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 pro-posal of novel correction techniques, the procedure of selecting the most suit-able method has remained a black box. The trade-off that arises from con-trolling 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 in-dependent 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 serv-ing as a new statistical test. An innovative Significant Index Plot (SIP) is un-veiled to assist in the detection of significant hypotheses across different meth-ods. 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 tran-scriptomes (GSE58315), extending the usage to enable independent hypoth-esis 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.