RNAseq processing (1457)

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

   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

   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

   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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