A classification based framework for quantitative description of large-scale microarray data.
Genome-wide surveys of transcription depend on gene classifications for the purpose of data interpretation. We propose a new information-theoretical-based method to: assess significance of co-expression within any gene group; quantitatively describe condition-specific gene-class activity; and systematically evaluate conditions in terms of gene-class activity. We applied this technique to describe microarray data tracking Escherichia coli transcriptional responses to more than 30 chemical and physiological perturbations. We correlated the nature and breadth of the responses with the nature of perturbation, identified gene group proxies for the perturbation classes and quantitatively compared closely related physiological conditions.
Sangurdekar
Dipen P
DP
Department of Chemical Engineering and Materials Science, University of Minnesota, Saint Paul, MN 55108, USA.
Srienc
Friedrich
F
Khodursky
Arkady B
AB
D049109:Cell Proliferation; D002965:Classification / Q000379:methods; D016000:Cluster Analysis; D004926:Escherichia coli / Q000187:drug effects / Q000235:genetics / Q000378:metabolism; D020869:Gene Expression Profiling / Q000379:methods*; D023281:Genomics / Q000379:methods; D007257:Information Theory; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D014158:Transcription, Genetic