Header

UZH-Logo

Maintenance Infos

DifferentialRegulation: a Bayesian hierarchical approach to identify differentially regulated genes


Tiberi, Simone; Meili, Joël; Cai, Peiying; Soneson, Charlotte; He, Dongze; Sarkar, Hirak; Avalos-Pacheco, Alejandra; Patro, Rob; Robinson, Mark D (2023). DifferentialRegulation: a Bayesian hierarchical approach to identify differentially regulated genes. bioRxiv 553679, Cold Spring Harbor Laboratory.

Abstract

MOTIVATION: Although transcriptomics data is typically used to analyse mature spliced mRNA, recent attention has focused on jointly investigating spliced and unspliced (or precursor-) mRNA, which can be used to study gene regulation and changes in gene expression production. Nonetheless, most methods for spliced/unspliced inference (such as RNA velocity tools) focus on individual samples, and rarely allow comparisons between groups of samples (e.g., healthy vs . diseased). Furthermore, this kind of inference is challenging, because spliced and unspliced mRNA abundance is characterized by a high degree of quantification uncertainty, due to the prevalence of multi-mapping reads, i.e., reads compatible with multiple transcripts (or genes), and/or with both their spliced and unspliced versions.
RESULTS: Here, we present DifferentialRegulation , a Bayesian hierarchical method to discover changes between experimental conditions with respect to the relative abundance of unspliced mRNA (over the total mRNA). We model the quantification uncertainty via a latent variable approach, where reads are allocated to their gene/transcript of origin, and to the respective splice version. We designed several benchmarks where our approach shows good performance, in terms of sensitivity and error control, versus state-of-the-art competitors. Importantly, our tool is flexible, and works with both bulk and single-cell RNA-sequencing data.
AVAILABILITY AND IMPLEMENTATION: DifferentialRegulation is distributed as a Bioconductor R package.

Abstract

MOTIVATION: Although transcriptomics data is typically used to analyse mature spliced mRNA, recent attention has focused on jointly investigating spliced and unspliced (or precursor-) mRNA, which can be used to study gene regulation and changes in gene expression production. Nonetheless, most methods for spliced/unspliced inference (such as RNA velocity tools) focus on individual samples, and rarely allow comparisons between groups of samples (e.g., healthy vs . diseased). Furthermore, this kind of inference is challenging, because spliced and unspliced mRNA abundance is characterized by a high degree of quantification uncertainty, due to the prevalence of multi-mapping reads, i.e., reads compatible with multiple transcripts (or genes), and/or with both their spliced and unspliced versions.
RESULTS: Here, we present DifferentialRegulation , a Bayesian hierarchical method to discover changes between experimental conditions with respect to the relative abundance of unspliced mRNA (over the total mRNA). We model the quantification uncertainty via a latent variable approach, where reads are allocated to their gene/transcript of origin, and to the respective splice version. We designed several benchmarks where our approach shows good performance, in terms of sensitivity and error control, versus state-of-the-art competitors. Importantly, our tool is flexible, and works with both bulk and single-cell RNA-sequencing data.
AVAILABILITY AND IMPLEMENTATION: DifferentialRegulation is distributed as a Bioconductor R package.

Statistics

Citations

Dimensions.ai Metrics

Altmetrics

Downloads

4 downloads since deposited on 27 Oct 2023
4 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Working Paper
Communities & Collections:07 Faculty of Science > Institute of Molecular Life Sciences
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:17 August 2023
Deposited On:27 Oct 2023 10:25
Last Modified:29 May 2024 12:12
Series Name:bioRxiv
ISSN:2164-7844
OA Status:Green
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1101/2023.08.17.553679
PubMed ID:37645841
Other Identification Number:PubMed ID: 37645841
  • Language: English
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)