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Chunk12-SD Boxplot for validation of housekeeping using the DNA microarray and RNA-seq datasets.R
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Chunk12-SD Boxplot for validation of housekeeping using the DNA microarray and RNA-seq datasets.R
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################################################################################
# &&&....&&& % Project: Identification of HKG candidates in C. elegans #
# &&&&&&..&&&&&& % Author: Bo Li, Jingxin Tao, Youjin Hao #
# &&&&&&&&&&&&&& % Date: Dec. 24th, 2019 #
# &&&&&&&&&&&& % #
# &&&&&&&& % Environment: R version 3.5.3; #
# &&&& % Platform: x86_64-pc-linux-gnu (64-bit) #
# & % #
################################################################################
### ****************************************************************************
### code chunk number 11: Validation of HKGs using RNA-sequencing datasets.
### ****************************************************************************
###==========================================================================###
### Common variation.
genes_26 <- c("cyc-1", "tba-1", "atp-3", "mdh-1", "gpd-2",
"eif-3.C", "act-1", "cdc-42", "pmp-3", "act-2",
"csq-1", "ama-1", "rbd-1", "rps-23", "rps-27",
"rps-26", "rps-4", "rps-2", "rps-16", "rps-17",
"rpl-24.1", "rpl-15", "rpl-35", "rpl-36", "rpl-33","rpl-27")
col_26 <- rep(c("#EE9572", "#698B69"), times = c(13, 13))
# op <- par(mfcol = c(3, 2)) # width:length = 12:8
# windowsFonts(A = windowsFont("Times New Roman"))
# pdf(file = "myplot1.pdf", family = "Times", width = 18, height = 10)
op <- par(mfrow = c(2, 3), bty = "l", cex.lab = 1.5, cex.axis = 1.5,
cex.main = 2, oma = c(0.5, 0.5, 0.5, 0.5), bg = "#FFFACD") # width:length = 12:8
setwd("J:/HKG_test/")
###----------------------- (1) Microarray: GSE118294 ------------------------###
###--For DNA microarray dataset, GSE118294.----------------------------------###
library(affy)
es <- get(load("GSE118294.RData"))
library(GEOquery)
dat <- getGEO("GSE118294", GSEMatrix = TRUE)
anno.tab <- dat[[1]]@featureData@data; dim(anno.tab)
eset <- es; dim(eset)
table(anno.tab$ID == rownames(eset))
probe.num <- nrow(anno.tab)
eset.mat <- NULL
for (g in genes_26) {
gene.pos <- NULL
for (i in 1:probe.num) {
gene.hit <- which(strsplit(anno.tab$`Gene Symbol`[i], " /// ")[[1]] == g)
if (length(gene.hit) != 0) gene.pos <- c(gene.pos, i)
}
# gene.pos
tmp <- eset[gene.pos, ]
# print(tmp)
if (length(gene.pos) > 1) gene.eset <- apply(as.matrix(tmp), 2, mean) else gene.eset <- tmp
print(g)
print(gene.eset)
print("######################################################333############")
eset.mat <- rbind(eset.mat, gene.eset)
}
rownames(eset.mat) <- genes_26
sd.seq <- apply(eset.mat, 1, sd)
x <- t(eset.mat[sd.seq != 0, ])
y <- reshape2::melt(x)
# SD.
sort(sd.seq)
boxplot(sd.seq[col_26 == "#EE9572"], sd.seq[col_26 == "#698B69"],
col = c("#EE9572", "#698B69"), names = c("pRGs", "iRGs"), pch = 16,
main = "GSE118294", notch = FALSE)
# GC.
sort(apply(eset.mat, 1, ineq::Gini))
### End of GSE118294 ======================================================= ###
###--------------------------------------------------------------------------###
###----------------------- (2) Microarray: GSE108968 ------------------------###
###--For DNA microarray dataset, GSE108968.----------------------------------###
library(limma)
dat <- get(load("GSE108968.RData"))
dat2 <- backgroundCorrect(dat, "normexp", offset = 50)
eset <- normalizeBetweenArrays(dat2$R, method = "quantile")
eset2 <- log(eset, 2)
rownames(eset2) <- dat$genes$ProbeName
eset2 <- as.data.frame(eset2)
# write.csv(eset2,"GSE108968_expr.csv")
################################################################################
gse108968 <- getGEO("GSE108968", GSEMatrix = TRUE)
str(gse108968)
anno.tab <- gse108968[[1]]@featureData@data; dim(anno.tab)
rownames(eset2) <- NULL
eset <- eset2; dim(eset)
# table(anno.tab$NAME == rownames(eset))
nr <- nrow(anno.tab)
eset.mat <- NULL
for (g in genes_26) {
gene.pos <- NULL
for (i in 1:nr) {
gene.hit <- which(strsplit(anno.tab$GENE_SYMBOL[i], " /// ")[[1]] == g)
if (length(gene.hit) != 0) gene.pos <- c(gene.pos, i)
}
# gene.pos
tmp <- eset[gene.pos, ]
# print(tmp)
if (length(gene.pos) > 1) gene.eset <- apply(as.matrix(tmp), 2, mean) else
if (length(gene.pos) == 0) gene.eset <- 0
else gene.eset <- tmp
print(g)
print(gene.eset)
print("##################################################################")
eset.mat <- rbind(eset.mat, gene.eset)
}
rownames(eset.mat) <- genes_26
sd.seq <- apply(eset.mat, 1, sd)
x <- t(eset.mat[sd.seq != 0, ]);
y <- reshape2::melt(x)
