ABSTRACT
Here we provide additional information on the Materials and Methods as well as the statistical software code used for the random forest analysis of following article:
Mehrian Shai R, Chen CD, Shi T, Horvath S, Nelson SF, Reichardt JKV, Sawyers CL (2007) IGFBP2 is a Biomarker for PTEN Status and PI3K/Akt Pathway Activation in Glioblastoma and Prostate Cancer. Proc Natl Acad Sci U S A. 2007 Mar 19
For the journal article, click here
We use random forest predictors (Breiman 2001) to find genes that are associated with PTEN status in brain cancer (glioblastoma multiform) and prostate tumors. In our data, we find that 10 probesets are associated with PTEN status irrespective of the tissue origin. While our main analysis uses a random forest importance measure to implicate these 10 probesets, we show that they are also statistically significant according to a Kruskal Wallis test or a Student T-test. We use supervised hierarchical clustering and a classical multi-dimensional scaling plot to visualize the relationship between the microarrays (patients).
CONTENTS
Supplement (Word version PDF version)
Microarray data (Zipped Excel File)
Gene Summary data (Zipped Excel File)
Raw, unnormalized, Affymetrix .cel files (Zipped)
As an aside, we mention that random forest predictors can also be used for unsupervised learning (clustering). Read here.
2007-03-26
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comments to: shorvath@mednet.ucla.edu