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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: Aug 8, 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

SCMcluster: a high-precision cell clustering algorithm integrating marker gene set with single-cell RNA sequencing

Hao Wu^1,2, Haoru Zhou¹, Bing Zhou¹

¹College of Information Engineering, Northwest A&F University, Yangling, 712100, Shaanxi, China.

Briefings in Functional Genomics

|February 27, 2023

Summary

This summary is machine-generated.

SCMcluster enhances single-cell RNA sequencing analysis by using cell markers for improved feature extraction and high-precision clustering. This novel algorithm outperforms existing methods on human and mouse datasets.

Keywords:

ensemble clustering marker genes scRNA-seq single-cell clustering

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Last Updated: Aug 8, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 19, 2024

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Single-cell RNA sequencing (scRNA-seq) is crucial for understanding cellular heterogeneity.
Noise and sparsity in scRNA-seq data present significant challenges for accurate cell clustering.
Existing clustering algorithms struggle with the inherent complexities of scRNA-seq data.

Purpose of the Study:

To develop a high-precision single-cell clustering algorithm for scRNA-seq data analysis.
To improve feature extraction by integrating cellular marker information.
To address the challenges of noise and sparsity in scRNA-seq data.

Main Methods:

Proposed SCMcluster (single-cell cluster using marker genes) algorithm.
Integrated CellMarker and PanglaoDB databases with scRNA-seq data for feature extraction.
Constructed an ensemble clustering model using a consensus matrix.

Main Results:

SCMcluster demonstrated superior feature extraction capabilities.
The algorithm achieved higher clustering performance compared to eight popular methods.
Validated on scRNA-seq datasets from human and mouse tissues.

Conclusions:

SCMcluster offers a robust solution for high-precision single-cell clustering.
Integrating cell marker databases significantly enhances scRNA-seq data analysis.
The developed algorithm advances the field of single-cell data interpretation.