Question: EdgeR (TMM): Samples with outlier but still show extremely low p-value and FDR
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gravatar for Joe
3 months ago by
Joe30
USA
Joe30 wrote:

please see my data here:

https://user-images.githubusercontent.com/20710640/34529987-ba9f1414-f07b-11e7-913a-3ea787771a6e.JPG

https://github.com/Jinggg2016/NGS/issues/4

These are not raw data but normalized after edgeR. I list the first few genes with highest fold change, and found one sample is definitely a outlier (highlight in yellow), which cause high fold change (If I remove this outlier, the fold change is only 2 fold-ish.) I am so surprised that the p value and FDR are both extremely small even with an outlier.

Is it common issue when use edgeR for differential expression?

If it is a real issue, how could I find out outlier if I have a large set samples (eg, >100 samples) for data analysis?

We usually use DEseq2 for DE, DEseq2 can identify outlier and report NA for p value.

Thanks,

ADD COMMENTlink modified 9 weeks ago by swbarnes23.4k • written 3 months ago by Joe30
0
gravatar for vm.higareda
9 weeks ago by
vm.higareda10
vm.higareda10 wrote:

Hello

Did you resolve your problem?

I have similar a behaviour with use edgeR. If i have one outlier in one of my four biological replicates the program takes it as DE gene. I don't understand why this happen, but seem to be common

am thinking to changue to deseq2

ADD COMMENTlink written 9 weeks ago by vm.higareda10
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gravatar for swbarnes2
9 weeks ago by
swbarnes23.4k
United States
swbarnes23.4k wrote:

A tiny p-value means that the software is very sure the difference between the groups is real. It has nothing at all to do with how large the difference itself is.

ADD COMMENTlink written 9 weeks ago by swbarnes23.4k

But even if you see one replica is an outliers as in your example?¿ Did you trust in that gene?

ADD REPLYlink written 8 weeks ago by vm.higareda10
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