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Updated: Nov 14, 2025

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Published on: November 11, 2014

c-CSN: Single-cell RNA Sequencing Data Analysis by Conditional Cell-specific Network.

Lin Li¹, Hao Dai², Zhaoyuan Fang¹

¹Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China.

Genomics, Proteomics & Bioinformatics

|March 8, 2021

Summary

This summary is machine-generated.

This study introduces a new computational method, conditional cell-specific network (c-CSN), to analyze single-cell RNA sequencing data. c-CSN improves gene association accuracy and helps predict cell differentiation potential.

Keywords:

Cell-specific network Conditional independence Direct association Network flow entropy Single-cell network

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Last Updated: Nov 14, 2025

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Published on: November 11, 2014

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

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Published on: March 12, 2021

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Published on: May 22, 2017

Area of Science:

Computational biology
Genomics
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) reveals cellular heterogeneity but faces challenges with noise and low coverage.
Existing methods like cell-specific network (CSN) can overestimate indirect gene associations.

Purpose of the Study:

To develop a novel computational method, conditional cell-specific network (c-CSN), to address limitations in scRNA-seq data analysis.
To improve the accuracy of gene association quantification in single cells.
To introduce a new metric, network flow entropy (NFE), for estimating cell differentiation potency.

Main Methods:

Proposed the c-CSN method to construct conditional cell-specific networks (CCSNS) by eliminating indirect gene associations.
Developed network flow entropy (NFE) based on CCSN to quantify single-cell differentiation potency.
Applied c-CSN to scRNA-seq datasets for network construction, cell clustering, and dimension reduction.

Main Results:

c-CSN generates direct association networks for individual cells, overcoming CSN's overestimation issues.
Transformed degree matrices from c-CSN are compatible with existing scRNA-seq analysis tools.
CCSN-based NFE effectively resolves differentiation trajectories by quantifying cell potency.

Conclusions:

c-CSN offers a more reliable way to infer gene-gene associations from noisy scRNA-seq data.
The method enhances downstream analyses like cell clustering and trajectory inference.
CCSN-based NFE provides a powerful tool for understanding cell differentiation dynamics.