TY - JOUR
T1 - Finding regulatory modules through large-scale gene-expression data analysis
AU - Kloster, M.
AU - Tang, C.
AU - Wingreen, N. S.
N1 - Funding Information:
We wish to thank J.Ihmels and N.Barkai for sharing their dataset, and Rahul Kulkarni for valuable discussions. C.T. acknowledges support from the National Key Basic Research Project of China (No. 2003CB715900).
PY - 2005/4/1
Y1 - 2005/4/1
N2 - Motivation: The use of gene microchips has enabled a rapid accumulation of gene-expression data. One of the major challenges of analyzing this data is the diversity, in both size and signal strength, of the various modules in the gene regulatory networks of organisms. Results: Based on the iterative signature algorithm [Bergmann,S., Ihmels,J. and Barkai,N. (2002) Phys. Rev. E 67, 031902], we present an algorithm - the progressive iterative signature algorithm (PISA) - that, by sequentially eliminating modules, allows unsupervised identification of both large and small regulatory modules. We applied PISA to a large set of yeast gene-expression data, and, using the Gene Ontology database as a reference, found that the algorithm is much better able to identify regulatory modules than methods based on high-throughput transcription-factor binding experiments or on comparative genomics.
AB - Motivation: The use of gene microchips has enabled a rapid accumulation of gene-expression data. One of the major challenges of analyzing this data is the diversity, in both size and signal strength, of the various modules in the gene regulatory networks of organisms. Results: Based on the iterative signature algorithm [Bergmann,S., Ihmels,J. and Barkai,N. (2002) Phys. Rev. E 67, 031902], we present an algorithm - the progressive iterative signature algorithm (PISA) - that, by sequentially eliminating modules, allows unsupervised identification of both large and small regulatory modules. We applied PISA to a large set of yeast gene-expression data, and, using the Gene Ontology database as a reference, found that the algorithm is much better able to identify regulatory modules than methods based on high-throughput transcription-factor binding experiments or on comparative genomics.
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U2 - 10.1093/bioinformatics/bti096
DO - 10.1093/bioinformatics/bti096
M3 - Article
C2 - 15513996
AN - SCOPUS:16344370048
SN - 1367-4803
VL - 21
SP - 1172
EP - 1179
JO - Bioinformatics
JF - Bioinformatics
IS - 7
ER -