miRglmm: a generalized linear mixed model of isomiR-level counts improves estimation of miRNA-level differential expression and uncovers variable differential expression between isomiRs - Andrea M. Baran; Arun H. Patil; Ernesto Aparicio-Puerta; Seong-Hwan Jun; Marc K. Halushka; Matthew N. McCall (Genome Biology)
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.