TY - JOUR
T1 - Model-free unsupervised gene set screening based on information enrichment in expression profiles
AU - Niida, Atushi
AU - Imoto, Seiya
AU - Yamaguchi, Rui
AU - Nagasaki, Masao
AU - Fujita, André
AU - Shimamura, Teppei
AU - Miyano, Satoru
N1 - Funding Information:
Funding: This work was supported by the Grant-in-Aid for the Global COE Program ‘Center of Education and Research for the Advanced Genome-Based Medicine’ from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan.
PY - 2010/12
Y1 - 2010/12
N2 - Motivation: 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. Results: 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.
AB - Motivation: 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. Results: 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.
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U2 - 10.1093/bioinformatics/btq592
DO - 10.1093/bioinformatics/btq592
M3 - Article
C2 - 20959379
AN - SCOPUS:79951783217
VL - 26
SP - 3090
EP - 3097
JO - Bioinformatics
JF - Bioinformatics
SN - 1367-4803
IS - 24
ER -