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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: Jun 27, 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

CASCC: a co-expression-assisted single-cell RNA-seq data clustering method.

Lingyi Cai^1,2, Dimitris Anastassiou^1,2,3

¹Department of Systems Biology, Columbia University, New York, NY 10032, United States.

Bioinformatics (Oxford, England)

|April 25, 2024

Summary

This summary is machine-generated.

New co-expression-assisted single-cell clustering (CASCC) improves cell population analysis. This method enhances biological accuracy in single-cell transcriptomics, aiding discovery of underlying biological mechanisms.

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Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Existing single-cell RNA sequencing clustering methods struggle with heterogeneous and transitioning cell populations.
Dominant cell populations can be identified by gene co-expression signatures, independent of partitioning methods.

Purpose of the Study:

To introduce a novel clustering method, CASCC (co-expression-assisted single-cell clustering), to enhance biological accuracy in single-cell transcriptomics.
To leverage gene co-expression features identified via an unsupervised adaptive attractor algorithm for improved clustering.

Main Methods:

Development of the CASCC algorithm integrating gene co-expression features.
Application of CASCC to single-cell RNA sequencing data.
Evaluation of CASCC performance against existing clustering methods using multiple metrics.

Main Results:

CASCC demonstrated superior performance compared to other clustering methods.
The method effectively handles challenges posed by non-homogeneous and transitioning cell populations.
Gene co-expression features significantly improved clustering accuracy.

Conclusions:

CASCC offers improved biological accuracy for single-cell transcriptomics analysis.
The method has the potential to facilitate new discoveries regarding cellular mechanisms.
CASCC is available as an R package for public use.