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mcRigor: A Statistical Software Package for Evaluating and Optimizing Metacell Partitioning in Single-Cell Data

Pan Liu1, Jingyi Jessica Li1

  • 1Department of Statistics and Data Science, University of California, Los Angeles, California, USA.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|October 9, 2025
PubMed
Summary
This summary is machine-generated.

Metacell partitioning in single-cell analysis can group diverse cells, affecting results. The mcRigor R package provides a statistical method to evaluate and improve metacell partitioning for more reliable data interpretation.

Keywords:
hyperparameter optimizationmetacell partitioningsingle-cell sequencing

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Metacell partitioning is crucial for single-cell data analysis, aiming to reduce sparsity by grouping similar cells.
  • Current algorithms may group heterogeneous cells, introducing bias in downstream analyses.
  • Metacell results are sensitive to hyperparameter choices, causing user uncertainty.

Purpose of the Study:

  • To introduce mcRigor, an R package for evaluating and optimizing metacell partitioning.
  • To provide a statistical framework for more rigorous metacell-based single-cell data analysis.
  • To enhance the interpretability and trustworthiness of metacell partitions.

Main Methods:

  • Development of the mcRigor R package implementing a novel statistical approach.
  • Utilizing mcRigor to assess the quality and stability of metacell partitions.
  • Demonstrating the application of mcRigor for hyperparameter optimization.

Main Results:

  • mcRigor offers a robust statistical method for metacell evaluation.
  • The package helps identify optimal hyperparameters for stable metacell partitioning.
  • mcRigor enhances the reliability of single-cell data preprocessing.

Conclusions:

  • mcRigor facilitates more rigorous and interpretable metacell-based workflows in single-cell analysis.
  • The package addresses the limitations of existing metacell partitioning methods.
  • Users can confidently apply mcRigor for improved downstream analysis of single-cell data.