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

Authors

Gaojianyong Wang, Frank Liu, Ze Chen, Teres Davoli

Published in

Bioinformatics (Oxford, England). Jun 30, 2026. Epub Jun 30, 2026.

Abstract

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.
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. A supplementary document is available online.

PMID:
42378451
Bibliographic data and abstract were imported from PubMed on 01 Jul 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.