SuMO-Fil: Supervised multi-omic filtering prior to performing network analysis

• Published in: PloS one 2021-08, Vol.16 (8), p.e0255579-e0255579
• Main Authors: Towle-Miller, Lorin M, Miecznikowski, Jeffrey C, Zhang, Fan, Tritchler, David L
• Format: Article
• Language: English
• Subjects: Algorithms; Analytical methods; Biological research; Biology and life sciences; Biology, Experimental; Computer and Information Sciences; Correlation; Correlation analysis; Datasets; DNA methylation; Engineering and Technology; Filtration; Gene expression; Information science; Medicine and Health Sciences; Methods; Network analysis; Physical Sciences; Power consumption; Principal components analysis; Research and Analysis Methods; Similarity; Statistical analysis; Systems biology
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0255579

Multi-omic analyses that integrate many high-dimensional datasets often present significant deficiencies in statistical power and require time consuming computations to execute the analytical methods. We present SuMO-Fil to remedy against these issues which is a pre-processing method for Supervised Multi-Omic Filtering that removes variables or features considered to be irrelevant noise. SuMO-Fil is intended to be performed prior to downstream analyses that detect supervised gene networks in sparse settings. We accomplish this by implementing variable filters based on low similarity across the datasets in conjunction with low similarity with the outcome. This approach can improve accuracy, as well as reduce run times for a variety of computationally expensive downstream analyses. This method has applications in a setting where the downstream analysis may include sparse canonical correlation analysis. Filtering methods specifically for cluster and network analysis are introduced and compared by simulating modular networks with known statistical properties. The SuMO-Fil method performs favorably by eliminating non-network features while maintaining important biological signal under a variety of different signal settings as compared to popular filtering techniques based on low means or low variances. We show that the speed and accuracy of methods such as supervised sparse canonical correlation are increased after using SuMO-Fil, thus greatly improving the scalability of these approaches.