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atriumization_source_code.R
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# Title : the R script to atriumization on heart regeneration
# Objective : to find the initiation factor leading to the start of heart regeneration
# Created by: Yisong Zhen
# Created on: 11/6/2019
#----
pkgs <- c( 'tidyverse','Rsubread','org.Mm.eg.db','edgeR',
'limma', 'DESeq2','DESeq','genefilter','grid',
'openxlsx','pheatmap','gridExtra','ggrepel',
'annotate','clusterProfiler', 'cowplot',
'RColorBrewer','affy','ComplexHeatmap',
'cluster','factoextra','RDAVIDWebService',
'Rsamtools', 'devtools','mouse4302.db',
'GenomicAlignments', 'magrittr', 'network',
'reshape2', 'stringr', 'rtracklayer','mgu74a.db',
'TxDb.Mmusculus.UCSC.mm10.knownGene', 'GGally',
'Mus.musculus', 'FactoMineR',
'BSgenome.Mmusculus.UCSC.mm10')
load.lib <- lapply(pkgs, require, character.only = TRUE)
#---
# define data and file deposition layout
# this maybe the constant
# absolute path
# <annotation>
# ----genome : the raw genome fasta seqeunces
# ----index : rsubread indexed genome data
# ----annot : all the genome annotation data
# <project>
# ----<project name>
# ----rawdata : deposition SRA data or/and fastq.gz data
# ----rdata : save the processed .Rdata from the R procedure
# ----rsubread : save the bam aligned RNA sequencing data
#---
data.root.dir <- '/home/zhenyisong/cloudatlas/projects'
annotation.root.dir <- '/home/zhenyisong/cloudatlas/annotation'
genome.annotation.dir <- paste0(annotation.root.dir,'/annot') %>% file.path()
rsubread.index.dir <- paste0(annotation.root.dir, '/index') %>% file.path()
genome.fasta.file <- paste0(annotation.root.dir,
'/genome/Mus_musculus/Ensembl/GRCm38/Sequence/WholeGenomeFasta/genome.fa')
raw.data.dir <- paste0(data.root.dir, '/GSE64403')
#change the file tag index and gtf files downloading
rsubread.index.file <- paste0( rsubread.index.dir, '/mm10.00.b.tab')
mRNA.GRCm38.GTF.file <- paste0( genome.annotation.dir,'/igenomes.mm10.ensembl.gtf') %>%file.path()
# data output
onedrive.path <- paste0('~/onedrive/fuwaihospital/',
'publication/2020papers/atriumization')
"
load the processed RNA-seq data
"
setwd('../data/rdata')
load('GSE64403.Rdata')
"
microarray data path in my local PC
"
GSE1479.raw.path <- 'data/raw/GSE1479'
GSE775.raw.path <- 'data/raw/GSE775'
#---
# expand the data deposition layout
# define the dir structure or file names
#----
create.a.project.layout <- function(raw.path) {
project.raw.data.dir <- paste0(raw.data.dir, '/data') %>% file.path()
project.output.dir <- paste0(raw.data.dir,'/rsubread') %>% file.path()
project.Rdata.dir <- paste0(raw.data.dir, '/rdata') %>% file.path()
if (! dir.exists(project.Rdata.dir)) dir.create( project.Rdata.dir,
showWarnings = FALSE, recursive = TRUE)
project.layout <- list(raw.data.dir = project.raw.data.dir,
output.dir = project.output.dir,
rdata.dir = project.Rdata.dir)
return(project.layout)
}
generate.output.filename <- function(double.ends.rawdata.dir, output.dir) {
project.files <- double.ends.rawdata.dir %>%
list.files( pattern = 'SRR.*.fastq.gz$',
all.files = FALSE, full.names = TRUE,
recursive = FALSE, ignore.case = FALSE, include.dirs = F)
read.1.files <- project.files[grep('_1',project.files)]
read.2.files <- project.files[grep('_2',project.files)]
project.output.filenames <- basename(read.1.files) %>%
sub(pattern = '_1.fastq.gz', replacement = '') %>%
paste0(output.dir,'/', . ,'.bam')
if (! dir.exists(output.dir)) dir.create(output.dir, showWarnings = FALSE, recursive = TRUE)
files.layout <- list(output = project.output.filenames, readfile1 = read.1.files, readfile2 = read.2.files )
return(files.layout)
}
create.rsubread.index <- function(subread.index.dir, subread.file, base.string = 'mm10') {
if (! dir.exists(subread.index.dir)) dir.create(subread.index.dir,
showWarnings = FALSE, recursive = TRUE)
setwd(subread.index.dir)
if (! file.exists(subread.file)) buildindex( basename = base.string,
indexSplit = TRUE,
memory = 4000,
reference = genome.fasta.file )
return(base.string)
}
'
project.layout <- create.a.project.layout(raw.data.dir)
filenames.layout <- generate.output.filename(project.layout$raw,project.layout$output)
rsubread.basename <- create.rsubread.index(rsubread.index.dir, rsubread.index.file)
'
align.raw.data <- function(raw.data.layout, index.dir, index.file) {
project.layout <- create.a.project.layout(raw.data.layout)
filenames.layout <- generate.output.filename(project.layout$raw,project.layout$output)
rsubread.basename <- create.rsubread.index(index.dir, index.file)
align( index = rsubread.basename,
readfile1 = filenames.layout$readfile1,
readfile2 = filenames.layout$readfile2,
input_format = 'gzFASTQ',
type = 'rna',
output_file = filenames.layout$output,
output_format = 'BAM',
PE_orientation = 'fr',
nthreads = 2,
indels = 1,
maxMismatches = 3,
phredOffset = 33,
unique = T )
}
get.featureCount.result <- function(raw.data.layout,GTF.file) {
project.layout <- create.a.project.layout(raw.data.layout)
filenames.layout <- generate.output.filename(project.layout$raw,project.layout$output)
featureCount.result <- featureCounts( filenames.layout$output, useMetaFeatures = TRUE,
countMultiMappingReads = FALSE,
strandSpecific = 0,
isPairedEnd = TRUE,
requireBothEndsMapped = TRUE,
autosort = TRUE,
nthreads = 2,
annot.ext = GTF.file,
isGTFAnnotationFile = TRUE,
GTF.featureType = 'exon',
GTF.attrType = 'gene_id',
allowMultiOverlap = TRUE)
return(featureCount.result)
