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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 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

Inference of single-cell network using mutual information for scRNA-seq data analysis.

Lan-Yun Chang¹, Ting-Yi Hao¹, Wei-Jie Wang¹

¹Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan.

BMC Bioinformatics

|September 5, 2024

Summary

We developed SINUM, a novel method for constructing single-cell networks (SCN) using mutual information from scRNA-seq data. SINUM accurately identifies cell types and reveals gene associations, outperforming existing methods.

Keywords:

Gene expression Mutual information Network inference Single-cell analysis Single-cell clustering Single-cell network

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Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing

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Published on: March 23, 2022

Area of Science:

Genomics
Systems Biology
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) enables biological system analysis at single-cell resolution.
Network modeling of gene associations can reveal dynamic biological changes.
Accurate single-cell network (SCN) construction for cell heterogeneity exploration remains challenging.

Purpose of the Study:

To introduce SINUM, a method for constructing SCNs using mutual information.
To evaluate SINUM's accuracy and robustness in cell type identification.
To assess the biological relevance of SINUM-derived SCNs.

Main Methods:

SINUM estimates mutual information between gene pairs in scRNA-seq data to infer dependencies within individual cells.
The method was validated on diverse scRNA-seq datasets using multiple performance metrics.
SINUM-generated SCNs were compared against the human interactome and analyzed for scale-free properties.

Main Results:

SINUM demonstrated superior accuracy and robustness in cell type identification compared to state-of-the-art methods.
SINUM-derived SCNs showed significant overlap with the human interactome.
The constructed SCNs exhibited scale-free network properties.

Conclusions:

SINUM provides a network-level view of biological systems, aiding in the detection of cell-type marker genes and gene pairs.
The method facilitates the investigation of time-dependent gene association changes, exemplified during embryo development.
SINUM's open-source code is available for broader research application.