Work

conMItion: an R package adjusting confounding factors for associations in multi-omics

Year: 2026

Type: article

Abstract: SUMMARY: Association measurements, such as mutual information (MI), are fundamental in the analysis of cancer multi-omics data for identifying cancer-related genes, gene signatures, and gene regulatory networks, thereby shedding light on tumor development, progression, and treatment. Confounding factors, including tumor purity and mutation burden, can bias association measurements in MI, potentially leading to the misclassification of passenger events as drivers. Conditional mutual information (CMI) provides a robust framework for assessing both linear and nonlinear associations while effectively accounting for different confounding factors. An R package called conMItion is introduced to estimate CMI and its statistical significance for multi-omics data, with the flexibility to adjust for one or two confounding factors. We demonstrated the utilization of conMItion through two use cases. First, we identified interchromosomal somatic copy number alteration-expression associations in bladder cancer. Second, we identified associated cell types within the lung cancer tumor microenvironment using single-cell RNA sequencing datasets. AVAILABILITY AND IMPLEMENTATION: The conMItion package is freely available on CRAN at https://CRAN.R-project.org/package=conMItion. The two use cases described in the paper can be accessed at https://github.com/GJYWang/conMItion.

Source: Bioinformatics

DOI: 10.1093/bioinformatics/btag472

Authors: Gaojianyong Wang, Frank Liu, Ze Chen, Teresa Davoli

Institutions: Max Planck Institute for Molecular Genetics, New York University

Language: English

Topic: Bioinformatics and Genomic Networks

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,989