}
#---
# Figure 1
#---
#----
# get the atrium specific genes from microarray data
# original data processing code is in the script 'GSE1479_Microarray.R'
# at the fuwai dir published_paper?
#----
get.microarray.exprs <- function(data.path) {
exprs.data <- data.path %>%
ReadAffy(celfile.path = .) %>%
rma() %>% exprs()
return(exprs.data)
}
secure.GSE1479.PCA <- function(GSE1479.exprs) {
complete.data.pca <- prcomp(t(GSE1479.exprs))
return(complete.data.pca)
}
# Figure.1 sample size = 36
get.PCA.plot <- function(GSE1479.exprs) {
pca.result <- secure.GSE1479.PCA(GSE1479.exprs)
whole.av.attr <- factor( c( rep(1,6),rep(2:3,5, each = 3)),
levels = 1:3, labels = c('heart','ventricle','atrium'))
GSE1479_pca_ggplot <- data.frame( PC1 = pca.result$x[,1],
PC2 = pca.result$x[,2],
AV = whole.av.attr) %>%
ggplot() +
geom_point( aes(x = PC1, y = PC2, color = AV), size = 2.5) +
geom_text_repel( aes(x = PC1, y = PC2,
label = GSE1479.colnames),
size = 2.0) +
scale_colour_manual( name = 'tissue types',
values = c( '#F5D547','#DB3069','#1446A0'),
labels = c( 'heart','ventricle','atrium')) +
theme_classic() +
theme( aspect.ratio = 1,
legend.position = c(0.9, 0.5),
legend.title = element_text(size = 12),
legend.text = element_text(size = 10.5))
return(GSE1479_pca_ggplot)
}
'
get.PCA.plot(GSE1479.exprs)
'
get.av.genes.from.PCA <- function(pca.result) {
PC1 <- pca.result$rotation[,'PC1'] %>% unlist
PC2 <- pca.result$rotation[,'PC2'] %>% unlist
gene.symbols <- rownames(pca.result$rotation) %>%
mget(mouse4302SYMBOL,ifnotfound = NA) %>%
make.names(unique = T)%>% unlist()
names(PC1) <- gene.symbols
names(PC2) <- gene.symbols
PC1 <- sort(PC1,decreasing = F)
PC2 <- sort(PC2,decreasing = F)
}
get.av.genes.from.DGE <- function(GSE1479.exprs) {
chamber.norm.exprs <- GSE1479.exprs[,7:36]
av.fac <- rep(rep(1:0,each = 3),5) %>%
factor(levels = 0:1, labels = c('atrium','ventricle'))
atrium.median <- apply(chamber.norm.exprs[,av.fac == 'atrium'], 1, median)
ventricle.median <- apply(chamber.norm.exprs[,av.fac == 'ventricle'], 1, median)
chamber.contrast <- ventricle.median - atrium.median
chamber.exprs <- chamber.norm.exprs[chamber.contrast >= 1 | chamber.contrast <= -1,]
pt <- apply(chamber.exprs, 1, function(x) t.test(x ~ av.fac)$p.value);
pw <- apply(chamber.exprs, 1, function(x) wilcox.test(x ~ av.fac)$p.value)
selected.chamber.genes <- chamber.exprs[pt < 0.001 & pw < 0.001,]
genes.name <- rownames(selected.chamber.genes) %>%
getSYMBOL('mouse4302')
result <- hclust( as.dist( 1 - cor(t( selected.chamber.genes ))),
method = 'complete') %>%
cutree(k = 2, h = 5)
atrium.probe.ID <- names(result)[result == 2]
ventricle.probe.ID <- names(result)[result == 1]
atrium.gene.names <- mget(atrium.probe.ID,mouse4302SYMBOL,ifnotfound = NA) %>%
unique() %>% na.omit() %>% unlist()
atrium.EntrezID <- mapIds(mouse4302.db,
keys = atrium.probe.ID,
c('ENTREZID'), keytype = 'PROBEID') %>%
unique() %>% na.omit() %>% unlist
atrium.gene.names <- atrium.gene.names[!is.na(atrium.gene.names)]
ventricle.gene.names <- mget(ventricle.probe.ID,mouse4302SYMBOL,ifnotfound = NA) %>%
unique() %>% na.omit() %>% unlist()
ventricle.EntrezID <- mapIds(mouse4302.db,
keys = ventricle.probe.ID,
c('ENTREZID'), keytype = 'PROBEID') %>%
unique() %>% na.omit() %>% unlist
# remove NA
ventricle.gene.names <- ventricle.gene.names[!is.na(ventricle.gene.names)]
av.groups <- list( atrium = atrium.gene.names ,
ventricle = ventricle.gene.names,
atrium.ID = atrium.EntrezID,
ventricle.ID = ventricle.EntrezID,
atrium.probeID = atrium.probe.ID,
ventricle.probeID = ventricle.probe.ID)
return(av.groups)
}
get.chamber.scree.plot <- function(GSE1479.exprs) {
chamber.list <- get.av.genes.from.DGE(GSE1479.exprs)
probe.ID <- rownames(GSE1479.exprs)
chamber.probeID <- c(chamber.list$atrium.probeID, chamber.list$ventricle.probeID)
chamber.genes.exprs <- GSE1479.exprs[probe.ID %in% chamber.probeID, ]
GSE1479_pca <- PCA(t(chamber.genes.exprs), scale.unit = FALSE, graph = FALSE)
GSE1479_scree_ggplot <- fviz_eig(GSE1479_pca, addlabels = TRUE, main = '') +
theme(aspect.ratio = 1) + theme_minimal()
return(GSE1479_scree_ggplot)
}
#---
# https://david.ncifcrf.gov/content.jsp?file=DAVID_API.html#approved_list
# https://stackoverflow.com/questions/31480579/r-david-webservice-sudden-transport-error-301-error-moved-permanently
# https://github.com/YuLab-SMU/clusterProfiler/issues/35
#---
get.atrium.DAVID.plot <- function(GSE1479.exprs) {
av.genes <- get.av.genes.from.DGE(GSE1479.exprs)
av.ids <- paste(av.genes$atrium.ID, collapse = ',')
david.service <- DAVIDWebService$new(email = '[email protected]',
url = 'https://david.ncifcrf.gov/webservice/services/DAVIDWebService.DAVIDWebServiceHttpSoap12Endpoint/')
david.param <- addList(david.service, av.ids,
idType = 'ENTREZ_GENE_ID',
listName = 'atrium_gene_set',
listType = 'Gene')
#setCurrentSpecies(david.service,10090)
setAnnotationCategories(david.service, c('UP_KEYWORDS'))
result.david <- getClusterReport(david.service)
data.final <- members(result.david)[[1]] %>% as.data.frame %$%
{data.frame( termlabels = Term,
LogPvalue = - log(Bonferroni) ) } %>%
ggplot( aes( x = reorder(termlabels, LogPvalue),
y = LogPvalue)) +
geom_bar( stat = 'identity', width = 0.6,
