Effect of the absolute statistic on gene-sampling gene-set analysis methods.
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.
Department of Biological Sciences and Department of Mathematical Sciences, UNIST, Ulsan, Republic of Korea.
Dougu
D
Nam
D001827:Body Height / Q000235:genetics; D045744:Cell Line, Tumor; D005189:False Positive Reactions; D020869:Gene Expression Profiling / Q000379:methods*; D016158:Genes, p53 / Q000235:genetics; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D007723:Korea; D017711:Nonlinear Dynamics; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012372:ROC Curve
Gene-set analysis; absolute statistic; false-positive control; genome-wide association study; microarray analysis