where the gene is overexpressed and down regulated while doing DESeq2 analysis
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Dinesh • 0
@fa98e69a
Last seen 3 days ago
India

Hi,

I used DEseq2 for estimation of differentially gene expression in iPS cells and CMs cells

my design was first sample_name cell_type condition SRR1 iPSCs d0 SRR2 iPSCs d0 SRR3 iPSCs d0 SRR1 CMs d15 SRR2 CMs d15 SRR3 CMs d15

I got results properly. dds<- DESeqDataSetFromMatrix(countData = data, colData = pheno, design = ~ cell_type)

I have got fold changes in the results.

I am confused to understand where the gene expression is higher?

may anyone tell me the genes with -ive fold change are downregulated in CMs or iPSCs?

thanks in advance

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DESe DESeq2 • 69 views
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What is the complete code you used and what does your output look like ?

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@mikelove
Last seen 7 hours ago
United States

Interpreting results is covered in depth in the basic workflow, if you pull up the vignette, the workflow is linked to from the abstract:

vignette("DESeq2")

Might be good for you to look over the vignette as well.

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