Pattern-centric transformation of omics-data sources grounded on multi-wise gene associations aids predictive tasks in TCGA while ensuring interpretability.

Abstract

MotivationThe increasing prevalence of omics data sources is pushing the study of regulatory mechanisms underlying complex diseases such as cancer. However, the vast quantities of features produced and the inherent interplay between them lead to a level of complexity that hampers both descriptive and predictive tasks, requiring custom-built algorithms that can extract relevant information from these sources of data. ResultsWe propose a transformation that moves data centered on molecules (e.g. transcripts and proteins) to a new data space focused on putative regulatory modules given by statistically relevant patterns of coexpression. The proposed transformation extracts patterns from the data through biclustering and uses them to create new variables with guarantees of interpretability and discriminative power. The transformation is shown to achieve dimensionality reductions of up to 99% and to increase the predictive performance of various classifiers across multiple omics layers. Our results suggest that a transformation of omics data from gene-centric to pattern-centric data provides benefits to both prediction tasks and human interpretation. The proposed approach is expected to greatly support further bioinformatic analyses for precision medicine applications. AvailabilitySoftware code and the raw results generated are available at github.com/Andrempp/Pattern-Centric-Transformation. Contactandremppatricio@tecnico.ulisboa.pt Supplementary informationSupplementary data are available at Journal Name online.