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

IF 1.6 4区 生物学 Q4 BIOCHEMICAL RESEARCH METHODS
Pan Liu, Jingyi Jessica Li
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引用次数: 0

Abstract

Metacell partitioning is a common preprocessing step in single-cell data analysis, used to reduce sparsity by aggregating similar cells. However, existing metacell partitioning algorithms may inadvertently group heterogeneous cells, potentially biasing downstream analyses. The resulting metacell partitions can vary substantially with different hyperparameter settings, leaving users uncertain about which result to trust. The mcRigor R package offers a statistical method for evaluating and optimizing metacell partitioning in single-cell data analysis. This article provides instructions for installing and using mcRigor to support more rigorous and interpretable metacell-based workflows.

在单细胞数据分析中评估和优化元细胞划分的统计软件包。
元细胞划分是单细胞数据分析中常见的预处理步骤,用于通过聚集相似的细胞来降低稀疏性。然而,现有的元细胞划分算法可能会无意中对异质细胞进行分组,潜在地影响下游分析。所得到的元细胞分区可能因不同的超参数设置而大不相同,这使得用户不确定应该信任哪个结果。mcRigor R包提供了一种在单细胞数据分析中评估和优化元细胞划分的统计方法。本文提供了安装和使用mcgregor的说明,以支持更严格和可解释的基于元胞的工作流。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Computational Biology
Journal of Computational Biology 生物-计算机:跨学科应用
CiteScore
3.60
自引率
5.90%
发文量
113
审稿时长
6-12 weeks
期刊介绍: Journal of Computational Biology is the leading peer-reviewed journal in computational biology and bioinformatics, publishing in-depth statistical, mathematical, and computational analysis of methods, as well as their practical impact. Available only online, this is an essential journal for scientists and students who want to keep abreast of developments in bioinformatics. Journal of Computational Biology coverage includes: -Genomics -Mathematical modeling and simulation -Distributed and parallel biological computing -Designing biological databases -Pattern matching and pattern detection -Linking disparate databases and data -New tools for computational biology -Relational and object-oriented database technology for bioinformatics -Biological expert system design and use -Reasoning by analogy, hypothesis formation, and testing by machine -Management of biological databases
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