TRANSCRIPTOME 2002: From Functional Genomics to Systems
Using Multivariate Regression Analysis to Detect Differential Co-Regulation of Transcriptions Between Subgroups of Medulloblastomas
Chun Cheng, Andrew R. Hallahan, James M.Olson, and Lue Ping Zhao, Divisions of Public Health Sciences and Clinical Research, Fred Hutchinson Cancer Research Center, Seattle, WA
Transcription profiles have been utilized to study the co-regulation of genes under different experimental conditions or from different patients. Here we present a regression-based method that quantitatively measures the strength of association between the genes and evaluates its statistical significance. Furthermore, this method can help identify the genes co-regulated in different fashions between two groups of samples. Using this method, we studied the transcription profiles of 60 medulloblastoma samples published recently. We focused on the co-regulation of molecules that have been shown to correlate with prognosis. These genes include the neurotrophin-3 receptor (TrkC), NeuroD3 and N-Myc. Discovery of genes associated with these molecules may shed light on the pathways important in determining prognosis of medulloblastoma. Using the transcription level of these molecules as explanatory variable respectively, we performed linear regression analysis on the transcription levels of all other genes. We identified co-regulated genes at a desired significance level. Furthermore, to study how the co-regulation of this group of genes differs in tumors with different prognostic outcomes or histological types, we used an indicator variable to denote the identity of a sample and performed multivariate regression analysis. The findings of these studies will be presented here.
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