[BioC] besides batch effects, whether adjust for specific quality metric variables (pos_control_mean, neg_control_mean, etc) in the analysis models

Kasper Daniel Hansen kasperdanielhansen at gmail.com
Tue Dec 20 15:48:29 CET 2011


On Tue, Dec 20, 2011 at 9:27 AM, shirley zhang <shirley0818 at gmail.com> wrote:
> I have about 1000 samples run on Affy's exon gene expression arrays.
> For differential analysis, I was suggested by one of our statistician
> that in my model, besides batch effects, I have to adjust for the
> following technical variables.
>
> RNA concentration
> RNA Quality (RIN number)
> cell counts
>
> all_probeset_mean
> pos_control_mean
> pos_control_stdev
> neg_control_mean
> neg_control_stdev
> pos_control_mad_residual_mean
> all_probeset_stdev
> all_probeset_rle_stdev
> all_probeset_rle_mean
> spike-in control variables
>
> I have adjusted the first 3 variables in my previous analysis besides
> treating batch effect as a random effect, but for other variables, I
> think they are quality metrics to check whether the quality of the
> Affy array data is high or low during processing the chip
> (hybridization, scanning, etc.).
>
> I've also tried to use principle components and SVA method to deal
> with hidden variables, but I have not heard about to adjust these
> specific  quality metric variables (pos_control_mean,
> neg_control_mean, etc) in the analysis models.
>
> Could anybody give me more comments and suggestions on this?

I don't think your local statistician really intended for you to
control for these variables.  But really, why don't you ask him/her
again?  That is going to be much more profitable than for us to guess
at what the intention is.

Kasper



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