# miRglmm: a generalized linear mixed model of isomiR-level counts improves estimation of miRNA-level differential expression and uncovers variable differential expression between isomiRs

## TL;DR

This paper introduces miRglmm, a new method for analyzing microRNA sequencing data that improves detection of differential expression and isomiR usage.

## Contribution

miRglmm introduces a generalized linear mixed model for isomiR-level counts, improving miRNA differential expression estimation.

## Key findings

- miRglmm outperforms existing methods in estimating miRNA differential expression.
- miRglmm provides estimates of isomiR-level differential expression.
- The method handles differential isomiR usage effectively.

## Abstract

MicroRNA-seq data is produced by aligning small RNA sequencing reads of different microRNA transcript isoforms, called isomiRs, to known microRNAs. Aggregation to microRNA-level counts discards information and violates core assumptions of differential expression methods developed for mRNA-seq data. We establish miRglmm, a differential expression method for microRNA-seq data, that uses a generalized linear mixed model of isomiR-level counts, facilitating detection of miRNA with differential expression or differential isomiR usage. We demonstrate that miRglmm outperforms current differential expression methods in estimating differential expression for miRNA, whether or not there is differential isomiR usage, and simultaneously provides estimates of isomiR-level differential expression.

The online version contains supplementary material available at 10.1186/s13059-025-03549-y.