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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 13, 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

Dimension reduction, cell clustering, and cell-cell communication inference for single-cell transcriptomics with

Qian Ding¹, Wenyi Yang¹, Guangfu Xue¹

¹Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150000, China.

|September 9, 2024

Summary

DcjComm enhances single-cell transcriptomics analysis by improving dimension reduction, cell clustering, and cell-cell communication inference. This versatile method offers superior performance for exploring complex biological processes.

Keywords:

Cell clustering Cell–cell communication Joint learning Non-negative matrix factorization Single-cell

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Last Updated: Jun 13, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Published on: July 18, 2019

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Single-cell transcriptomics offers deep insights into biological complexity.
Current computational methods require enhancement in dimension reduction, clustering, and cell-cell communication inference.

Purpose of the Study:

To introduce DcjComm, a versatile computational method for comprehensive single-cell transcriptomics analysis.
To improve the analysis of gene expression patterns, cellular identities, and intercellular communication.

Main Methods:

Utilizes non-negative matrix factorization-based joint learning for dimension reduction and clustering.
Integrates ligand-receptor pairs, transcription factors, and target genes for cell-cell communication inference.
Detects functional modules to explore gene expression patterns.

Main Results:

DcjComm demonstrates superior performance over existing state-of-the-art methods.
Successfully performs dimension reduction, cell clustering, and cell-cell communication inference.
Identifies functional modules and cellular identities within complex datasets.

Conclusions:

DcjComm provides a robust and versatile platform for single-cell transcriptomics data analysis.
The method offers significant improvements in key analytical areas, advancing biological discovery.
Facilitates a more comprehensive understanding of cellular functions and interactions.