Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data.
Much of a cell's activity is organized as a network of interacting modules: sets of genes coregulated to respond to different conditions. We present a probabilistic method for identifying regulatory modules from gene expression data. Our procedure identifies modules of coregulated genes, their regulators and the conditions under which regulation occurs, generating testable hypotheses in the form 'regulator X regulates module Y under conditions W'. We applied the method to a Saccharomyces cerevisiae expression data set, showing its ability to identify functionally coherent modules and their correct regulators. We present microarray experiments supporting three novel predictions, suggesting regulatory roles for previously uncharacterized proteins.
Computer Science Department, Stanford University, Stanford, California, 94305, USA. eran@cs.stanford.edu
Eran
E
Segal
Michael
M
Shapira
Aviv
A
Regev
Dana
D
Pe'er
David
D
Botstein
Daphne
D
Koller
Nir
N
Friedman
D000465:Algorithms; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D015966:Gene Expression Regulation, Fungal; D005800:Genes, Fungal; D005809:Genes, Regulator; D008957:Models, Genetic; D015233:Models, Statistical; D009154:Mutation; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D012441:Saccharomyces cerevisiae / Q000235:genetics; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics
D029701:Saccharomyces cerevisiae Proteins