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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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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

Recent advances in differential expression analysis for single-cell RNA-seq and spatially resolved transcriptomic

Xiya Guo^1,2, Jin Ning^1,2, Yuanze Chen^1,2

¹School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China.

Briefings in Functional Genomics

|April 6, 2023

Summary

Differential expression analysis for single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) presents unique challenges. This review guides the selection of appropriate tools for detecting differentially expressed genes in complex experimental designs.

Keywords:

challenges and opportunities computational methods differential expression analysis single-cell RNA-seq spatially resolved transcriptomics

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Differential expression (DE) analysis is crucial for single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT).
DE analysis in scRNA-seq and SRT data presents unique challenges compared to bulk RNA-seq, complicating the detection of differentially expressed genes.
A comprehensive review for multi-condition, multi-sample DE analysis in scRNA-seq or SRT is currently lacking.

Approach:

This study addresses the challenges in DE detection for scRNA-seq and SRT data.
It highlights opportunities for advancing DE analysis methods.
The work provides guidance for selecting existing DE tools or developing novel computational approaches.

Key Points:

Unique characteristics of scRNA-seq and SRT data pose difficulties for standard DE analysis.
The wide array of available DE tools, each with different assumptions, makes tool selection challenging.
There is a need for a comprehensive review focusing on DE gene detection in complex scRNA-seq and SRT experimental designs.

Conclusions:

This work aims to bridge the gap in understanding and applying DE analysis for scRNA-seq and SRT.
It offers insights into overcoming DE detection challenges and selecting appropriate computational tools.
The review facilitates progress in scRNA-seq and SRT analysis by providing practical guidance.