Institute of Pathology, Charité-Universitätsmedizin Berlin, Berlin, 10117, Germany.
Institute of Biology, Humboldt Universität zu Berlin, Berlin, 10117, Germany.
Bioinformatics. 2024 Nov 1;40(11). doi: 10.1093/bioinformatics/btae669.
High dimensional single-cell mass cytometry data are confounded by unwanted covariance due to variations in cell size and staining efficiency, making analysis, and interpretation challenging.
We present RUCova, a novel method designed to address confounding factors in mass cytometry data. RUCova removes unwanted covariance from measured markers applying multivariate linear regression based on surrogates of sources of unwanted covariance (SUCs) and principal component analysis (PCA). We exemplify the use of RUCova and show that it effectively removes unwanted covariance while preserving genuine biological signals. Our results demonstrate the efficacy of RUCova in elucidating complex data patterns, facilitating the identification of activated signalling pathways, and improving the classification of important cell populations such as apoptotic cells. By providing a robust framework for data normalization and interpretation, RUCova enhances the accuracy and reliability of mass cytometry analyses, contributing to advances in our understanding of cellular biology and disease mechanisms.
The R package is available on https://github.com/molsysbio/RUCova. Detailed documentation, data, and the code required to reproduce the results are available on https://doi.org/10.5281/zenodo.10913464.
由于细胞大小和染色效率的变化等因素，高维单细胞质谱流式细胞术数据存在混杂的协方差，这使得分析和解释变得具有挑战性。
我们提出了 RUCova，这是一种专门用于解决质谱流式细胞术数据中混杂因素的新方法。RUCova 通过基于混杂来源的替代物（SUC）和主成分分析（PCA）的多元线性回归，从测量的标记物中去除不需要的协方差。我们举例说明了 RUCova 的使用，并表明它可以有效地去除不需要的协方差，同时保留真实的生物学信号。我们的结果表明，RUCova 能够有效地阐明复杂的数据模式，有助于识别激活的信号通路，并改善对重要细胞群体（如凋亡细胞）的分类。通过为数据归一化和解释提供一个稳健的框架，RUCova 提高了质谱流式细胞术分析的准确性和可靠性，有助于我们对细胞生物学和疾病机制的理解的进展。
R 包可在 https://github.com/molsysbio/RUCova 上获得。详细的文档、数据以及重现结果所需的代码可在 https://doi.org/10.5281/zenodo.10913464 上获得。
