Question: FNR in RNASeq
0
gravatar for prat
7 weeks ago by
prat0
prat0 wrote:

Why are false neg rates usually not seen/used for RNASeq analysis? My guess is that the TP is not known... Is it possible to still calculate/estimate the FNR in DESeq2/edgeR?

edger deseq2 • 108 views
ADD COMMENTlink modified 7 weeks ago by Gordon Smyth38k • written 7 weeks ago by prat0
Answer: FNR in RNASeq
0
gravatar for Michael Love
7 weeks ago by
Michael Love24k
United States
Michael Love24k wrote:

One way to get an idea about possible false negative rate is to conduct an RNA-seq power analysis. There are a number of Bioconductor packages for this. There are various dimensions and parameters at play, including number of samples, sequencing depth, biological variability across replicates, and of course the true effect size.

ADD COMMENTlink written 7 weeks ago by Michael Love24k
Answer: FNR in RNASeq
0
gravatar for Gordon Smyth
7 weeks ago by
Gordon Smyth38k
Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia
Gordon Smyth38k wrote:

No, it isn't possible to estimate the FNR for real data. Computing the FNR is only possible in a simulation study or with artificially constructed data for which you know all the TPs.

You could use the propTrueNull() function of the statmod package to estimate the total number of null hypotheses that are actually true, and that would give you some idea of now many false nulls have failed to reach your significance cutoff. That does rely on the p-values being uniformly distributed for truly non-DE genes, which will never be exactly true.

As Michael has said, you can get a general idea from a power analysis of what FNR might be typical for an experiment like yours, given assumptions about effect size etc, but that will not yield an estimate of FNR for any specific dataset.

ADD COMMENTlink modified 7 weeks ago • written 7 weeks ago by Gordon Smyth38k
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