I'm learning to perform meta-analysis of affymetrix microarrays.
I tested 4 studies with package geneMeta.
The sample sizes of the 4 studies are very small:
|Study 76-||10 (1 outlier)||20|
|Study 1-||16||3 (1 outlier)|
1) I performed RMA normalization for each study seperately, excluded the outliers and made 4 ExpressionSets.
2) Then I performed non-specific gene filtering with 'nsFilter' for each ExpressionSet and
got 4 filtered ExpressionSets.
3) Matched the identifiers of 4 filtered ExpressionSets with 'ENTREZID' using 'intersect' and the 4 ExpressionSets had same rows (ENTREID).
4) Performed meta-analysis with 'GeneMeta'.
My questions are:
(1) Should I preprocess the expression matrix such as centralizing or scaling the expression intensities after RMA before 'step 3)'. Or should I preprocess the expression matrix with other methods?
(2) The sample sizes of the above studies are very small. Is it right for me to use the GeneMeta package to perform meta-analysis? How should I deal with studies with small sample sizes?
(3) In my test analysis with GeneMeta package. In the FDR plot, the y axis of FDR curve of the combined set (meta-analyzed set) was higher than those of 3 individual studies (Study 2a, Study 1-, Study 4-) and was only lower than that of one study (Study 76-). Theoretically, I think the FDR of the combined set (meta-analyzed set) should be lower than individual studies. What should I do to improve the analysis?
(4) I'm not good at statistics and I think I must have missed some necessary and important steps in my analysis. Could you please teach me which work I should do in addition to the above steps or which of the above steps are wrong?
I want to learn the workflow of performing meta-analysis.
Sorry for my questions if they are too basic.
Thank you very much!