position = position_dodge(width = 0.05), size = 15) +
theme(axis.text.x = element_text(angle = 60,hjust = 1, size = 6),
axis.text.y = element_text(hjust = 1, size = 10),
axis.title = element_text(size = 12)) +
ylab('-log(p.adjust)') +
xlab('Enrichment of SP Terms') +
coord_flip() +
theme(aspect.ratio = 1)
return(data.final)
}
"
get.atrium.DAVID.plot(GSE1479.exprs)
"
get.atrium.GO.BF.plot <- function(GSE1479.exprs) {
av.genes <- get.av.genes.from.DGE(GSE1479.exprs)
go.datafarme <- enrichGO( gene = av.genes$atrium.ID,
OrgDb = org.Mm.eg.db,
ont = 'BP',
pAdjustMethod = 'BH',
qvalueCutoff = 0.05)
atrium.GO.plot <- go.datafarme %>% as.data.frame() %$%
{data.frame( Golabels = paste(ID, Description, sep = ' '),
LogPvalue = - log(p.adjust) ) } %>% .[1:9,] %>%
ggplot( aes( x = reorder(Golabels, LogPvalue),
y = LogPvalue)) +
geom_bar( stat = 'identity', width = 0.7,
position = position_dodge(width = 0.1), size = 20) +
theme(axis.text.x = element_text(angle = 60,hjust = 1, size = 7),
axis.text.y = element_text(hjust = 1, size = 7),
axis.title = element_text(size = 11) ) +
ylab('-log(p.adjust)') +
xlab('Enrichment of GO BP') +
theme(aspect.ratio = 1)
return(atrium.GO.plot)
}
get.figure.1 <- function(GSE1479.exprs) {
figure.1.A <- get.PCA.plot(GSE1479.exprs)
figure.1.B <- get.chamber.scree.plot(GSE1479.exprs)
figure.1.C <- get.atrium.GO.BF.plot(GSE1479.exprs)
figure.1.D <- get.atrium.DAVID.plot(GSE1479.exprs)
figure.1.final <- ggdraw() +
draw_plot( figure.1.A,
x =.00, y = .5, width = .5, height = .5) +
draw_plot( figure.1.B,
x = 0.5, y = 0.5, width = .5, height = .5) +
draw_plot( figure.1.C,
x = 0.0, y = 0.0, width = .5, height = .5) +
draw_plot( figure.1.D,
x = .5, y = 0, width = .5, height = .5) +
draw_plot_label( label = LETTERS[1:4],
x = c(0,.5,0,.5),
y = c(1,1,.5,.5),
size = 10)
return(figure.1.final)
}
#---
# Figure 2.
#---
# Figure.2 sample size = 6 * 9 = 54
.get.GSE775.groups <- function() {
groups <- list( h.1 = c(1:3,24:26,42:44),
h.4 = c(13:15,36:38,54:56),
h.24 = c(7:9,30:32,48:50),
h.48 = c(10:12,33:35,51:53),
w.1 = c(4:6,27:29,45:47),
w.8 = c(16:18,39:41,57:59) )
return(groups)
}
secure.tidy.data <- function(GSE775.exprs, gene.name = 'Sln') {
infarction.norm.exprs <- GSE775.exprs
groups <- .get.GSE775.groups()
gene.probe.id <- mget(gene.name,revmap(mgu74aSYMBOL)) %>% unlist() %>% .[1]
gene.norm.exprs <- infarction.norm.exprs[gene.probe.id, groups %$%
c(h.1,h.4,
h.24,h.48,w.1,w.8)]
gene.tidy.data <- data.frame( time = factor(rep(1:6,each = 9),
levels = 1:6,
labels = c('h.1','h.4','h.24','h.48','w.1','w.8')),
exprs = infarction.norm.exprs[ gene.probe.id,
groups %$% c(h.1,h.4,h.24,h.48,w.1,w.8)],
region = factor( rep(rep(1:3,each = 3),6),
levels = 1:3,
labels = c('lv','ilv','nilv')))
return(gene.tidy.data)
}
# TODO
# the figure show two legends. why?
# https://stackoverflow.com/questions/35108443/ggplot2-make-legend-key-fill-transparent
#---
get.nppa.regression.plot <- function(data.exprs, gene.name = 'Nppa') {
gene.tidy.data <- secure.tidy.data(data.exprs,gene.name)
gene.figure <- ggplot(data = gene.tidy.data, aes(x = time, y = exprs, group = region) ) +
geom_point(aes(color = region, shape = region), show.legend = F) +
geom_smooth(aes(color = region), method = lm, se = T,
show.legend = F, alpha = 0.1) +
geom_smooth(aes(color = region), method = lm, se = F, alpha = 0.1) +
theme(legend.position = c(0.2, 0.8)) +
theme(axis.title.x = element_blank()) +
scale_color_manual( values = c('#540D6E','#EE4266','#FFD23F'),
name = 'regions') +
labs(y = 'microarray exprs value') +
theme(legend.title.align = 0.5, legend.title = element_text(size = 13),
legend.key = element_rect(colour = 'transparent', fill = 'transparent')) +
theme( aspect.ratio = 1)
return( gene.figure )
}
"
gene.tidy.data <- secure.tidy.data(GSE775.result)
gene.tidy.data %>% select(region = ilv)
get.nppa.regression.plot(GSE775.result)
"
get.acta2.boxplot <- function(data.exprs, gene.name = 'Acta2') {
gene.tidy.data <- secure.tidy.data(data.exprs,gene.name)
gene.figure <- ggplot(data = gene.tidy.data, aes( x = time,
y = exprs) ) +
geom_boxplot(aes(fill = region),
position = position_dodge((0.5))) +
stat_summary(fun.y = mean, geom = 'point',
shape = 18, size = 2, color = 'grey') +
scale_fill_manual( values = c('#540D6E','#EE4266','#FFD23F'),
name = 'regions') +
theme(legend.position = c(0.1, 0.8),
legend.title.align = 0.5,
legend.title = element_text(size = 13)) +
theme( aspect.ratio = 1)
return(gene.figure)
}
"
get.acta2.boxplot(GSE775.exprs)
"
# Pheatmap error: 'gpar' element 'fill' must not be length 0 ,
# what to do about that in an assymmetrical matrix
# https://www.biostars.org/p/240404/
get.atrium.marker.heatmap <- function( data.affy) {
atrium.markers <- c('Sln','Fgf12','Myl7','Nppa')
groups <- .get.GSE775.groups() %$% c(h.1,h.4,h.24,h.48,w.1,w.8)
gene.probe.ids <- mget(atrium.markers,revmap(mgu74aSYMBOL)) %>% unlist()
gene.norm.exprs <- data.affy[gene.probe.ids, groups]
column.annotation <- factor( rep(rep(1:3,each = 3),6),
levels = 1:3,
labels = c('lv','ilv','nilv')) %>%
data.frame(regions = .) %>%
data.frame(time = factor(rep(1:6,each = 9),
levels = 1:6,
labels = c('h.1','h.4','h.24','h.48','w.1','w.8')),.)
