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Meta-analysis based on control of false discovery rate: combining yeast ChIP-chip datasets
Author(s) -
Saumyadipta Pyne,
Bruce Futcher,
Steve Skiena
Publication year - 2006
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btl439
Subject(s) - false discovery rate , computer science , false positive paradox , data mining , false positives and false negatives , bioconductor , multiple comparisons problem , dna microarray , computational biology , machine learning , biology , statistics , gene , mathematics , genetics , gene expression
High-throughput microarray technology can be used to examine thousands of features, such as all the genes of an organism, and measure their expression. Two important issues of microarray bioinformatics are first, how to combine the significance values for each feature across experiments with high statistical power, and second, how to control the proportion of false positives. Existing methods address these issues separately, in spite of their linked usage.

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