Is Variance Stabilization Neccessary When Using Sam?
2
5
Entering edit mode
12.2 years ago
Sebastian ▴ 50

I'm working on some miRNA microarray data and I'm wondering if I have to do variance stabilization before I use SAM (significance analysis of microarrays)? If I understand the SAM method correctly it already computes a term to minimize the variance of intensities.

microarray sam • 2.2k views
ADD COMMENT
5
Entering edit mode
12.2 years ago

SAM does not minimize the variance per se. The SAM statistic d for two-class unpaired data is (difference in means) / (standard deviation + S0 ), a modified T statistic. The constant term S0 in the denominator reduces the score d of genes where the difference in means between the two classes is very small. Such genes are usually expressed at very low levels. The signal-to-noise ratio of genes expressed at low levels is very low, so the practical consequence is that you avoid calling them significantly differentially expressed. If you compare SAM results to a raw T statistic you can get a feel for what the difference is.

See their paper (Tusher PNAS 2001) and the SAM manual for gory details on how S0 is calculated.

ADD COMMENT
3
Entering edit mode
12.2 years ago
toni ★ 2.2k

I am not really used to analyze miRNA microarrays but variance stabilization is about normalizing your dataset whereas SAM methodology is about doing a supervised analysis between two classes of interest. You can do a variance stabilization before a SAM analysis unless you already normalized your data with another method (like RMA for RNA microarrays).

This is a question you should better ask to the BioConductor community through the BioC mailing list.

best,

tony

ADD COMMENT

Login before adding your answer.

Traffic: 1285 users visited in the last hour
Help About
FAQ
Access RSS
API
Stats

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6