rownames(column.annotation) <- colnames(gene.norm.exprs)
column.colors <- list(time = c(h.1 = '#320D6D', h.4 = '#FFBFB7',h.24 = 'white',
h.48= '#FFD447', w.1 = '#700353',w.8 = '#4C1C00'),
regions = c(lv = '#540D6E',ilv = '#EE4266',nilv = '#FFD23F'))
heat.map <- pheatmap(gene.norm.exprs, scale = 'none', cluster_cols = F, cluster_rows = F,
color = colorRampPalette(c('#0B3954', '#BFD7EA', '#FF6663'))(50),
cellheight = 23, cellwidth = 6, border_color = NA,
labels_row = atrium.markers,
show_colnames = F, annotation_col = column.annotation,
annotation_colors = column.colors, silent = T)
return(heat.map)
}
atriumization.GSEA.analysis <- function( GSE775.exprs, GSE1479.exprs,
group = 'w.1', contrast = 'ilv - lv', chamber = 'atrium') {
av.genes <- get.av.genes.from.DGE(GSE1479.exprs)
chamber.set <- av.genes$atrium
if (chamber == 'ventricle') chamber.set <- av.genes$ventricle
limma.result <- get.GSE775.limma.result(GSE775.exprs,group, contrast)
chamber.index <- rownames(limma.result) %>% getSYMBOL('mgu74a') %in% chamber.set
chamber.probe.id <- rownames(limma.result)[chamber.index]
t.statistic <- limma.result$t[chamber.index] %>%
sum()/sum(chamber.index) * sqrt(sum(chamber.index))
logFC.data <- limma.result$logFC
names(logFC.data) <- rownames(limma.result)
logFC.data.sorted <- sort(logFC.data, decreasing = TRUE)
logFC.data.sorted.id <- seq(1:length(logFC.data.sorted))
chamber.pseudo.binary <- names(logFC.data.sorted) %>%
getSYMBOL('mgu74a') %in% chamber.set
chamber.pseudo.id <- logFC.data.sorted.id[chamber.pseudo.binary]
names(logFC.data.sorted) <- logFC.data.sorted.id
chamber.gsea.set <- data.frame( diseaseId = rep(chamber,length(chamber.pseudo.id)),
geneId = chamber.pseudo.id, check.names = TRUE)
chamber.GSEA <- GSEA(logFC.data.sorted,
TERM2GENE = chamber.gsea.set,
minGSSize = 20, pvalueCutoff = 1)
#(1 - pnorm(t.statistic))
atrium.GSEA.plot <- gseaplot(chamber.GSEA, geneSetID = chamber,
by = 'runningScore', pvalue_table = T,
color.line = '#20BF55', color.vline = '#0B4F6C' ) +
theme(axis.title = element_text(size = 12),
axis.text = element_text(size = 8)) +
labs(caption = '(data source: GSE775)') +
theme( aspect.ratio = 1)
result <- list( class.t = 1 - pnorm(t.statistic),
chamber.gsea = chamber.GSEA,
gsea.plot = atrium.GSEA.plot)
return(result)
}
"
gsea.result <- atriumization.GSEA.analysis(GSE775.exprs,GSE1479.exprs)
"
get.figure.2 <- function(GSE775.exprs,GSE1479.exprs) {
figure.2.A <- get.nppa.regression.plot(GSE775.exprs,'Nppa')
figure.2.B <- get.acta2.boxplot(GSE775.exprs)
figure.2.C <- get.atrium.marker.heatmap(GSE775.exprs)$gtable
figure.2.D <- atriumization.GSEA.analysis(GSE775.exprs,GSE1479.exprs) %$%
gsea.plot
figure.2.final <- ggdraw() +
draw_plot( figure.2.A,
x =.00, y = .5, width = .5, height = .5) +
draw_plot( figure.2.B,
x = 0.5, y = 0.5, width = .5, height = .5) +
draw_plot( figure.2.C,
x = 0.0, y = 0.0, width = .5, height = .5) +
draw_plot( figure.2.D,
x = .5, y = 0, width = .5, height = .5) +
draw_plot_label( label = LETTERS[1:4],
x = c(0,.5,0,.5),
y = c(1,1,.5,.5),
size = 10)
return(figure.2.final)
}
#---
# Figure 3
#---
#---
# SLN expression pattern for 4
# test hypothesis, the anterior-poserior boundary will disappeear
# ventricle
# see experimental design
# https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM1570682
# Boyer data
# https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE64403
# iP0 : 0 day postnatal myocardium, isolated CMs
# iDS7 : 7 days post-sham surgery myocardium, isolated CMs
# iDR7 : 7 days post-resection myocardium, isolated CMs
# vP0 : 0 day postnatal ventricular myocardium
# ex0hr: Explanted, dissociated adult cardiomyocytes, 0hr
# vAd : Adult ventricular myocardium
# vD7R : 7 days post-resection surgery ventricular myocardium
# vD7S : 7 days post-sham surgery ventricular myocardium
#---
.get.rsubread.exprs <- function(rsubread.result) {
rsubread.gene.symbols <- mapIds( org.Mm.eg.db, keys = rsubread.result$annotation$GeneID,
keytype = 'ENSEMBL', column = 'SYMBOL') %>%
make.names(unique = T)
rsubread.exprs <- rsubread.result$counts
