Integrating multi-OMICS data through sparse Canonical Correlation Analysis for predicting complex traits: A comparative study

Abstract

Motivation: Recent developments in technology have enabled researchers to collect multiple OMICS datasets for the same individuals. The conventional approach for understanding the relationships between the collected datasets and the complex trait of interest would be through the analysis of each OMIC dataset separately from the rest, or to test for associations between the OMICS datasets. In this work we show that by integrating multiple OMICS datasets together, instead of analysing them separately, improves our understanding of their in-between relationships as well as the predictive accuracy for the tested trait. As OMICS datasets are heterogeneous and high-dimensional (p >> n) integrating them can be done through Sparse Canonical Correlation Analysis (sCCA) that penalises the canonical variables for producing sparse latent variables while achieving maximal correlation between the datasets. Over the last years, a number of approaches for implementing sCCA have been proposed, where they differ on their objective functions, iterative algorithm for obtaining the sparse latent variables and make different assumptions about the original datasets. Results: Through a comparative study we have explored the performance of the conventional CCA proposed by Parkhomenko et al. [2009], penalised matrix decomposition CCA proposed by Witten and Tibshirani [2009] and its extension proposed by Suo et al. [2017]. The aferomentioned methods were modified to allow for different penalty functions. Although sCCA is an unsupervised learning approach for understanding of the in-between relationships, we have twisted the problem as a supervised learning one and investigated how the computed latent variables can be used for predicting complex traits. The approaches were extended to allow for multiple (more than two) datasets where the trait was included as one of the input datasets. Both ways have shown improvement over conventional predictive models that include one or multiple datasets. Contact: tr1915@ic.ac.uk

Modelling capture efficiency of single-cell RNA-sequencing data improves inference of transcriptome-wide burst kinetics

“…The data are quickly becoming available and could thus inform our understanding of global gene expression variability ( Lee et al 2020 , Argelaguet et al 2021 ). Some research is already starting in this important area based on both statistical data integration ( Argelaguet et al 2021 , Rautenstrauch et al 2022 , Rodosthenous et al 2021 ) and model-based inference ( La Manno et al 2018 , Bergen et al 2020 , Gorin and Pachter 2022 ). Ultimately, by harnessing gene-gene correlations, such multi-omic single-cell datasets could be used to infer genetic networks ( Stumpf 2021 , Qiu et al 2022 ).…”