Cell Features Reconstruction from Gene Association Network of Single Cell

Area of Science:

• Computational Biology
• Genomics
• Bioinformatics

Background:

• Gene expression is a key but unstable feature in single-cell analyses.
• Cell-specific networks (CSNs) offer insights but are information-rich and lack interaction quantification.
• Existing methods struggle to measure the precise interaction levels between genes within single cells.

Purpose of the Study:

• To develop a novel two-level approach for reconstructing single-cell features.
• To transform gene expression data into gene ontology and gene interaction features.
• To quantitatively evaluate single-cell differentiation levels using gene gravitation entropy.

Main Methods:

• Fusing global gene position and neighborhood influence to create a cell network feature matrix (CNFM) from CSNs.
• Developing a gene gravitation computational method based on CNFM to quantify gene-gene interactions.
• Designing a gene gravitation entropy index to assess single-cell differentiation.

Main Results:

• Successfully reconstructed single-cell features by transforming gene expression into gene ontology and interaction features.
• Quantified gene-gene interaction levels and constructed gene gravitation networks for single cells.
• Demonstrated the effectiveness of gene gravitation entropy in evaluating single-cell differentiation across eight scRNA-seq datasets.

Conclusions:

• The proposed two-level approach effectively reconstructs single-cell features.
• Gene gravitation and its entropy provide a novel way to measure gene interactions and cell differentiation.
• The method shows broad applicability and effectiveness in analyzing scRNA-seq data.