Determination of strongly overlapping signaling activity from microarray data

BMC Bioinformatics
Ghislain BidautMichael F Ochs

Abstract

As numerous diseases involve errors in signal transduction, modern therapeutics often target proteins involved in cellular signaling. Interpretation of the activity of signaling pathways during disease development or therapeutic intervention would assist in drug development, design of therapy, and target identification. Microarrays provide a global measure of cellular response, however linking these responses to signaling pathways requires an analytic approach tuned to the underlying biology. An ongoing issue in pattern recognition in microarrays has been how to determine the number of patterns (or clusters) to use for data interpretation, and this is a critical issue as measures of statistical significance in gene ontology or pathways rely on proper separation of genes into groups. Here we introduce a method relying on gene annotation coupled to decompositional analysis of global gene expression data that allows us to estimate specific activity on strongly coupled signaling pathways and, in some cases, activity of specific signaling proteins. We demonstrate the technique using the Rosetta yeast deletion mutant data set, decompositional analysis by Bayesian Decomposition, and annotation analysis using ClutrFree. We determined f...Continue Reading

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Citations

Oct 27, 2009·Briefings in Bioinformatics·Michael F Ochs
Mar 9, 2010·PloS One·Andrew P HodgesYongqun He
Apr 9, 2010·International Journal of Data Mining and Bioinformatics·Andrew V Kossenkov, Michael F Ochs
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Aug 26, 2018·Trends in Genetics : TIG·Genevieve L Stein-O'BrienElana J Fertig

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Methods Mentioned

BETA
chips
chemical treatments
PCA

Software Mentioned

ClutrFree
Rosetta
Rosetta Inpharmatics
MathType
SAM
VERAandSAM

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