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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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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

Hierarchical marker genes selection in scRNA-seq analysis.

Yutong Sun¹, Peng Qiu²

¹School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America.

Plos Computational Biology

|December 12, 2024

Summary

This summary is machine-generated.

A new hierarchical strategy improves marker gene selection for single-cell RNA sequencing (scRNA-seq) data. This method enhances cell type identification accuracy and interpretability by grouping similar cell clusters.

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Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Published on: March 14, 2022

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-seq) enables the analysis of cellular heterogeneity.
Identifying marker genes is crucial for distinguishing cell clusters and annotating cell types.
Current one-vs-all marker gene selection methods struggle with closely related cell types, yielding overlapping gene signatures.

Purpose of the Study:

To develop an improved strategy for marker gene selection in scRNA-seq data.
To address the limitations of the one-vs-all approach in differentiating similar cell clusters.
To enhance the accuracy and interpretability of cell type annotation.

Main Methods:

Proposed a novel hierarchical marker gene selection strategy.
Implemented a method that groups similar cell clusters before marker gene identification.
Selected marker genes in a hierarchical, stepwise manner.

Main Results:

The hierarchical strategy effectively distinguishes between closely related cell types.
Overlapping marker genes, common in the one-vs-all approach, were reduced.
Improved accuracy and interpretability in cell type annotation were achieved.

Conclusions:

Hierarchical marker gene selection offers a superior alternative to the one-vs-all strategy for scRNA-seq data analysis.
This approach enhances the biological meaningfulness of cell type identification.
The proposed method is valuable for researchers analyzing complex cellular populations.