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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: May 14, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

An overview of computational methods in single-cell transcriptomic cell type annotation.

Tianhao Li¹, Zixuan Wang², Yuhang Liu³

¹School of Computer Science, Chengdu University of Information Technology, No. 24 Block 1, Xuefu Road, 610225 Chengdu, China.

Briefings in Bioinformatics

|May 10, 2025

Summary

This summary is machine-generated.

Single-cell RNA sequencing data aids cell type annotation, advancing biological understanding. This review categorizes methods and highlights deep learning

Keywords:

cell type annotation continual learning dynamic clustering long-tail distribution open-world cell recognition scRNA-seq

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Last Updated: May 14, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

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A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) generates vast transcriptomic data.
Understanding cellular heterogeneity is crucial in biology.
Accurate cell type annotation is essential for interpreting scRNA-seq data.

Purpose of the Study:

To systematically review and categorize transcriptomics-based cell type annotation methods.
To compare existing annotation strategies.
To discuss challenges and future directions, including deep learning applications.

Main Methods:

Review and synthesis of existing literature on cell type annotation methods.
Categorization of methods based on transcriptomics-specific gene expression profiles.
Analysis of challenges such as data imbalance and rare cell types.

Main Results:

Identification and comparison of diverse annotation strategies (marker genes, correlation, supervised learning).
Highlighting the long-tail distribution problem due to rare cell types.
Exploring the potential of deep learning for improved annotation and novel cell type discovery.

Conclusions:

scRNA-seq data offers powerful tools for cell type annotation.
Current methods face challenges, particularly with rare cell types.
Deep learning presents a promising avenue for advancing cell type identification and understanding cellular heterogeneity.