2020/12/01 by Angela Andreella, Andreella, Angela, Jesse Hemerik +7 · 3 citations
Biochemistry, Genetics and Molecular Biology · Mathematics · Medicine · #Applications (stat.AP) #FOS: Computer and information sciences #Gene expression and cancer classification #MRI in cancer diagnosis #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.2012.00368
openalex publication_date 2020/12/01 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
We propose a permutation-based method for testing a large collection of hypotheses simultaneously. Our method provides lower bounds for the number of true discoveries in any selected subset of hypotheses. These bounds are simultaneously valid with high confidence. The methodology is particularly useful in functional Magnetic Resonance Imaging cluster analysis, where it provides a confidence statement on the percentage of truly activated voxels within clusters of voxels, avoiding the well-known spatial specificity paradox. We offer a user-friendly tool to estimate the percentage of true discoveries for each cluster while controlling the family-wise error rate for multiple testing and taking into account that the cluster was chosen in a data-driven way. The method adapts to the spatial correlation structure that characterizes functional Magnetic Resonance Imaging data, gaining power over parametric approaches.