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Library size-stabilized metacells construction enhances co-expression network analysis in single-cell data.

Tianjiao Zhang1, Haibin Zhu2

  • 1School of Pharmacy and Food Engineering, Wuyi University, Jiangmen, China.

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|November 13, 2025
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Summary

LSMetacell stabilizes library sizes to reduce noise in single-cell RNA sequencing data. This computational framework improves co-expression network analysis, revealing robust biological insights in complex diseases like Alzheimer's.

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Single-cell RNA sequencing (scRNA-seq) is crucial for understanding cell type-specific gene expression and co-expression networks.
  • Existing metacell construction methods aggregate cells but are prone to compositional biases and library size variance, leading to inaccurate co-expression correlations.
  • These limitations obscure true biological interactions and hinder the study of complex biological systems.

Purpose of the Study:

  • To develop a computational framework, LSMetacell (Library Size-stabilized Metacells), that mitigates compositional biases in scRNA-seq data.
  • To enhance the accuracy of co-expression network inference by stabilizing library sizes during metacell aggregation.
  • To improve downstream analyses, such as Weighted Gene Co-expression Network Analysis (WGCNA), for more reliable biological discoveries.

Main Methods:

  • Mathematical modeling and simulations were used to demonstrate the impact of library size variance on co-expression networks.
  • LSMetacell was developed as a computational framework to explicitly stabilize library sizes across metacells.
  • The framework was applied to a postmortem Alzheimer's disease brain scRNA-seq dataset for validation.

Main Results:

  • Mathematical modeling confirmed that uncontrolled library size variance inflates false-positive correlations and distorts co-expression networks.
  • LSMetacell effectively stabilized library sizes, reducing compositional noise while preserving cellular heterogeneity.
  • Application to Alzheimer's disease data revealed robust, cell type-specific co-expression modules enriched for disease-relevant pathways, outperforming conventional metacells.

Conclusions:

  • LSMetacell provides a principled strategy for resolving compositional biases in scRNA-seq data, enhancing co-expression network reliability.
  • The framework offers a generalizable solution for improving transcriptional analyses in single-cell studies.
  • This approach advances the accurate inference of gene interactions in complex biological systems and disease research.