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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Updated: Jan 1, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

M3S: a comprehensive model selection for multi-modal single-cell RNA sequencing data.

Yu Zhang^1,2, Changlin Wan^2,3, Pengcheng Wang⁴

¹MOE Key Laboratory of Symbolic Computation and Knowledge Engineering, Colleges of Computer Science and Technology, Jilin University, Changchun, 130012, China.

BMC Bioinformatics

|December 22, 2019

Summary

This summary is machine-generated.

This study introduces the Multi-Modal Model Selection (M3S) R package to identify the best statistical model for single-cell RNA sequencing data. M3S accurately captures gene expression multimodality and aids in differential gene expression analysis.

Keywords:

Differential gene expression analysis Drop-seq Left truncated mixture Gaussian Multimodality Single cell RNA-seq

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Last Updated: Jan 1, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

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Published on: September 18, 2021

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Statistical models are crucial for analyzing single-cell RNA sequencing (scRNA-seq) data, including modeling expression profiles, capturing multimodality, and performing differential gene expression tests.
Existing methods lack the capability to select the most appropriate statistical model for scRNA-seq data generated across diverse experimental designs and platforms.

Purpose of the Study:

To develop an R package, Multi-Modal Model Selection (M3S), for selecting the optimal statistical model on a gene-by-gene basis for transcriptomic data.
To enable downstream analyses, such as differential gene expression testing, using the selected model.

Main Methods:

Gene-wise selection of the most parsimonious statistical model from 11 commonly used models to best fit gene expression distributions.
Parameter estimation for the selected statistical model.
Differential gene expression testing integrated with the model selection process.

Main Results:

The M3S package accurately captures multimodality in both simulated and real single-cell RNA sequencing data.
The package facilitates gene-wise model selection and downstream differential gene expression analysis for transcriptomic data.

Conclusions:

M3S provides a robust solution for selecting appropriate statistical models for single-cell and large-scale bulk tissue transcriptomic data.
The open-source M3S package is available on GitHub, promoting accessibility and further development in the field.