Please read the posting guide linked below for how to post questions
that are likely to receive useful responses. "Help me do a gene
enrichment analysis" does not conform to the pg. We usually expect
posters to offer their best attempts, preferably in a reproducible
example ("a reprex" -- see here for discussion:
https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example
), to provide help.
Also note that per the pg:
"For questions about functions in standard packages distributed with R
(see the FAQ Add-on packages in R), ask questions on R-help.
If the question relates to a contributed package , e.g., one
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can also use find("functionname") and
packageDescription("packagename") to find this information. Only send
such questions to R-help or R-devel if you get no reply or need
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Finally, note that "topgo" is a Bioconductor **package** (not
"library"), and you should almost certainly post questions about its
use on their support site not here:
https://www.bioconductor.org/help/
(Do read their pg before posting, of course)
Bert Gunter
"The trouble with having an open mind is that people keep coming along
and sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Mon, Aug 30, 2021 at 9:22 AM Anas Jamshed <anasjamshed1...@gmail.com> wrote:
>
> 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
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
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