I have done this analysis:
#You can select Working Directory according to your choice
setwd("D:")
#Check Working Directory
getwd()
#Creation of object(folder) exdir
exdir <- path.expand("~/GSE162562_RAW")
dir.create(exdir, showWarnings = FALSE)
URL <- "
https://ftp.ncbi.nlm.nih.gov/geo/series/GSE162nnn/GSE162562/suppl/GSE162562_RAW.tar
"
FILE <- file.path(tempdir(), basename(URL))
#Downlaod the Raw Data from GEO
utils::download.file(URL, FILE, mode = "wb")
#unzip the files and storing them in GSE162562_RAW folder which we created
already
utils::untar(FILE, exdir = exdir)
#Check your GSE162562_RAW folder after this , it must contains 108 samples
in compressed format
unlink(FILE, recursive = TRUE, force = TRUE)
#listing of samples
listed_files <- list.files(exdir, pattern=".gz", full.names=TRUE)
#/ load files into R:
loaded <- lapply(listed_files, function(x) read.delim(x, header=FALSE,
row.names = "V1"))
#/ bind everything into a single count matrix and assign colnames:
raw_counts <- do.call(cbind, loaded)
colnames(raw_counts) <- gsub(".txt.*", "", basename(listed_files))
#/ there is some nonsense in these files, probably due to the tool that was
used (HTSeq?),
#/ such as rows with names "__no_feature", lets remove that:
raw_counts <- raw_counts[!grepl("^__", rownames(raw_counts)),]
# / View raw_counts after filtering
raw_counts
#There are total 26364 genes after filtering
#Store Raw counts in CSV File
#I am sacing them in my Desktop.You can save according to your choice
write.csv(raw_counts,"C:\\Users\\USER\\Desktop\\countsdata.csv")
#load library
library(DESeq2)
#/ make a DESeq2 object. We can parse the group membership (Mild etc) from
the colnames,
#/ which are based on the original filenames:
dds <- DESeqDataSetFromMatrix(
countData=raw_counts,
colData=data.frame(group=factor(unlist(lapply(strsplit(colnames(raw_counts),
split="_"), function(x) x[4])))),
design=~group)
#View Deseq2 object
dds
#/ some Quality Control via PCA using the in-built PCA function from DESeq2:
vsd <- vst(dds, blind=TRUE)
#/ plot the PCA:
plotPCA(vsd, "group")
#/ extract the data:
pcadata <- plotPCA(vsd, "group", returnData=TRUE)
#view pca data
pcadata
#/ there is a very obvious batch effect, that we can correct, simply
everything that is greater or less than zero in the first principal
component, very obvious just by looking at the plot:
dds$batch <- factor(ifelse(pcadata$PC1 > 0, "batch1", "batch2"))
#/ lets use limma::removeBatchEffect to see whether it can be removed:
library(limma)
vsd2 <- vsd
assay(vsd2) <- removeBatchEffect(assay(vsd), batch=dds$batch)
#/ plot PCA again, looks much better. this means we modify our design to
include batch and group,
plotPCA(vsd2, "group")
#include batch and group both in design
design(dds) <- ~batch + group
#Running the differential expression pipeline
dds <- DESeq(dds)
#Building the results table
res <- results(dds)
res
#Saving Results in CSV file
write.csv(res , "dds.csv")
mcols(res, use.names = TRUE)
#We can also summarize the results with the following line of code, which
reports some additional information:
summary(res)
#Total 1236 DEGs have been found when we se p_value less than 0.1
table(res$padj < 0.01)
res.05 <- results(dds, alpha = 0.05)
summary(res.05)
table(res.05$padj < 0.05)
sum(res$pvalue < 0.05, na.rm=TRUE)
sum(!is.na(res$pvalue<0.05))
sum(res$padj < 0.1, na.rm=TRUE)
sum(res$padj < 0.05, na.rm=TRUE)
resSig <- subset(res, padj < 0.1)
head(resSig[ order(resSig$log2FoldChange),])
head(resSig[ order(resSig$log2FoldChange, decreasing = TRUE), ])
#Save DEGS at padj < 0.1
write.csv(resSig,"d...@0.1.csv")
resSig0.05 <- subset(res, padj < 0.05)
#Save DEGS at padj < 0.01
write.csv(resSig0.05,"d...@0.05.csv")
Now I want to do gene set enrichment analysis by using topgo library and
also want to make a heatmap and Venn diagram. Please help me
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