Mining gene expression data by interpreting principal components |
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Authors: | Joseph C Roden Brandon W King Diane Trout Ali Mortazavi Barbara J Wold Christopher E Hart |
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Institution: | (1) Jet Propulsion Laboratory, California Institute of Technology, Pasadena, USA;(2) Division of Biology, California Institute of Technology, Pasadena, USA |
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Abstract: | Background There are many methods for analyzing microarray data that group together genes having similar patterns of expression over
all conditions tested. However, in many instances the biologically important goal is to identify relatively small sets of
genes that share coherent expression across only some conditions, rather than all or most conditions as required in traditional
clustering; e.g. genes that are highly up-regulated and/or down-regulated similarly across only a subset of conditions. Equally
important is the need to learn which conditions are the decisive ones in forming such gene sets of interest, and how they
relate to diverse conditional covariates, such as disease diagnosis or prognosis. |
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Keywords: | |
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