MOVIE: Multi-Omics VIsualization of Estimated contributions

Abstract

SummaryThe growth of multi-omics datasets has given rise to many methods for identifying sources of common variation across data types. The unsupervised nature of these methods makes it difficult to evaluate their performance. We present MOVIE, Multi-Omics Visualization of Estimated contributions, as a framework for evaluating the degree of overfitting and the stability of unsupervised multi-omics methods. MOVIE plots the contributions of one data type against another to produce contribution plots, where contributions are calculated for each subject and each data type from the results of each multi-omics method. The usefulness of MOVIE is demonstrated by applying existing multi-omics methods to permuted null data and breast cancer data from The Cancer Genome Atlas. Contribution plots indicated that principal components-based Canonical Correlation Analysis overfit null data, while Sparse multiple Canonical Correlation Analysis and Multi-Omics Factor Analysis provided stable results with high specificity for both the real and permuted null datasets.\n\nAvailabilityMOVIE is available as an R package at https://github.com/mccabes292/movie\n\nContactmilove@email.unc.edu\n\nSupplementary informationSupplementary data are available at Bioinformatics online.