miRglmm: a generalized linear mixed model of isomiR-level counts improves estimation of miRNA-level differential expression and uncovers variable differential expression between isomiRs.
| Autor: | Baran AM; Department of Biostatistics and Computational Biology, University of Rochester Medical Center, 265 Crittenden Blvd., Box 630, Rochester, NY 14642, USA., Patil AH; Lieber Institute for Brain Development, Johns Hopkins University, 855 North Wolfe St. Suite 300, Baltimore, MD 21205, USA., Aparicio-Puerta E; Department of Biostatistics and Computational Biology, University of Rochester Medical Center, 265 Crittenden Blvd., Box 630, Rochester, NY 14642, USA., Halushka MK; Department of Pathology, Cleveland Clinic, 9500 Euclid Ave., Cleveland, OH 44195, USA., McCall MN; Department of Biostatistics and Computational Biology, University of Rochester Medical Center, 265 Crittenden Blvd., Box 630, Rochester, NY 14642, USA. |
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| Jazyk: | angličtina |
| Zdroj: | BioRxiv : the preprint server for biology [bioRxiv] 2024 Jul 18. Date of Electronic Publication: 2024 Jul 18. |
| DOI: | 10.1101/2024.05.03.592274 |
| Abstrakt: | 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. Competing Interests: Competing interests The authors declare that they have no competing interests. |
| Databáze: | MEDLINE |
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