A Novel Weighted Hybrid Method for Multiple Hypothesis Testing of Genomic Data

Multiple Hypothesis Testing presents challenges due to increased false discoveries when conducting statistical tests simultaneously. Despite the proposal of novel correction techniques, the procedure of selecting the most suitable method has remained a black box. The trade-off that arises from controlling false positives and negatives through different correction techniques underlines the need for a cohesive framework. This study addresses the above challenges with a special focus on gene expression data and evaluates six widely used Multiple Hypothesis Testing (MHT) methods, namely, Bonferroni, Holm, sequential goodness of fit (SGoF), Benjamini-Hochberg, Benjamini-Yekutieli, and Storey’s Q-values, across different scenarios to compare two independent groups. Our results show that Storey’s Q-value performs well with large effect sizes, whereas SGoF excels in low-effect scenarios. However, the Bonferroni and Holm methods offer high precision owing to the strict control of false positives. Recognizing the limitations of relying on a single method, we introduce a novel Weighted Hybrid Method (WHM), a decision-support framework that allows users to navigate between approaches rather than serving as a new statistical test. An innovative Significant Index Plot (SIP) is unveiled to assist in the detection of significant hypotheses across different methods. The framework was tested on four genomic datasets: gene expression in multiple sclerosis (GSE21942), myelodysplastic syndrome (GSE61853), alcohol-related gene expression (GSE52553), and age-related corneal transcriptomes (GSE58315), extending the usage to enable independent hypothesis weighting. A novel Python library, MultiDST, and a web interface were developed to enable researchers to apply the framework efficiently, improving the transparency of their findings.