# SD.
sort(sd.seq)
boxplot(sd.seq[sd.seq != 0][col_26 == "#EE9572"], sd.seq[sd.seq != 0][col_26 == "#698B69"],
col = c("#EE9572", "#698B69"), names = c("pRGs", "iRGs"), pch = 16,
main = "GSE108968", notch = FALSE)
# GC.
sort(apply(eset.mat, 1, ineq::Gini))
### End of GSE108968. =======================================================###
###--------------------------------------------------------------------------###
###----------------------- (3) Microarray: GSE76380 -------------------------###
###--For DNA microarray dataset, GSE76380.-----------------------------------###
library(limma)
dat <- get(load("GSE76380.RData"))
dat2 <- backgroundCorrect(dat, "normexp", offset = 50)
eset <- normalizeBetweenArrays(dat2$R, method = "quantile")
eset2 <- log(eset, 2)
rownames(eset2) <- dat$genes$ProbeName
eset2 <- as.data.frame(eset2)
########################################################
gse76380 <- getGEO("GSE76380", GSEMatrix = TRUE)
str(gse76380)
anno.tab <- gse76380[[1]]@featureData@data; dim(anno.tab)
rownames(eset2) <- NULL
eset <- eset2; dim(eset)
# table(anno.tab$NAME == rownames(eset))
nr <- nrow(anno.tab)
eset.mat <- NULL
for (g in genes_26) {
gene.pos <- NULL
for (i in 1:nr) {
gene.hit <- which(strsplit(anno.tab$GENE_SYMBOL[i], " /// ")[[1]] == g)
if (length(gene.hit) != 0) gene.pos <- c(gene.pos, i)
}
# gene.pos
tmp <- eset[gene.pos, ]
# print(tmp)
if (length(gene.pos) > 1) gene.eset <- apply(as.matrix(tmp), 2, mean) else
if (length(gene.pos) == 0) gene.eset <- 0
else gene.eset <- tmp
print(g)
print(gene.eset)
print("##################################################################")
eset.mat <- rbind(eset.mat, gene.eset)
}
rownames(eset.mat) <- genes_26
sd.seq <- apply(eset.mat, 1, sd)
x <- t(eset.mat[sd.seq != 0, ]);
y <- reshape2::melt(x)
# SD.
sort(sd.seq)
boxplot(sd.seq[sd.seq != 0][col_26 == "#EE9572"], sd.seq[sd.seq != 0][col_26 == "#698B69"],
col = c("#EE9572", "#698B69"), names = c("pRGs", "iRGs"), pch = 16,
main = "GSE76380", notch = FALSE)
# GC.
sort(apply(eset.mat, 1, ineq::Gini))
### End of GSE76380. ========================================================###
###--------------------------------------------------------------------------###
###----------------------- (4) Microarray: GSE76380 -------------------------###
###--For RNA-sequencing dataset, GSE63528, GSE60755 and GSE98919-------------###
### Step-01. Comparison of gene expression levels using RNA-sequencing datasets
# devtools::install_github("markziemann/dee2/getDEE2")
library('getDEE2')
#. mdat <- getDee2Metadata("celegans")
expre_gse <- c("GSE63528",
"GSE60755",
"GSE98919")
library(edgeR)
library(openxlsx)
fileName <- dir()[grep("_count.csv", dir())]
ask.mean <- function(x) {mean(x, na.rm = TRUE)}
for (f in fileName) {
data <- read.csv(f)
data_norm <- cpm.default(data[, -(1:6)])
sym <- as.character(data$GeneSymbol)
rownames(data_norm) <- sym
cpm_data <- data_norm
mat.26 <- NULL
for (g in genes_26) {
pos <- which(rownames(cpm_data) == g)
tmp <- cpm_data[pos, ]
if (!is.null(nrow(tmp))) tmp1 <- apply(tmp, 2, ask.mean) else tmp1 <- tmp
mat.26 <- rbind(mat.26, tmp1)
}
rownames(mat.26) <- genes_26
mat.26 <- log(mat.26 + 1, 2)
#Gini rank
tmp <- apply(mat.26, 1, ineq::Gini)
GC <- sort(tmp)
GC <- as.matrix(GC)
print("############################ Start ##################################")
print(f)
#print(as.data.frame(GC[1:10]))
# write.csv(GC[1:10], paste(f, "csv", sep = "."))
#SD rank
sd.seq <- apply(mat.26, 1, sd)
SD <- sort(sd.seq)
SD <- as.matrix(SD)
#print(as.data.frame(SD[1:10]))
res <- list(SD = SD, GC = GC)
gse.nam <- strsplit(f, "_")[[1]][1]
xlsx.file <- paste(gse.nam, "xlsx", sep = ".")
write.xlsx(res, file = xlsx.file, col.names = TRUE, row.names = TRUE)
Sys.sleep(20)
# Boxplot
# print(mat.26[tmp1 != 0, ])
print(sd.seq)
# boxplot(t(mat.26[tmp1 != 0, ]), col = col_26[tmp1 != 0], las = 3)
x <- t(mat.26[sd.seq != 0, ]);
y <- reshape2::melt(x)
boxplot(sd.seq[sd.seq != 0][col_26 == "#EE9572"], sd.seq[sd.seq != 0][col_26 == "#698B69"],
col = c("#EE9572", "#698B69"), names = c("pRGs", "iRGs"), pch = 16,
main = gse.nam, notch = FALSE)
print("############################# End ####################################")
}
par(op)
#. dev.off()
### End of the chunk 11.
###==========================================================================###