPE"))  #  "ENTREZID", "TXID","TXBIOTYPE","TXSEQSTART","TXSEQEND"
  #geness <- geness[!duplicated(geness$GENEID), ]

  #using getBM replacing AnnotationDbi::select
  #filters = 'ensembl_gene_id' means the records should always have a valid ensembl_gene_ids.
  geness <- getBM(attributes = c('ensembl_gene_id', 'external_gene_name', 'gene_biotype', 'entrezgene_id', 'chromosome_name', 'start_position', 'end_position', 'strand', 'description'),
      filters = 'ensembl_gene_id',
      values = rownames(res), 
      mart = ensembl)
  geness_uniq <- distinct(geness, ensembl_gene_id, .keep_all= TRUE)

  #merge by column by common colunmn name, in the case "GENEID"
  res$ENSEMBL = rownames(res)
  identical(rownames(res), rownames(geness_uniq))
  res_df <- as.data.frame(res)
  geness_res <- merge(geness_uniq, res_df, by.x="ensembl_gene_id", by.y="ENSEMBL")
  dim(geness_res)
  rownames(geness_res) <- geness_res$ensembl_gene_id
  geness_res$ensembl_gene_id <- NULL
  write.csv(as.data.frame(geness_res[order(geness_res$pvalue),]), file = paste(i, "all.txt", sep="-"))
  up <- subset(geness_res, padj<=0.05 & log2FoldChange>=2)
  down <- subset(geness_res, padj<=0.05 & log2FoldChange<=-2)
  write.csv(as.data.frame(up[order(up$log2FoldChange,decreasing=TRUE),]), file = paste(i, "up.txt", sep="-"))
  write.csv(as.data.frame(down[order(abs(down$log2FoldChange),decreasing=TRUE),]), file = paste(i, "down.txt", sep="-"))
}

#for i in "M1_vs_mock" "M2_vs_mock" "M10_vs_mock" "delta9H_vs_mock" "TCR9_vs_mock" "X1457_vs_mock"; do
for i in "M10_vs_X1457" "delta9H_vs_X1457"; do
  # read files to geness_res
  echo "geness_res <- read.csv(file = paste(\"${i}\", \"all.txt\", sep=\"-\"), row.names=1)"

  echo "subset(geness_res, external_gene_name %in% top_g & pvalue < 0.05 & (abs(geness_res\$log2FoldChange) >= 2.0))"
  echo "geness_res\$Color <- \"NS or log2FC < 2.0\""
  echo "geness_res\$Color[geness_res\$pvalue < 0.05] <- \"P < 0.05\""
  echo "geness_res\$Color[geness_res\$padj < 0.05] <- \"P-adj < 0.05\""
  echo "geness_res\$Color[abs(geness_res\$log2FoldChange) < 2.0] <- \"NS or log2FC < 2.0\""
  echo "write.csv(geness_res, \"${i}_with_Category.csv\")"

  # pick top genes for either side of volcano to label
  # order genes for convenience:
  echo "geness_res\$invert_P <- (-log10(geness_res\$pvalue)) * sign(geness_res\$log2FoldChange)"
  echo "top_g <- c()"
  echo "top_g <- c(top_g, \
             geness_res[, 'external_gene_name'][order(geness_res[, 'invert_P'], decreasing = TRUE)[1:100]], \
             geness_res[, 'external_gene_name'][order(geness_res[, 'invert_P'], decreasing = FALSE)[1:100]])"
  echo "top_g <- unique(top_g)"
  echo "geness_res <- geness_res[, -1*ncol(geness_res)]"  # remove invert_P from matrix

  # Graph results
  echo "png(\"${i}.png\",width=1200, height=2000)"
  echo "ggplot(geness_res, \
      aes(x = log2FoldChange, y = -log10(pvalue), \
          color = Color, label = external_gene_name)) + \
      geom_vline(xintercept = c(2.0, -2.0), lty = \"dashed\") + \
      geom_hline(yintercept = -log10(0.05), lty = \"dashed\") + \
      geom_point() + \
      labs(x = \"log2(FC)\", y = \"Significance, -log10(P)\", color = \"Significance\") + \
      scale_color_manual(values = c(\"P < 0.05\"=\"darkgray\",\"P-adj < 0.05\"=\"red\",\"lysosomal\"=\"lightblue\",\"TFEB\"=\"green\",\"lysosomal & TFEB\"=\"cyan\",\"NS or log2FC < 2.0\"=\"gray\"),guide = guide_legend(override.aes = list(size = 4))) + scale_y_continuous(expand = expansion(mult = c(0,0.05))) + \
      geom_text_repel(data = subset(geness_res, external_gene_name %in% top_g & pvalue < 0.05 & (abs(geness_res\$log2FoldChange) >= 2.0)), size = 4, point.padding = 0.15, color = \"black\", min.segment.length = .1, box.padding = .2, lwd = 2) + \
      theme_bw(base_size = 16) + \
      theme(legend.position = \"bottom\")"
  echo "dev.off()"
done

