# bayesReact: expression-coupled regulatory motif analysis detects microRNA activity across cancers, tissues, and at the single-cell level

## TL;DR

bayesReact is a new method that detects microRNA activity in bulk and single-cell data, revealing regulatory patterns in cancers, tissues, and during development.

## Contribution

bayesReact introduces an unsupervised generative model to infer microRNA activity from gene expression data, improving accuracy in sparse and single-cell datasets.

## Key findings

- bayesReact outperforms existing methods in inferring microRNA activity from sparse bulk and single-cell data.
- Inferred miRNA activities correlate strongly with target genes and reveal cancer-type-specific patterns.
- bayesReact identifies key miRNAs during murine stem cell differentiation and embryonic spinal cord development.

## Abstract

Gene regulatory mechanisms control cell differentiation and homeostasis but are often undetectable, particularly at the single-cell level. We introduce bayesReact, which quantifies regulatory activities from bulk or single-cell omics data. It is based on an unsupervised generative model, exploiting the fact that each regulator typically targets many genes sharing a sequence motif. Using mRNA expression data, we illustrate and evaluate bayesReact on microRNAs (miRNAs). It outperforms existing methods on sparse bulk data and improves activity inference on single-cell data. Inferred miRNA activities correlate with miRNA expression across pan-cancer TCGA and healthy GTEx tissue samples. The activities capture cancer-type-specific miRNA patterns, e.g., for miR-122-5p and miR-124-3p, which also correlate more strongly with their target genes than their measured expression. This includes a strong negative correlation between miR-124-3p and the anti-neuronal REST transcription factor in nervous system cancers. Analyzing single-cell data, bayesReact detects prominent miRNAs during murine stem cell differentiation, including miR-298-5p, miR-92-2-5p, and the Sfmbt2 cluster (miR-297-669). Furthermore, spatio-temporal inference shows increasing miR-124-3p activity in differentiating neurons during embryonic spinal cord development in mice. bayesReact enables large-scale hypothesis-generating screens for novel regulatory factors and the discovery of condition-specific activities. It is implemented as a user-friendly R package (https://github.com/JakobSkouPedersenLab/bayesReact).

Graphical Abstract