Effect of the absolute statistic on gene-sampling gene-set analysis methods.

Citation data:

Statistical methods in medical research, ISSN: 1477-0334, Vol: 26, Issue: 3, Page: 1248-1260

Publication Year:
2017
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Repository URL:
http://scholarworks.unist.ac.kr/handle/201301/18147
PMID:
25733546
DOI:
10.1177/0962280215574014
Author(s):
Nam, Dougu
Publisher(s):
SAGE Publications; SAGE PUBLICATIONS LTD
Tags:
Medicine; Mathematics; Health Professions; Gene-set analysis, absolute statistic, microarray analysis, false-positive control, genome-wide association study
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article description
Gene-set enrichment analysis and its modified versions have commonly been used for identifying altered functions or pathways in disease from microarray data. In particular, the simple gene-sampling gene-set analysis methods have been heavily used for datasets with only a few sample replicates. The biggest problem with this approach is the highly inflated false-positive rate. In this paper, the effect of absolute gene statistic on gene-sampling gene-set analysis methods is systematically investigated. Thus far, the absolute gene statistic has merely been regarded as a supplementary method for capturing the bidirectional changes in each gene set. Here, it is shown that incorporating the absolute gene statistic in gene-sampling gene-set analysis substantially reduces the false-positive rate and improves the overall discriminatory ability. Its effect was investigated by power, false-positive rate, and receiver operating curve for a number of simulated and real datasets. The performances of gene-set analysis methods in one-tailed (genome-wide association study) and two-tailed (gene expression data) tests were also compared and discussed.