rownames(rsubread.exprs) <- rsubread.gene.symbols
return(rsubread.exprs)
}
.get.isolated.cell.deseq2 <- function(rsubread = GSE64403.results) {
GSE64403.isolated.cells <- .get.rsubread.exprs(rsubread)[,1:12]
GSE64403.cardiac.cells.condition <- factor( rep(1:4,each = 3),levels = 1:4,
labels = c('iP0','iP4','iD7S','iD7R'))
GSE64403.atrium.pValue <- newCountDataSet( GSE64403.isolated.cells,
GSE64403.cardiac.cells.condition ) %>%
estimateSizeFactors() %>% estimateDispersions() %>%
nbinomTest('iD7S', 'iD7R')
rsubread.gene.symbols <- mapIds( org.Mm.eg.db, keys = rsubread$annotation$GeneID,
keytype = 'ENSEMBL', column = 'SYMBOL') %>%
make.names(unique = T)
rownames(GSE64403.atrium.pValue) <- rsubread.gene.symbols
#atrium.gene.P.value <- GSE64403.atrium.pValue[GSE64403.atrium.pValue$id == 'Nppa',]
return(GSE64403.atrium.pValue)
}
# Figure.3.B sample size = 4
.get.heart.tissue.deseq2 <- function(rsubread = GSE64403.results) {
GSE64403.heart.tissue <- .get.rsubread.exprs(rsubread)[,29:36]
GSE64403.cardiac.tissue.condition <- factor( rep(1:4,each = 2),levels = 1:4,
labels = c('vD1S','vD1R','vD7R','vD7S'))
GSE64403.tissue.pValue <- newCountDataSet( GSE64403.heart.tissue,
GSE64403.cardiac.tissue.condition ) %>%
estimateSizeFactors() %>% estimateDispersions() %>%
nbinomTest('vD7S', 'vD7R')
rsubread.gene.symbols <- mapIds( org.Mm.eg.db, keys = rsubread$annotation$GeneID,
keytype = 'ENSEMBL', column = 'SYMBOL') %>%
make.names(unique = T)
rownames(GSE64403.tissue.pValue) <- rsubread.gene.symbols
#atrium.gene.P.value <- GSE64403.tissue.pValue[GSE64403.tissue.pValue$id == 'Sln',]
return(GSE64403.tissue.pValue)
}
secure.GSE64403.GSEA <- function(GSE1479.exprs, chamber = 'atrium') {
tissue.deseq2 <- .get.heart.tissue.deseq2()
av.genes <- get.av.genes.from.DGE(GSE1479.exprs)
chamber.set <- av.genes$atrium
if (chamber == 'ventricle') chamber.set <- av.genes$ventricle
logFC.data <- tissue.deseq2$log2FoldChange
names(logFC.data) <- rownames(tissue.deseq2)
logFC.data <- logFC.data[!is.na(logFC.data) & !is.infinite(logFC.data)]
logFC.data.sorted <- sort(logFC.data, decreasing = TRUE)
logFC.data.sorted.id <- seq(1:length(logFC.data.sorted))
chamber.pseudo.binary <- names(logFC.data.sorted) %in% chamber.set
chamber.pseudo.id <- logFC.data.sorted.id[chamber.pseudo.binary]
names(logFC.data.sorted) <- logFC.data.sorted.id
chamber.gsea.set <- data.frame( diseaseId = rep(chamber,length(chamber.pseudo.id)),
geneId = chamber.pseudo.id, check.names = TRUE)
chamber.GSEA <- GSEA(logFC.data.sorted,
TERM2GENE = chamber.gsea.set,
minGSSize = 20, pvalueCutoff = 1)
atrium.GSEA.plot <- gseaplot(chamber.GSEA, geneSetID = chamber,
by = 'runningScore',
color.line = '#20BF55', color.vline = '#0B4F6C' ) +
theme(axis.title = element_text(size = 12),
axis.text = element_text(size = 8)) +
labs(caption = '(data source: GSE64403)') +
theme( aspect.ratio = 1)
return(atrium.GSEA.plot)
}
"
foo <- secure.GSE64403.GSEA(GSE1479.exprs)
"
# figure.3.D sample szie = 2 * 3 = 6
secure.Sln.from.GSE64403 <- function(GSE64403.rsubread = GSE64403.results) {
GSE64403.atrium.pValue <- .get.isolated.cell.deseq2(GSE64403.rsubread)
GSE64403.cardiac.cells <- .get.rsubread.exprs(GSE64403.rsubread)[,7:12]
GSE64403.cardiac.cells.condition <- factor(rep(1:2,each = 3),
levels = 1:2,
labels = c('iDS','iDR'))
atrium.tidy.data <- data.frame(exprs = GSE64403.cardiac.cells['Sln',],
treat = GSE64403.cardiac.cells.condition )
atrium.p.adj.Value <- paste0( 'italic(\'P\')[italic(\'adi\')]',
' ==',GSE64403.atrium.pValue['Sln',]$padj)
prelim.figure.A <- ggplot(atrium.tidy.data, aes(x = treat, y = log(exprs))) +
geom_jitter( aes(color = treat),
width = 0.05, height = 0.5, size = 3.0) +
scale_x_discrete(labels = c('sham','surgical')) +
scale_colour_manual( values = c('#1C3144','#D00000'),
name = 'treatment',labels = c('sham','surgical')) +
scale_y_continuous(breaks = seq(-5,10,2),limits = c(-2,10)) +
geom_segment(aes(x = 1.0, y = 9.0, xend = 1.0, yend = 9.5)) +
geom_segment(aes(x = 1.0, y = 9.5, xend = 2.0, yend = 9.5)) +
geom_segment(aes(x = 2.0, y = 9.0, xend = 2.0, yend = 9.5)) +
