miRglmm：一种基于异构体水平计数的广义线性混合模型，可改善对miRNA水平差异表达的估计，并揭示异构体之间的可变差异表达。

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

作者信息

Baran Andrea M, Patil Arun H, Aparicio-Puerta Ernesto, Halushka Marc K, McCall Matthew N

机构信息

Department of Biostatistics and Computational Biology, University of Rochester Medical Center, 265 Crittenden Blvd., Box 630, Rochester, NY 14642, USA.

Lieber Institute for Brain Development, Johns Hopkins University, 855 North Wolfe St. Suite 300, Baltimore, MD 21205, USA.

出版信息

bioRxiv. 2024 Jul 18:2024.05.03.592274. doi: 10.1101/2024.05.03.592274.

DOI:10.1101/2024.05.03.592274

PMID:39071300

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11275874/

Abstract

MicroRNA-seq data is produced by aligning small RNA sequencing reads of different miRNA transcript isoforms, called isomiRs, to known microRNAs. Aggregation to microRNA-level counts discards information and violates core assumptions of differential expression (DE) methods developed for mRNA-seq data. We establish miRglmm, a DE 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 DE methods in estimating DE for miRNA, whether or not there is significant isomiR variability, and simultaneously provides estimates of isomiR-level DE.

摘要

微小RNA测序数据是通过将不同微小RNA转录本异构体（称为异微小RNA）的小RNA测序读数与已知微小RNA进行比对而产生的。汇总到微小RNA水平的计数会丢弃信息，并违反为mRNA测序数据开发的差异表达（DE）方法的核心假设。我们建立了miRglmm，一种用于微小RNA测序数据的DE方法，它使用异微小RNA水平计数的广义线性混合模型，有助于检测具有差异表达或差异异微小RNA使用情况的微小RNA。我们证明，无论是否存在显著的异微小RNA变异性，miRglmm在估计微小RNA的DE方面都优于当前的DE方法，同时还提供异微小RNA水平DE的估计值。