期刊
BIOINFORMATICS
卷 27, 期 1, 页码 70-77出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btq593
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资金
- National Human Genome Research Institute [R01 HG003749]
- National Library of Medicine [R21 LM009662]
- National Institute of Environmental Health Sciences Center for Environmental Genetics [P30 ES06096]
- NATIONAL HUMAN GENOME RESEARCH INSTITUTE [R01HG003749] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES [P30ES006096] Funding Source: NIH RePORTER
- NATIONAL LIBRARY OF MEDICINE [R21LM009662] Funding Source: NIH RePORTER
Motivation: Functional enrichment analysis using primary genomics datasets is an emerging approach to complement established methods for functional enrichment based on predefined lists of functionally related genes. Currently used methods depend on creating lists of 'significant' and 'non-significant' genes based on ad hoc significance cutoffs. This can lead to loss of statistical power and can introduce biases affecting the interpretation of experimental results. Results: We developed and validated a new statistical framework, generalized random set (GRS) analysis, for comparing the genomic signatures in two datasets without the need for gene categorization. In our tests, GRS produced correct measures of statistical significance, and it showed dramatic improvement in the statistical power over other methods currently used in this setting. We also developed a procedure for identifying genes driving the concordance of the genomics profiles and demonstrated a dramatic improvement in functional coherence of genes identified in such analysis.
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