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In single-cell sequencing data analysis, addressing sparsity often involves aggregating the profiles of homogeneous single cells into metacells. However, existing metacell partitioning methods lack checks on the homogeneity assumption and may aggregate heterogeneous single cells, potentially biasing downstream analysis and leading to spurious discoveries. To fill this gap, we introduce mcRigor, a statistical method to detect dubious metacells composed of heterogeneous cells and optimize the choice of metacell partitioning methods and hyperparameters.

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mcRigor: A Statistical Method to Enhance the Rigor of Metacell Partitioning in Single-Cell RNA-seq and ATAC-seq Data Analysis

  • 会议论文

  • 发表日期：

    2025年

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  • Pan Liu,
  • Jingyi Jessica Li

摘要

In single-cell sequencing data analysis, addressing sparsity often involves aggregating the profiles of homogeneous single cells into metacells. However, existing metacell partitioning methods lack checks on the homogeneity assumption and may aggregate heterogeneous single cells, potentially biasing downstream analysis and leading to spurious discoveries. To fill this gap, we introduce mcRigor, a statistical method to detect dubious metacells composed of heterogeneous cells and optimize the choice of metacell partitioning methods and hyperparameters.