Standard adjustments for multiple hypothesis testing, such as the Bonferroni correction, are too conservative, as they assume that the tests are independent.
To determine statistical significance in a genome-wide study, adjustment for multiple hypotheses is usually applied.
In the current work, we sought to address these issues through appropriate statistical adjustment for multiple hypotheses.
Enrichment of predicted protein domains in each gene list was determined by a hypergeometric distribution with adjustment for multiple hypotheses testing by resampling methods implemented with FuncAssociate 2.0 using 10,000 simulations (Berriz et al. 2009).
In some cases, following FDR adjustment for multiple hypothesis testing, more than 1000 unique genes were either increased or decreased by HF diet (e.g., NZB males and females, A/J females; see Table 1).
Adjustment for multiple hypothesis testing was not undertaken [ 30].
PCBs 194 and 199 remained significant after adjustment for multiple hypothesis testing.
Adjustment for multiple hypothesis testing was not undertaken (Perneger, 1998), rather P-values <0.05 were considered statistically significant.
GSEA consists of three major steps: calculation of an enrichment score (ES), estimation of the significance level of the ES, and adjustment for multiple hypothesis testing [ 27].
Differences were considered statistically significant if p < 0.05 after adjustment for multiple hypothesis testing using the method of Benjamini and Hochberg (method = "BH") [ 46].
Statistical significance and adjustment for multiple hypothesis testing were determined by a permutation-based procedure with Π = 10,000 random permutations of the phenotypic status of the subjects.