annotate( geom = 'text', x = 1.5, y = 9.9,
label = atrium.p.adj.Value, parse = T) +
theme_classic() +
theme(legend.position = c(0.8, 0.18)) +
theme(axis.title.x = element_blank(), legend.title.align = 0.5) +
labs(y = 'log 2(RPKM exprs value)') +
theme( aspect.ratio = 1)
return(prelim.figure.A)
}
# Figure.3.A sample size = 10
get.AV.postSurgery.heatmap <- function(GSE64403.rsubread = GSE64403.results ) {
av.genes <- c('Sln','Tbx5','Nr2f2','Myl7','Myl4',
'Hey2','Irx4','Kcne1','Myh7','Pln')
sample.info <- data.frame( treat = c('vAd','vAd','vD1S','vD1S','vD1R','vD1R',
'vD7R','vD7R','vD7S','vD7S'))
rusubread.vsd.exprs <- DESeqDataSetFromMatrix( countData = GSE64403.rsubread$counts[,27:36],
colData = sample.info,
design = ~ treat) %>%
DESeq2::varianceStabilizingTransformation() %>%
assay()
rsubread.gene.symbols <- mapIds( org.Mm.eg.db, keys = GSE64403.rsubread$annotation$GeneID,
keytype = 'ENSEMBL', column = 'SYMBOL') %>%
make.names(unique = T)
rownames(rusubread.vsd.exprs) <- rsubread.gene.symbols
gene.norm.exprs <- rusubread.vsd.exprs[av.genes,]
column.annotation <- data.frame(samples = factor(rep(1:5,each = 2),
labels = c('vAd','vD1S',
'vD1R','vD7R','vD7S')) )
column.colors <- list(samples = c(vAd = '#1C3114', vD1S = '#D00000',vD1R = '#FFBA08',
vD7R = '#A2AEBB', vD7S = '#3F88C5'))
rownames(column.annotation) <- colnames(gene.norm.exprs)
heat.map <- pheatmap(gene.norm.exprs, scale = 'none', cluster_cols = F, cluster_rows = F,
color = colorRampPalette(c('#0B3954', '#BFD7EA', '#FF6663'))(50),
cellheight = 23, cellwidth = 6, labels_row = av.genes,
border_color = NA, annotation_col = column.annotation,
show_colnames = F, annotation_colors = column.colors, silent = T)
return(heat.map)
}
# figure.3.C sample size = 3 * 4 = 12
get.AV.isolated.heatmap <- function(GSE64403.rsubread = GSE64403.results) {
av.genes <- c('Sln','Tbx5','Nr2f2','Myl7','Myl4',
'Hey2','Irx4','Kcne1','Myh7','Pln')
sample.info <- data.frame( treat = c(rep('iP0',3),rep('iP4',3),rep('iP7S',3),
rep('iD7R',3)))
rusubread.vsd.exprs <- DESeqDataSetFromMatrix( countData = GSE64403.rsubread$counts[,1:12],
colData = sample.info,
design = ~ treat) %>%
DESeq2::varianceStabilizingTransformation() %>%
assay()
rsubread.gene.symbols <- mapIds( org.Mm.eg.db, keys = GSE64403.rsubread$annotation$GeneID,
keytype = 'ENSEMBL', column = 'SYMBOL') %>%
make.names(unique = T)
rownames(rusubread.vsd.exprs) <- rsubread.gene.symbols
gene.norm.exprs <- rusubread.vsd.exprs[av.genes,]
column.annotation <- data.frame(samples = factor(rep(1:4,each = 3),
labels = c('iP0','iP4','iP7S','iP7R')))
rownames(column.annotation) <- colnames(gene.norm.exprs)
column.colors <- list(samples = c(iP0 = '#D00000',iP4 = '#FFBA08',
iP7S = '#A2AEBB',iP7R = '#3F88C5'))
heat.map <- pheatmap(gene.norm.exprs, scale = 'none', cluster_cols = F, cluster_rows = F,
cellheight = 23, cellwidth = 6, border_color = NA,
color = colorRampPalette(c('#0B3954', '#BFD7EA', '#FF6663'))(50),
labels_row = av.genes, annotation_col = column.annotation,
show_colnames = F, annotation_colors = column.colors, silent = T)
return(heat.map)
}
get.figure.3 <- function(GSE64403.rsubread,GSE1479.exprs) {
figure.3.A <- get.AV.postSurgery.heatmap(GSE64403.rsubread) %$% gtable
figure.3.B <- secure.GSE64403.GSEA(GSE1479.exprs)
figure.3.C <- get.AV.isolated.heatmap(GSE64403.rsubread) %$% gtable
figure.3.D <- secure.Sln.from.GSE64403(GSE64403.rsubread)
figure.3.final <- ggdraw() +
draw_plot( figure.3.A,
x =.00, y = .5, width = .5, height = .5) +
draw_plot( figure.3.B,
x = 0.5, y = 0.5, width = .5, height = .5) +
draw_plot( figure.3.C,
x = 0.0, y = 0.0, width = .5, height = .5) +
draw_plot( figure.3.D,
x = .5, y = 0, width = .5, height = .5) +
draw_plot_label( label = LETTERS[1:4],
x = c(0,.5,0,.5),
y = c(1,1,.5,.5),
size = 10)
return(figure.3.final)
}
# TODO
#---
# Figure 4
#---
#----
# the Original data
# from Broad institute
# http://software.broadinstitute.org/gsea/msigdb/cards/PID_RETINOIC_ACID_PATHWAY.html
#----
.get.probe.id <- function(gene.name) {
#---
