Wang, G.; Liu, F.; Chen, Z.; Davoli, T.

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 non-linear 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 flexibility to adjust for one or two confounding factors. We demonstrated the utilization of conMItion through two use cases. First, we identified co-occurring somatic alterations in bladder cancer genomic data. Second, we applied conMItion to a single-cell RNA sequencing dataset of lung cancer patients and identified positively or negatively associated cell types within the lung cancer tumor microenvironment.