VSClust: Feature-based variance-sensitive clustering of omics data.

We present VSClust that accounts for feature-specific variance. Based on an algorithm derived from fuzzy clustering, VSClust unifies statistical testing with pattern recognition to cluster the data into feature groups that more accurately reflect the underlying molecular and functional behavior. We apply VSClust to artificial and experimental data sets comprising hundreds to more than 80,000 features across 6-20 different conditions including genomics, transcriptomics, proteomics and metabolomics experiments. VSClust avoids arbitrary averaging methods, outperforms standard fuzzy c-means clustering and simplifies the data analysis workflow in large-scale omics studies. Availability: Download VSClust at https://bitbucket.org/veitveit/vsclust or access it through computproteomics.bmb.sdu.dk/Apps/VSClust. Contact: veits@bmb.sdu.dk. Supplementary information: Supplementary data are available at Bioinformatics online. PMID: 29635359 [PubMed - as supplied by publisher]
Source: Genomics Proteomics ... - Category: Genetics & Stem Cells Authors: Tags: Bioinformatics Source Type: research