Entering edit mode
I am trying to analysis 40 samples htseq-count data (20 healthy and 20 diseases) with deseq2
I have read the manual of the package but I have the data ready in my workspace . It is a dataframe of 30000*40
The first 20 columns are my healthy and the second 20 are the diseases
Is there anyone who can help me how to use th deseq2 for this analysis ?
I know already that one can use like this
dds <- DESeqDataSetFromHTSeq(countData = cts,
colData = coldata,
design= ~ batch + condition)
dds <- DESeq(dds)
resultsNames(dds) # lists the coefficients
res <- results(dds, name="condition_trt_vs_untrt")
# or to shrink log fold changes association with condition:
res <- lfcShrink(dds, coef="condition_trt_vs_untrt")
@Michael Love Thanks, I have read but still could not use it properly. should I follow from 4.2 Starting from count matrices ? my question is that I have put together the samples and so I have one column with my gene ID and 40 columns of counts (20 conrtol and 20 treated) Can you please tell me from which section I should follow ?
Yes, you have a count matrix. You just need to provide only the columns that have counts to the first argument of DESeqDataSetFromMatrix. You can later add the gene IDs as rownames of the DESeqDataSet.
I'd suggest working with someone with experience in R perhaps, if the material is not helping you to get started. The support site is more design for specific software questions, and we have lots of material and documentation online for how to get started.
@Michael Love Do you tihnk that htseq-count is ok for expression analysis? what if I take log2 of htseq-count and then use limma? the problem with your package is that i cannot follow the parts due to the fact that you have created a table inside your package. I am pretty good at R , I just want to know which steps people take to get htseq-count from A to Z (from count values to up and down regulated genes)
There isn't a specific software question here, so I'm going to stop replying. We have plenty of information in our workflow about approaches to doing differential gene expression, and I've already given you pointers above on how to proceed.