Results analysis of differential gene expression using DESeq2 package
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@c1d5a219
Last seen 7 weeks ago
United Arab Emirates

I wanted to do differential expression analysis of about 10,340 genes collected from smoking vs non smoking mothers and the upregulated and downregulated genes results just does not make sense with both being 0. Is there something I don't understand or something I'm missing? Moreover, the data is pure counts data without any normalization so could that also be the issue? Plus do I need to adjust the alpha value because most of the adjusted p-value are really high.

Code should be placed in three backticks as shown below

dseq <- DESeqDataSetFromMatrix(countData=exp30,
colData=meta30,
design= ~ smoking_status)
dseq
dseq<- DESeq(dseq)
result <-results(dseq)
result
summary(result)


out of 10340 with nonzero total read count adjusted p-value < 0.1 LFC > 0 (up) : 0, 0% LFC < 0 (down) : 1, 0.0097% outliers [1] : 0, 0% low counts [2] : 0, 0% (mean count < 6) [1] see 'cooksCutoff' argument of ?results [2] see 'independentFiltering' argument of ?results

RnaSeqSampleSize statistics DESeq2 • 131 views
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You need to add any details for help. No DEGs can be biological or technical (confounders/noisy data, wrong input data type) etc.

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swbarnes2 ★ 1.2k
@swbarnes2-14086
Last seen 1 day ago
San Diego

Pure count data is an acceptable input for DESeq. Follow the vignette to generate a PCA plot. If you see no separation between your sample types in the first PCAs, it might be that your condition really doesn't do anything big enough to see in your experiment.