You are correct that using the class labels in filtering is "cheating" and
could undermine the assumptions used in multiple testing correction. A
common method is to filter rows where fewer than N samples meet a given
threefold count, where N is the size of the smallest group (in your example
N = 3).
On Dec 10, 2012 11:53 PM, "James Perkins" <j.perkins@ucl.ac.uk> wrote:

> Hi all,
>
> I understand it is normal to filter out lowly expressed genes before
> performing differential expression analysis on RNA-seq data (e.g.,
> edgeR, DESeq).
>
> However I notice with such methods as edgeR, I find a number of genes
> where there is clearly one outlier that is causing the gene to be
> deemed significantly DE (thought the dispersion value is quite high):
>
> for example
>
>            control1 control2 control3 case1 case2 case3
> geneA           0          1          3        1        2        30
>
> Note that case3 is not an outlier sample, MDS plots show it to be like
> the other case samples, and the phenotype of the samples is as we
> would expect. I would say this gene is an outlier rather than the
> sample being an outlier, if that makes sense.
>
> Would it be fair to filter such examples out? I am thinking of a
> filtering rule such that:
>
> for each gene, if it has a number of counts below X for at least one
> case sample AND at least one control sample, discard it.
>
> This way I don't get rid of genes where the expression is high in case
> and very low (or unexpressed) in control and vice versa.
>
> However, I understand that this means I will be using the class labels
> for my filtering step, which I believe might lead to problems at the
> multiple testing correction stage.
>
> Thanks in advance for any help/ideas on this issue.
>
> Jim
>
> --
> James Perkins
> Institute of Structural and Molecular Biology
> Division of Biosciences
> University College London
> Gower Street
> London, WC1E 6BT
> UK
>
> email: j.perkins@ucl.ac.uk
>
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