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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: Sep 14, 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

Differential gene expression analysis in single-cell RNA sequencing data.

Tianyu Wang¹, Sheida Nabavi¹

¹Computer Science and Engineering, University of Connecticut, Storrs, USA.

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine

|July 22, 2025

Summary

This summary is machine-generated.

This study introduces SigEMD, a novel method for analyzing single-cell RNA sequencing data to find differentially expressed genes. SigEMD accurately identifies gene expression changes in complex single-cell RNA sequencing (scRNAseq) data.

Keywords:

differential gene expression analysis multimodal data nonparametric models single-cell RNAseq

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Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Single-cell RNA sequencing (scRNAseq) presents unique challenges like multimodality, high zero counts, and sparsity, differentiating it from bulk RNA sequencing.
These characteristics necessitate advanced methods for accurate differential gene expression (DE) analysis.
Identifying DE genes is crucial for understanding cell-type-specific expression changes.

Purpose of the Study:

To develop and evaluate SigEMD, a novel method for precise and efficient differential gene expression analysis in scRNAseq data.
To address the challenges of multimodality and sparsity in scRNAseq data.
To improve the accuracy and reduce false positives in DE gene detection.

Main Methods:

SigEMD integrates a logistic regression model to mitigate the impact of zero counts.
A nonparametric method based on Earth Mover's Distance enhances sensitivity for multimodal data.
Gene interaction network information is utilized to refine DE gene identification and minimize false positives.

Main Results:

The proposed SigEMD method demonstrated powerful performance in detecting differentially expressed genes.
Evaluation using simulated and real data confirmed SigEMD's high precision, sensitivity, and specificity.
SigEMD outperformed existing methods in differential expression analysis for scRNAseq data.

Conclusions:

SigEMD offers a robust and accurate approach for differential gene expression analysis in scRNAseq.
The method effectively handles the complexities of scRNAseq data, including zero counts and multimodality.
SigEMD provides a valuable tool for researchers analyzing single-cell gene expression patterns.