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

[shirley zhang]

Dear list,

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?

Thanks a lot,
Shirley