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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

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Published on: October 19, 2021

Independent component analysis based gene co-expression network inference (ICAnet) to decipher functional modules for

Weixu Wang¹, Huanhuan Tan², Mingwan Sun³

¹State Key Laboratory of Genetic Engineering, Collaborative Innovation Center of Genetics and Development, Human Phenome Institute, Shanghai Engineering Research Center of Industrial Microorganisms, School of Life Sciences and Huashan Hospital, Fudan University, Shanghai, 200438, P.R. China.

Nucleic Acids Research

|February 23, 2021

Summary

Independent Component Analysis-based Gene Co-expression Network Inference (ICAnet) improves single-cell RNA sequencing (scRNA-seq) analysis by discovering rare cell types and integrating batch effects. ICAnet enhances cell clustering and biological interpretation for scRNA-seq data.

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Published on: October 25, 2018

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Single-cell RNA sequencing (scRNA-seq) generates large datasets for biological discovery.
Current gene co-expression network methods struggle with rare cell types and batch effects.
Existing methods often overlook cell-specific co-expression patterns.

Purpose of the Study:

To develop a novel bioinformatics method for scRNA-seq data analysis.
To improve cell clustering and rare cell-type discovery.
To enhance batch integration and biological interpretation of scRNA-seq data.

Main Methods:

Developed Independent Component Analysis-based Gene Co-expression Network Inference (ICAnet).
Decomposed scRNA-seq data into independent gene expression components.
Inferred co-expression modules from these components.

Main Results:

ICAnet demonstrated improved cell clustering and rare cell-type discovery.
The method showed efficient performance for batch integration across diverse scRNA-seq datasets.
ICAnet is robust to variations in library strategy, sequencing depth, and cell number.
Identified potential diagnostic markers for acute myeloid leukemia from scRNA-seq data.

Conclusions:

ICAnet is a robust and effective tool for scRNA-seq data analysis.
The method enhances the discovery of rare cell types and biological insights.
ICAnet offers improved cell clustering and batch correction capabilities.
ICAnet has potential applications in disease diagnostics, such as acute myeloid leukemia.