On Jan 11, 2008 8:32 AM, Paolo Innocenti <paolo.innocenti@ebc.uu.se> wrote:

> Hi all,
>
> I have a set of 15 Affymetrix chips:
> 4 treatments, 2 technical replicates of 2 biological replicates for each
> treatment (one chips has been excluded, all the others are really good
> quality).
>
> After running rma(), when i try to filter the ExpressionSet
>
>  >eset <- rma(mydata)
>  >eset.f <- nsFilter(eset)$eset
>
> it removes 13047 features for low variance, leaving 171 features in my
> dataset.
>
> $numDupsRemoved
> [1] 3
>
> $numLowVar
> [1] 13047
>
> $feature.exclude
> [1] 3
>
> $numRemoved.ENTREZID
> [1] 786
>
> It is quite strange, because another analysis (few years ago) on the
> same dataset revealed more than 1000 DE genes.
> Now, I can just set a less stringent cutoff, but is it reasonable to go
> on with the analysis with 171 features? Is it realistic to get these
> results with the default parameters of nsFilter? Obviously, it depends
> by what I am expecting and by the experimental design... well, i was
> expecting some more dramatic changes in expression. At the end of the
> analysis, I end up with ~60 differentially expressed probesets (lfc=1,
> p.value=0.05, adjustment method=BH)
>

Sounds like you probably need to take a closer look at the data.  I have not
used the nsFilter function, but it looks like the default variance function
is IQR (interquartile range) and the default cutoff is 0.5 for that function
value is 0.5.  If nearly 99% of your probes have an IQR<0.5, I would look at
the data quality closely to see if there are data quality issues or
preprocessing steps that do not make sense (can't tell what was done before
RMA).


>
> Second question: Is it informative to test for gene sets (GSEA) on 171
> genes, or would be better not to filter the expressionset?
>

I think the issue is not whether or not it is possible to use GSEA on 171
genes or not, but what preprocessing makes the most sense for your data.  I
doubt that a preprocessing method that removes 99% of the data is the best
one to use, though.

Sean

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