Sparse multiple co-Inertia analysis with application to integrative analysis of multi -Omics data

Abstract

Background: Multiple co-inertia analysis (mCIA) is a multivariate analysis method that can assess relationships and trends in multiple datasets. Recently it has been used for integrative analysis of multiple high-dimensional -omics datasets. However, its estimated loading vectors are non-sparse, which presents challenges for identifying important features and interpreting analysis results. We propose two new mCIA methods: 1) a sparse mCIA method that produces sparse loading estimates and 2) a structured sparse mCIA method that further enables incorporation of structural information among variables such as those from functional genomics. Results: Our extensive simulation studies demonstrate the superior performance of the sparse mCIA and structured sparse mCIA methods compared to the existing mCIA in terms of feature selection and estimation accuracy. Application to the integrative analysis of transcriptomics data and proteomics data from a cancer study identified biomarkers that are suggested in the literature related with cancer disease. Conclusion: Proposed sparse mCIA achieves simultaneous model estimation and feature selection and yields analysis results that are more interpretable than the existing mCIA. Furthermore, proposed structured sparse mCIA can effectively incorporate prior network information among genes, resulting in improved feature selection and enhanced interpretability.

Cox-sMBPLS: An Algorithm for Disease Survival Prediction and Multi-Omics Module Discovery Incorporating Cis-Regulatory Quantitative Effects

“…The settings under consideration aim to control the redundancy within the Omics profiles (via a soft-threshold), the association between the Omics profiles (via cis -regulatory quantitative effects), and the relevance of each Omics profile or a combination of them to explain the survival probabilities. We compared the proposed model to elastic-net Cox-PH (El-net Cox) ( Simon et al, 2011 ), random survival forest (RSF) ( Ishwaran et al, 2008 ), Block Forest ( Hornung and Wright, 2019 ), and multiple co-inertia analysis (MCIA) ( Min and Long, 2020 ). All models are trained on 80% of the samples and tested on the left-out 20% portion.…”

“…We also compared the prediction performance of the Cox-sMBPLS model to El-net Cox ( Simon et al, 2011 ), RSF ( Ishwaran et al, 2008 ), Block Forest ( Hornung and Wright, 2019 ) and MCIA ( Min and Long, 2020 ) (see Supplementary Table 7 for prediction performance measures). All models are trained on 80% of the samples and tested on the left-out 20% portion.…”

Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review

“…MCIA (Multiple Co-Inertia Analysis) ( Meng et al, 2014 ) is the extension of the CIA for the analysis of more than two data tables. sMCIA (sparse Multiple Co-Inertia Analysis) ( Min and Long, 2020 ) is a sparse version of MCIA via imposing a sparsity constraint on the transformed direction vectors.…”

Nitrogen use efficiency, rhizosphere bacterial community, and root metabolome reprogramming due to maize seed treatment with microbial biostimulants

“…However, the recent development of new tools, including the integration of multiple omics datasets, has paved the way for a deeper understanding of plant‐microbe interactions. Multivariate approaches such as Multiple Co‐inertia Analysis (MCIA) have been proposed to identify co‐relationships between multiple high dimensional datasets, based on a covariance optimization criterion (Min & Long, 2020). Nevertheless, despite the advances in this field, this approach remained limited to human and food studies (Afshari et al, 2020; Meng et al, 2014).…”