RNA-Seq analysis with standard p-value

Question

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I have analyzed RNA-Seq data using standard thresholds of logFC 2 and p-vlaue 0,05 using Deseq2 package. I am expecting one particular candidate gene which I have found from qPCR. In RNA-Seq resutls when I look for that gene logFC is 4 and padjval is 0,00. I am not able to understand why so ?

[firatuyulur]

I think why you are confused is because you do not see the (-) sign at your padjval column. That (-) sign you are not seeing is there to show you 10's power. so when its -5, it is 10^-5, which is a small number between 1 and 0. when padj is 0, it is the highest significance value that you can get, so there is no confusion in your results. spend a bit more time on understanding and interpreting results rather than simply filtering them.

[Devon Ryan]

The adjusted p-value column isn't log10 transformed...

[WouterDeCoster]

What's unexpected about that finding? padjval indicates very significant, logFC is reasonably big.

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In simple way..as p-value is 0 its not in my candidate gene list because of which I have to exclude it. Were as I expect it to be in the candidate gene list. So, I am trying to find out reason/s of p-value being 0.Yes logFC is higher.

[WouterDeCoster]

Pvalue = 0 means too significant to represent by a tiny number. So it's very significant. And smaller than 0.05. So it's differentially expressed. Why would you exclude it then?

[Devon Ryan]

What p-value threshold are you using where 0 is not less than it?

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Using p-value 0.05. I didnt get you what you mean?

[WouterDeCoster]

So

0 < 0.05 => TRUE

therefore this gene is significant and should not be excluded

[####]

Thanks all for the reply I understand p=0 is highly significant.

now another case of gene where there is no p-value at all, not even zero , what does this says?

[Devon Ryan]

There are a couple reasons that one can get an NA adjusted p-value in DESeq2, the most common of which is due to independent filtering. I will quote from help(results):

On p-values:

By default, independent filtering is performed to select a set of genes for multiple test correction which maximizes the number of adjusted p-values less than a given critical value alpha (by de-fault 0.1). See the reference in this man page for details on independent filtering. The filter used for maximizing the number of rejections is the mean of normalized counts for all samples in the dataset. Several arguments from the filtered_p function of the genefilter package (used within the results function) are provided here to control the independent filtering behavior. In DESeq2 version >= 1.10, the threshold that is chosen is the lowest quantile of the filter for which the number of rejections is close to the peak of a curve fit to the number of rejections over the filter quantiles. ’Close to’ is defined as within 1 residual standard deviation. The adjusted p-values for the genes which do not pass the filter threshold are set to NA.

By default, results assigns a p-value of NA to genes containing count outliers, as identified using Cook’s distance. See the cooksCutoff argument for control of this behavior. Cook’s distances for each sample are accessible as a matrix "cooks" stored in the assays() list. This measure is useful for identifying rows where the observed counts might not fit to a Negative Binomial distribution.

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Thanks for this information Devon , I have exactly followed the same thing from Deseq2 vignette and have disable the independent filtering and cooksCutoff both to avoid default filtering of such candidate genes and it worked fine.

[EagleEye]

What do you mean by 'no p-value at all'. There must be some value like 'NA' or ''NaN' ...

[####]

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[Devon Ryan]

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