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NBBt-test: a versatile method for differential analysis of multiple types of RNA-seq data.
Yuan-De Tan1, Chittibabu Guda2,3
1Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE, 68198, USA.
Scientific Reports
|July 27, 2022
Summary
A new statistical method, the negative beta binomial t-test (NBBt-test), accurately analyzes various transcriptomic data types, including gene expression and alternative splicing. This tool improves efficiency and reduces errors for robust biological discoveries.
Area of Science:
- Transcriptomics
- Bioinformatics
- Computational Biology
Background:
- Transcriptome sequencing technologies have advanced, enabling new analyses like alternative splicing and gene expression.
- Existing statistical methods are often data-type specific and perform poorly across different transcriptomic datasets.
- Comprehensive characterization of cellular transcriptional landscapes has significant basic science and clinical potential.
Purpose of the Study:
- To develop a versatile statistical method for analyzing diverse transcriptomic data.
- To address the limitations of existing methods in handling various transcriptomic analyses.
- To introduce the negative beta binomial t-test (NBBt-test) for unified differential analyses.
Main Methods:
- Development of the negative beta binomial t-test (NBBt-test).
- Implementation of NBBt-test functions for differential gene expression, alternative splicing, alternative polyadenylation, and CRISPR knockout screening.
- Validation using real and simulated transcriptomic datasets of varying sample sizes.
Main Results:
- NBBt-test demonstrated superior performance compared to existing methods.
- The method showed higher efficiency and lower Type I error rates and False Discovery Rates (FDR).
- NBBt-test effectively identified differential isoforms, gene expression, and CRISPR screening genes.
Conclusions:
- NBBt-test offers a robust and efficient solution for analyzing multiple types of transcriptomic data.
- The method provides improved accuracy in identifying differential features across various datasets and sample sizes.
- An R-package for NBBt-test is available, facilitating its application in biological research.
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