~/Tools/csv2xls-0.4/csv_to_xls.py \
M1_vs_mock-all.txt \
M1_vs_mock-up.txt \
M1_vs_mock-down.txt \
-d$',' -o M1_vs_mock.xls;

  4. clustering the genes and draw heatmap DEGs_heatmap_1457

    up1 <-read.csv(file="./M1_vs_mock-up.txt", header = TRUE, row.names = 1)
up2 <-read.csv(file="./M2_vs_mock-up.txt", header = TRUE, row.names = 1)
up3 <-read.csv(file="./M10_vs_mock-up.txt", header = TRUE, row.names = 1)
up4 <-read.csv(file="./delta9H_vs_mock-up.txt", header = TRUE, row.names = 1)
up5 <-read.csv(file="./TCR9_vs_mock-up.txt", header = TRUE, row.names = 1)
up6 <-read.csv(file="./X1457_vs_mock-up.txt", header = TRUE, row.names = 1)

down1 <-read.csv(file="./M1_vs_mock-down.txt", header = TRUE, row.names = 1)
down2 <-read.csv(file="./M2_vs_mock-down.txt", header = TRUE, row.names = 1)
down3 <-read.csv(file="./M10_vs_mock-down.txt", header = TRUE, row.names = 1)
down4 <-read.csv(file="./delta9H_vs_mock-down.txt", header = TRUE, row.names = 1)
down5 <-read.csv(file="./TCR9_vs_mock-down.txt", header = TRUE, row.names = 1)
down6 <-read.csv(file="./X1457_vs_mock-down.txt", header = TRUE, row.names = 1)

RNASeq.NoCellLine <- assay(rld)
#Option3: as paper described, A heatmap showing expression values of all DEGs which are significant between any pair conditions.
all_genes <- c(rownames(up1),rownames(down1),  rownames(up2),rownames(down2), rownames(up3),rownames(down3), rownames(up4),rownames(down4), rownames(up5),rownames(down5), rownames(up6),rownames(down6))  #5473
all_genes <- unique(all_genes)   #2971
RNASeq.NoCellLine_  <- RNASeq.NoCellLine[all_genes,]
write.csv(as.data.frame(RNASeq.NoCellLine_), file ="significant_gene_expressions.txt")

#clustering methods: "ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or "centroid" (= UPGMC).  pearson or spearman
datamat = RNASeq.NoCellLine_
hr <- hclust(as.dist(1-cor(t(datamat), method="pearson")), method="complete")
hc <- hclust(as.dist(1-cor(datamat, method="spearman")), method="complete")
mycl = cutree(hr, h=max(hr$height)/1.08)
mycol = c("YELLOW", "DARKBLUE", "DARKORANGE", "DARKMAGENTA", "DARKCYAN", "DARKRED",  "MAROON", "DARKGREEN", "LIGHTBLUE", "PINK", "MAGENTA", "LIGHTCYAN","LIGHTGREEN", "BLUE", "ORANGE", "CYAN", "RED", "GREEN");

mycol = mycol[as.vector(mycl)]
#sampleCols <- rep('GREY',ncol(RNASeq.NoCellLine_))
#names(sampleCols) <- c("mock_r1", "mock_r2", "mock_r3", "mock_r4", "WAP_r1", "WAP_r2",  "WAP_r3", "WAP_r4", "WAC_r1","WAC_r2")
#sampleCols[substr(colnames(RNASeq.NoCellLine_),1,4)=='mock'] <- 'GREY'
#sampleCols[substr(colnames(RNASeq.NoCellLine_),1,3)=='WAP'] <- 'RED'
#sampleCols[substr(colnames(RNASeq.NoCellLine_),1,3)=='dM_'] <- 'CYAN'
#sampleCols[substr(colnames(RNASeq.NoCellLine_),1,3)=='dP_'] <- 'BLUE'
#sampleCols[substr(colnames(RNASeq.NoCellLine_),1,3)=='WAC'] <- 'GREEN'
png("DEGs_heatmap.png", width=900, height=1010)
heatmap.2(as.matrix(datamat),Rowv=as.dendrogram(hr),Colv = NA, dendrogram = 'row',
            scale='row',trace='none',col=bluered(75), 
            RowSideColors = mycol, labRow="", srtCol=30, keysize=0.72, cexRow = 2, cexCol = 1.4)
dev.off()

#### cluster members #####
write.csv(names(subset(mycl, mycl == '1')),file='cluster1_YELLOW.txt')
write.csv(names(subset(mycl, mycl == '2')),file='cluster2_DARKBLUE.txt') 
write.csv(names(subset(mycl, mycl == '3')),file='cluster3_DARKORANGE.txt')  

~/Tools/csv2xls-0.4/csv_to_xls.py \
cluster1_YELLOW.txt \
cluster2_DARKBLUE.txt \
cluster3_DARKORANGE.txt \
-d$',' -o gene_culsters.xls;

~/Tools/csv2xls-0.4/csv_to_xls.py \
significant_gene_expressions.txt \
-d',' -o DEGs_heatmap_data.xls;

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