Differential gene expression analysis results
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Last seen 13 days ago

can anyone please help me with the outlier in the given results ...


out of 31024 with nonzero total read count adjusted p-value < 0.1 LFC > 0 (up) : 51, 0.16% LFC < 0 (down) : 233, 0.75% outliers 1 : 578, 1.9% low counts [2] : 6588, 21% (mean count < 4) 1 see 'cooksCutoff' argument of ?results [2] see 'independentFiltering' argument of ?results ...

the PCA plot of my data is as follow enter image description here

DESeq2 DifferentialExpression • 203 views
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ATpoint ★ 4.2k
Last seen 23 minutes ago

Pease do not open multiple posts for the same issue: deseq2 results

The support site is not meant for hands-on guidance but for technical help and bugs with the packages.

What you see is that there is essentially no separation between groups. The outlier is not the biggest issue here. Remove and if you want and see if that improves outcome. If that is human data then other confounders might interfere here. See DESeq2 vignette for this, e.g. RUVseq. See also https://github.com/mikelove/preNivolumabOnNivolumab on how to investigate and correct a human dataset for potential confounders.

Note that you should seek local guidance to tackle your analysis. Please do not crosspost. I've seen this Q at least once at biostars as well, and if you crosspost at least indicate it by leaving a link.


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