Dear Dr. Huber,
Thank you for the advice. I have tried the script that you have advised to
use. As you mentioned I have used the script after the normalization, but
that has shown the following error, which I do not understand, whether I am
using in the right way.
MA<-normalizeBetweenArrays(log2(Rgene$G), method="quantile")# normalization
rs = rowSds(MA)
fx = fx[ rs > quantile(rs, 0.05), ]
Error: object "fx" not found
Can you advise me on the same.
Thanks in advance.
Abhilash
On Fri, Jul 11, 2008 at 4:06 AM, Wolfgang Huber wrote:
> Hi Abhilash
>
>
> I am working with single color data from Agilent platform. After the limma
>> analysis the adjusted p values were higher than 5% of FDR. At this
>> instance
>> I am thinking of filtering the genes using genefilter. As my data set
>> contains only raw intensities of normal and test before the normalization,
>> where I am uisng 'normalizeBetweenArrays' command after log transforming
>> the
>> data.
>> In this scenario I am quite confused whether I should use the filter
>> functions prior to normalization of after the normalization but efore
>> fitting the linear model?
>> As my data is not an expressionSet I cannot use the nonfilter commands, in
>> this case any suggestions of using other filtering methods?
>>
>> Appreciate the suggestions
>>
>>
> Such filtering is performed after normalisation, but it is essential that
> the filter criterion does *not use the sample annotations*. E.g. you can use
> for each gene the overall variance or IQR across the experiment.
>
> If x is a matrix with rows=genes and columns=samples, then this can be as
> simple as:
>
> rs = rowSds(x)
> fx = fx[ rs > quantile(rs, lambda), ]
>
> where rowSds is in the genefilter package, and lambda is a parameter
> between 0 and 1 that contains your belief in what fraction of probes on the
> array correspond to target molecules that are never expressed in the
> conditions you study.
>
> Also note that after such filtering, strictly speaking, the nominal
> p-values from the subsequent testing could be too small - but one can show
> that in typical microarray applications the bias is negligible (compared to
> the impact of other effects), and in any case the p-values can be used for
> ranking.
>
> Best wishes
> Wolfgang
>
>
> --
> ----------------------------------------------------
> Wolfgang Huber, EMBL-EBI, http://www.ebi.ac.uk/huber
>
--
Regards,
Abhilash
[[alternative HTML version deleted]]