Improved differential expression analysis of miRNA-seq data by modeling competition to be counted
Jun, S.-H.; Halushka, M. K.; McCall, M.
Show abstract
MicroRNAs play a central role in regulating gene expression and modulating diseases. Despite the importance of microRNAs, statistical methods for analyzing them have received far less attention compared to messenger RNAs. Commonly, messenger RNA-seq methods are applied to microRNA-seq data, which may produce erroneous results due to the highly competitive nature of microRNA sequencing. This study critically examines and challenges the assumptions of messenger RNA-seq methods when applied to microRNA-seq data. We propose a Negative Binomial Softmax Regression (NBSR) method to model the unique characteristics of microRNA-seq data. On both simulated and experimental datasets, NBSR outperforms existing methods and offers a new perspective for analyzing microRNA-seq data. NBSR is implemented in Python and freely available as open-source software.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments 96%
- Non-negative Independent Factor Analysis disentangles discrete and continuous sources of variation in scRNA-seq data 96%
- DataRemix: a universal data transformation for optimal inference from gene expression datasets 96%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Bayesian inference for copy number intra-tumoral heterogeneity from single-cell RNA-sequencing data 96%
- Joint Gene Network Construction by Single-Cell RNA Sequencing Data 96%
- An Interpretable Bayesian Clustering Approach with Feature Selection for Analyzing Spatially Resolved Transcriptomics Data 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.