Baran Andrea M, Patil Arun H, Aparicio-Puerta Ernesto, Jun Seong-Hwan, Halushka Marc K, McCall Matthew N
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.
Genome Biol. 2025 Apr 22;26(1):102. doi: 10.1186/s13059-025-03549-y.
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.
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，并违反了为mRNA测序数据开发的差异表达方法的核心假设。我们建立了miRglmm，一种用于微小RNA测序数据的差异表达方法，该方法使用异微小RNA水平计数的广义线性混合模型，便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，无论是否存在差异异微小RNA使用情况，miRglmm在估计微小RNA的差异表达方面均优于当前的差异表达方法，同时还提供异微小RNA水平差异表达的估计值。
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