# iModEst: disentangling -omic impacts on gene expression variation across genes and tissues

## TL;DR

This paper introduces iModEst, a tool that identifies which regulatory factors most influence gene expression in different tissues and genes using cancer data.

## Contribution

iModEst provides a comprehensive atlas of regulator-gene-tissue relationships and their predictive power in gene expression.

## Key findings

- iModEst models explain up to 70% of gene expression variance in 43% of genes across tumor and tumor-adjacent tissues.
- Transcription factor expression is the best predictor of gene expression in both tumor and tumor-adjacent tissues.
- Methylation models in tumor tissues do not predict gene expression well in tumor-adjacent tissues.

## Abstract

Many regulatory factors impact the expression of individual genes including, but not limited, to microRNA, long non-coding RNA (lncRNA), transcription factors (TFs), cis-methylation, copy number variation (CNV), and single-nucleotide polymorphisms (SNPs). While each mechanism can influence gene expression substantially, the relative importance of each mechanism at the level of individual genes and tissues is poorly understood. Here, we present the integrative Models of Estimated gene expression (iModEst), which details the relative contribution of different regulators to the gene expression of 16,000 genes and 21 tissues within The Cancer Genome Atlas (TCGA). Specifically, we derive predictive models of gene expression using tumour data and test their predictive accuracy in cancerous and tumour-adjacent tissues. Our models can explain up to 70% of the variance in gene expression across 43% of the genes within both tumour and tumour-adjacent tissues. We confirm that TF expression best predicts gene expression in both tumour and tumour-adjacent tissue whereas methylation predictive models in tumour tissues does not transfer well to tumour adjacent tissues. We find new patterns and recapitulate previously reported relationships between regulator and gene-expression, such as CNV-predicted FGFR2 expression and SNP-predicted TP63 expression. Together, iModEst offers an interactive, comprehensive atlas of individual regulator–gene–tissue expression relationships as well as relationships between regulators.

Graphical Abstract