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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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Related Experiment Video

Updated: Nov 15, 2025

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

Modeling expression ranks for noise-tolerant differential expression analysis of scRNA-seq data.

Krishan Gupta¹, Manan Lalit², Aditya Biswas³

¹Department of Computer Science and Engineering, Indraprastha Institute of Information Technology, Delhi 110020, India.

Genome Research

|March 6, 2021

Summary

This study introduces ROSeq, a novel method for analyzing single-cell transcriptomics data by modeling gene expression ranks. ROSeq offers a robust and scalable approach for identifying differential gene expression in complex biological systems.

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Last Updated: Nov 15, 2025

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

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Published on: June 8, 2020

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell transcriptomics provides high-resolution insights into cellular heterogeneity.
Accurate modeling of gene expression is crucial for identifying tissue-specific patterns.
Existing methods often struggle with high dropout rates common in single-cell data.

Purpose of the Study:

To explore modeling gene expression ranks as an alternative to expression estimates.
To develop and evaluate a novel differential expression test for single-cell data.
To assess the performance of the discrete generalized beta distribution (DGBD) for this purpose.

Main Methods:

Utilized the discrete generalized beta distribution (DGBD) to model gene expression ranks.
Devised a Wald-type test for comparing gene expression between two single-cell groups.
Developed the ROSeq R package for method dissemination.

Main Results:

The proposed ROSeq method demonstrated a good balance between Type I and Type II errors.
ROSeq showed exceptional robustness to expression noise.
The method exhibited rapid scalability with increasing sample sizes.

Conclusions:

ROSeq provides a robust and scalable solution for differential gene expression analysis in single-cell transcriptomics.
Modeling gene expression ranks offers advantages over traditional expression-based approaches.
The ROSeq R package is available on Bioconductor for broader adoption.