SEMFA: A General Framework for Inferring Statistical Significance of Mahalanobis Similarity between Multi-Omics Profiled Samples Built on Multiple Factor Analysis
Jiali Han, Wenting Luo, Edwin Baldwin, Hao Helen Zhang, Lingling An, Jian Liu, Haiquan Li
SEMFA infers the statistical significance of Mahalanobis similarity between multi-omics samples using multiple factor analysis, showing effectiveness in both Gaussian and non-Gaussian scenarios.
bioRxiv · Jun 24, 2026 · PreprintBioinformatics