# Sln mouse time expression pattern
# mget('1448394_at',mouse4302GENENAME)
# mget('17888',revmap(mouse4302ENTREZID))
#---
probe.ids <- mget(gene.name,revmap(mgu74aSYMBOL)) %>% unlist() %>% .[1]
return(probe.ids)
}
get.GSE775.limma.result <- function (GSE775.exprs,
group = 'w.1', contrast = 'ilv - lv') {
infarction.exprs <- GSE775.exprs
group.selection <- .get.GSE775.groups()[c(group)] %>% unlist()
f.1 <- factor( rep(1:3,each = 3),
levels = 1:3,labels = c('lv','ilv','nilv'))
design.1 <- model.matrix(~ 0 + f.1)
colnames(design.1) <- levels(f.1)
contrast.matrix.1 <- makeContrasts(contrasts = contrast,levels = design.1)
limma.result <- infarction.exprs[,group.selection] %>%
lmFit(design.1) %>%
contrasts.fit(contrast.matrix.1) %>%
eBayes() %>%
topTable(adjust = 'BH', number = Inf)
return(limma.result)
}
"
acta2.result.1 <- get.GSE775.limma.result(GSE775.result)
head(acta2.result.1)
rownames(acta2.result.1) <- rownames(acta2.result.1) %>%
getSYMBOL('mgu74a') %>% make.names(unique = T)
acta2.result.1['Acta2',]
"
.secure.RA.signaling.pathway <- function(spiece = 'mouse', manual = T) {
RA.components.human <- list(HDAC3 = 8841, PRKCG = 5582, CDK1 = 983, RBP1 = 5947, RARA = 5914,
RARB = 5915, VDR = 7421, RARG = 5916, PRKCA = 5578, PRKACA = 5566,
RXRA = 6256, MAPK1 = 5594, RXRB = 6257, AKT1 = 207 , MAPK8 = 5599,
RXRG = 6258, NRIP1 = 8204, CDK7 = 1022, CCNH = 902, MNAT1 = 4331,
EP300 = 2033, HDAC1 = 3065, NCOA2 = 10499, NCOA1 = 8648, MAPK14 = 1432,
CREBBP = 1387, KAT2B = 8850, NCOR2 = 9612, NCOA3 = 8202 )
RA.components.mouse <- list(Hdac3 = 15183, Prkcg = 18752, Cdk1 = 12534, Rbp1 = 19659, Rara = 19401,
Rarb = 218772, Vdr = 22337, Rarg = 19411, Prkca = 18750, Prkaca = 18747,
Rxra = 20181, Mapk1 = 26413, Rxrb = 20182, Akt1 = 11651, Mapk8 = 26419,
Rxrg = 20183, Nrip1 = 268903, Cdk7 = 12572, Ccnh = 66671, Mnat1 = 17420,
Ep300 = 328572, Hdac1 = 433759, Ncoa2 = 17978, Ncoa1 = 17977, Mapk14 = 26416,
Crebbp = 12914, Kat2b = 18519, Ncor2 = 20602, Ncoa3 = 17979)
RA.target.genes <- list(Nr2c1 = 22025,Rara = 19401, Rarb = 218772,Rarg = 19411,Hoxa1 = 15394,
Hoxb1 = 15407,Hoxa4 = 15401, Hoxb4 = 15412, Hoxd4 = 15436, Hoxb5 = 15413,
Cdx1 = 12590, Pax6 = 18508, Pdx1 = 18609, Pitx2 = 18741, Tlx2 = 21909,
Cebpe = 110794, Egr1 = 13653, Ets1 = 23871, Foxa1 = 15375,Hnf1a = 21405,
Neurog2 = 11924, Olig2 = 50913, Pou1f1 = 18736, Pou5f1 = 18999, Stat1 = 20846,
Adh7 = 11529, Aldh1a1 = 11668, Crabp2 = 12904, Cyp26a1 = 13082, Rbp1 = 19659,
Icam1 = 15894, Itgb3 = 16416, Lamb1 = 16777, Epo = 13856, Fgf8 = 14179,
Gh = 14599, Gnrh1 = 14714, Tshb = 22094, Lefty1 = 13590, Mdk = 17242,
Nodal = 18119, Oxt = 18429)
if(manual == T) return(RA.target.genes)
if (spiece == 'mouse') return(RA.components.mouse)
if (spiece == 'human') return(RA.components.human)
}
.get.manual.curation.RA.genes <- function(spiece = 'mouse') {
ra.mouse.genes <- list(Nr2c1 = 22025, Hoxa1 = 15394, Hoxb5 = 15413,Aldh1a1 = 11668,Aldh1a2 = 19378)
ra.human.genes <- list(NR2C1 = 7181, HOXA1 = 3198,HOXB5 = 3215,ALDH1A1 = 216,ALDH1A2 = 395844)
if (spiece == 'mouse') return(ra.mouse.genes)
if (spiece == 'human') return(ra.human.genes)
}
# Figure.4.A sample size = 54
# see the function .get.GSE775.groups()
.get.RA.infarction.tidydata <- function(data.affy, gene.name = 'Aldh1a2') {
ra.gene.1 <- secure.tidy.data(data.affy, gene.name)
ra.gene.1.probe <- .get.probe.id(gene.name)
h.24.pvalue.1 <- get.GSE775.limma.result(data.affy,
group = 'h.24',
contrast = 'ilv - lv') %>%
{.[ra.gene.1.probe,]} %$% adj.P.Val %>%
paste0( 'italic(\'P\')[italic(\'adi\')]', ' == ',.)
h.24.pvalue.2 <- get.GSE775.limma.result(data.affy,
group = 'h.24',
contrast = 'nilv - lv') %>%
{.[ra.gene.1.probe,]} %$% adj.P.Val %>%
paste0( 'italic(\'P\')[italic(\'adi\')]', ' == ',.)
h.48.pvalue.1 <- get.GSE775.limma.result(data.affy,
group = 'h.48',
contrast = 'ilv - lv') %>%
{.[ra.gene.1.probe,]} %$% adj.P.Val %>%
paste0( 'italic(\'P\')[italic(\'adi\')]', ' == ',.)
h.48.pvalue.2 <- get.GSE775.limma.result(data.affy,
group = 'h.48',
contrast = 'nilv - lv') %>%
{.[ra.gene.1.probe,]} %$% adj.P.Val %>%
paste0( 'italic(\'P\')[italic(\'adi\')]', ' == ',.)
