[BioC] edgeR on microRNA data

[Helena Persson]

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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