Paper Info
Title
Model-free unsupervised gene set screening based on information enrichment in expression profiles.
Abstract
A number of unsupervised gene set screening methods have recently been developed for search of putative functional gene sets based on their expression profiles. Most of the methods statistically evaluate whether the expression profiles of each gene set are fit to assumed models: e.g. co-expression across all samples or a subgroup of samples. However, it is possible that they fail to capture informative gene sets whose expression profiles are not fit to the assumed models.To overcome this limitation, we propose a model-free unsupervised gene set screening method, Matrix Information Enrichment Analysis (MIEA). Without assuming any specific models, MIEA screens gene sets based on information richness of their expression profiles. We extensively compared the performance of MIEA to those of other unsupervised gene set screening methods, using various types of simulated and real data. The benchmark tests demonstrated that MIEA can detect singular expression profiles that the other methods fail to find, and performs broadly well for various types of input data. Taken together, this study introduces MIEA as a broadly applicable gene set screening tool for mining regulatory programs from transcriptome data.
Year
DOI
Venue
2010
10.1093/bioinformatics/btq592
Bioinformatics
Keywords
Field
DocType
singular expression profile,assumed model,model-free unsupervised gene,unsupervised gene,various type,gene set,applicable gene,information enrichment,informative gene set,putative functional gene,expression profile
Data mining,Gene,Computer science,Transcriptome,Bioinformatics,Gene expression profiling
Journal
Volume
Issue
ISSN
26
24
1367-4811
Citations 
PageRank 
References 
0
0.34
5
Authors
7
Name
Order
Citations
PageRank
Atsushi Niida1605.95
Seiya Imoto297584.16
Rui Yamaguchi318026.49
Masao Nagasaki436826.22
André Fujita522617.14
Teppei Shimamura6388.80
Satoru Miyano72406250.71