tidy.data <- list( cleaned.data = ra.gene.1,
h.24.pvalue.1 = h.24.pvalue.1,
h.24.pvalue.2 = h.24.pvalue.2,
h.48.pvalue.1 = h.48.pvalue.1,
h.48.pvalue.2 = h.48.pvalue.2)
return(tidy.data)
}
get.RA.infarction.plot.1 <- function(data.affy, gene.name = 'Aldh1a2') {
cleaned.data <- .get.RA.infarction.tidydata(data.affy,gene.name)
ra.gene.1 <- cleaned.data$cleaned.data
h.24.pvalue.1 <- cleaned.data$h.24.pvalue.1
h.24.pvalue.2 <- cleaned.data$h.24.pvalue.2
h.48.pvalue.1 <- cleaned.data$h.48.pvalue.1
h.48.pvalue.2 <- cleaned.data$h.48.pvalue.2
ra.gene.1.plot <- ggplot(ra.gene.1, aes(x = time, y = exprs)) +
scale_y_continuous(breaks = seq(5,10,1),limits = c(5,10)) +
geom_boxplot(aes(fill = region),
position = position_dodge(0.5)) +
scale_fill_manual( name = 'regions',
values = c( '#F5D547','#DB3069','#1446A0'),
labels = c( 'lv','ilv','nilv')) +
geom_segment(aes(x = 2.8, y = 6.8, xend = 2.8, yend = 9.0)) +
geom_segment(aes(x = 2.8, y = 9.0, xend = 3.0, yend = 9.0)) +
geom_segment(aes(x = 3.0, y = 9.0, xend = 3.0, yend = 8.0)) +
annotate( geom = 'text', x = 2.8, y = 9.1,
label = h.24.pvalue.1, size = 3.5, parse = T) +
geom_segment(aes(x = 2.8, y = 5.5, xend = 2.8, yend = 6.1)) +
geom_segment(aes(x = 2.8, y = 5.5, xend = 3.2, yend = 5.5)) +
geom_segment(aes(x = 3.2, y = 5.5, xend = 3.2, yend = 6.2)) +
annotate( geom = 'text', x = 3.0, y = 5.3,
label = h.24.pvalue.2, size = 3.5, parse = T) +
geom_segment(aes(x = 3.8, y = 6.7, xend = 3.8, yend = 8.4)) +
geom_segment(aes(x = 3.8, y = 8.4, xend = 4.0, yend = 8.4)) +
geom_segment(aes(x = 4.0, y = 8.2, xend = 4.0, yend = 8.4)) +
annotate( geom = 'text', x = 4.0, y = 8.6,
label = h.48.pvalue.1, size = 3.5, parse = T) +
geom_segment(aes(x = 3.8, y = 5.8, xend = 3.8, yend = 6.0)) +
geom_segment(aes(x = 3.8, y = 5.8, xend = 4.2, yend = 5.8)) +
geom_segment(aes(x = 4.2, y = 5.8, xend = 4.2, yend = 6.6)) +
annotate( geom = 'text', x = 4.2, y = 5.6,
label = h.48.pvalue.2, size = 3.5, parse = T) +
theme_classic() +
theme(legend.position = c(0.85, 0.7), legend.title.align = 0.5) +
labs(y = 'log2 gene exprs') +
theme( aspect.ratio = 1)
return(ra.gene.1.plot)
}
"
get.RA.infarction.plot.1(data.affy)
"
get.RA.infarction.plot.2 <- function(data.affy, gene.name = 'Pbx1') {
cleaned.data <- .get.RA.infarction.tidydata(data.affy,gene.name)
ra.gene.1 <- cleaned.data$cleaned.data
h.24.pvalue.1 <- cleaned.data$h.24.pvalue.1
h.24.pvalue.2 <- cleaned.data$h.24.pvalue.2
h.48.pvalue.1 <- cleaned.data$h.48.pvalue.1
h.48.pvalue.2 <- cleaned.data$h.48.pvalue.2
ra.gene.1.plot <- ggplot(ra.gene.1, aes(x = time, y = exprs)) +
scale_y_continuous(breaks = seq(6,8,1),limits = c(6,8)) +
geom_boxplot(aes(fill = region), position = position_dodge(0.5)) +
geom_segment(aes(x = 2.8, y = 6.9, xend = 2.8, yend = 7.8)) +
geom_segment(aes(x = 2.8, y = 7.8, xend = 3.1, yend = 7.8)) +
geom_segment(aes(x = 3.1, y = 6.8, xend = 3.1, yend = 7.8)) +
annotate( geom = 'text', x = 2.9, y = 7.9,
label = h.24.pvalue.1, parse = T, size = 3.0) +
geom_segment(aes(x = 2.8, y = 6.2, xend = 2.8, yend = 6.3)) +
geom_segment(aes(x = 2.8, y = 6.2, xend = 3.3, yend = 6.2)) +
geom_segment(aes(x = 3.3, y = 6.2, xend = 3.3, yend = 6.4)) +
annotate( geom = 'text', x = 3.0, y = 6.1,
label = h.24.pvalue.2, parse = T, size = 3.0) +
geom_segment(aes(x = 3.8, y = 6.9, xend = 3.8, yend = 7.4)) +
geom_segment(aes(x = 3.8, y = 7.4, xend = 4.0, yend = 7.4)) +
geom_segment(aes(x = 4.0, y = 6.8, xend = 4.0, yend = 7.4)) +
annotate( geom = 'text', x = 3.8, y = 7.5,
label = h.48.pvalue.1, parse = T, size = 3.0) +
geom_segment(aes(x = 3.8, y = 6.2, xend = 3.8, yend = 6.6)) +
geom_segment(aes(x = 3.8, y = 6.2, xend = 4.2, yend = 6.2)) +
geom_segment(aes(x = 4.2, y = 6.2, xend = 4.2, yend = 6.5)) +
annotate( geom = 'text', x = 3.9, y = 6.1,
label = h.48.pvalue.2, parse = T, size = 3.0) +
theme_classic() +
labs(y = 'log2 gene exprs') +
theme( aspect.ratio = 1)
return(ra.gene.1.plot)
}
"
get.RA.infarction.plot.1(data.affy)
.get.RA.infarction.tidydata(data.affy,'Hoxa1')
"
.get.RA.regeneration.tidy.data <- function(data.rsubread, gene.name = 'Aldh1a2') {
GSE64403.heart.tissue <- .get.rsubread.exprs(data.rsubread)[,27:36]
GSE64403.cardiac.tissue.condition <- factor( rep(1:5,each = 2),levels = 1:5,
labels = c('vAd','vD1S','vD1R','vD7R','vD7S'))
GSE64403.tissue.exprs <- newCountDataSet( GSE64403.heart.tissue,
GSE64403.cardiac.tissue.condition ) %>%
estimateSizeFactors() %>% estimateDispersions()
GSE64403.tissue.pValue.1 <- GSE64403.tissue.exprs %>% nbinomTest('vD1S', 'vAd')
GSE64403.tissue.pValue.2 <- GSE64403.tissue.exprs %>% nbinomTest('vD1R', 'vD1S')
GSE64403.tissue.pValue.3 <- GSE64403.tissue.exprs %>% nbinomTest('vD1R', 'vAd')
rsubread.gene.symbols <- mapIds( org.Mm.eg.db, keys = data.rsubread$annotation$GeneID,
keytype = 'ENSEMBL', column = 'SYMBOL') %>%
make.names(unique = T)
rownames(GSE64403.tissue.pValue.1) <- rsubread.gene.symbols
rownames(GSE64403.tissue.pValue.2) <- rsubread.gene.symbols