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Functional Evaluation of Biological Neurotoxins in Networked Cultures of Stem Cell-derived Central Nervous System Neurons

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SPIDE: A single cell potency inference method based on the local cell-specific network entropy.

Ruiqing Zheng¹, Ziwei Xu¹, Yanping Zeng¹

¹School of Computer Science and Engineering, Central South University, Changsha 410083, China.

Methods (San Diego, Calif.)

|November 12, 2023

Summary

This summary is machine-generated.

We developed SPIDE, a new method using cell-specific network entropy to accurately measure cell differentiation potency from single-cell RNA sequencing data. SPIDE improves understanding of cell development and disease progression.

Keywords:

Cell differential potency Cell-specific Network Network entropy scRNA-seq data

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Area of Science:

Computational Biology
Genomics
Systems Biology

Background:

Accurately quantifying cell differentiation potency is crucial for understanding development and disease.
Existing methods using gene entropy or protein-protein interaction (PPI) networks struggle with noise and inaccuracies in single-cell RNA sequencing (scRNA-seq) data.

Purpose of the Study:

To introduce SPIDE, a novel method for inferring cell potency using cell-specific network entropy.
To address limitations of current methods by incorporating cell heterogeneity and gene expression with network structure.

Main Methods:

SPIDE utilizes a local weighted cell-specific network for each cell to capture heterogeneity.
Entropy is calculated by integrating gene expression levels with the constructed network topology.
Three cell entropy estimation models were compared across eight scRNA-seq datasets.

Main Results:

SPIDE demonstrated consistent results with actual cell differentiation potency across most datasets.
The method accurately captured continuous changes in potency during differentiation processes.
SPIDE showed a significant correlation with stemness in colorectal cancer tumor cells.

Conclusions:

SPIDE offers a universal and accurate framework for cell entropy estimation.
This approach enhances the understanding of cell differentiation, disease development, and related biological research.