[BioC] edgeR on microRNA data
Helena Persson
helena.persson at ki.se
Mon Oct 3 10:56:40 CEST 2011
Dear Gordon,
> Min. 1st Qu. Median Mean 3rd Qu. Max.
>0.09599 0.18370 0.24550 0.28160 0.31190 2.23000
> Min. 1st Qu. Median Mean 3rd Qu. Max.
> 0.1022 0.1894 0.2534 0.2916 0.3183 2.1890
>A small number of the dispersions are alarmingly high. Have you filtered
>the gene list as I suggested? This may remove the "variance outliers".
Depending on how I filter the counts table for expression there are ~5 genes with very high dispersion estimates. These are the genes I have been referring to as variance outliers – in the table for differential expression from DESeq they have very high residual variances. By raising the expression threshold or the number of required samples within reasonable limites I can remove a few, but not the last 3 genes. Of course this also removes a substantial number of genes from the counts table.
I attach plots for the variance estimates generated with plotMeanVar().
Best,
Helena
________________________________________
Från: Gordon K Smyth [smyth at wehi.EDU.AU]
Skickat: den 3 oktober 2011 05:42
Till: Helena Persson
Kopia: Bioconductor mailing list
Ämne: Re: edgeR on microRNA data
On Mon, 3 Oct 2011, Helena Persson wrote:
> The tagwise dispersions (cds1 <- estimateTagwiseDisp(cds1, prior.n=1,
> trend=TRUE, prop.used=0.3, grid=FALSE)):
>
> Min. 1st Qu. Median Mean 3rd Qu. Max.
>0.09599 0.18370 0.24550 0.28160 0.31190 2.23000
> Min. 1st Qu. Median Mean 3rd Qu. Max.
> 0.1022 0.1894 0.2534 0.2916 0.3183 2.1890
A small number of the dispersions are alarmingly high. Have you filtered
the gene list as I suggested? This may remove the "variance outliers".
Does a plot of the dispersions versus abundance suggest that the trend is
smooth?
Best wishes
Gordon
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