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Astaburuaga-García, R.* ; Sell, T.* ; Mutlu, S.* ; Sieber, A.* ; Lauber, K. ; Blüthgen, N.*

RUCova: Removal of unwanted covariance in mass cytometry data.

Bioinformatics 40:btae669 (2024)
Verlagsversion DOI PMC
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
MOTIVATION: 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. RESULTS: 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 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. AVAILABILITY AND IMPLEMENTATION: 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. SUPPLEMENTARY INFORMATION: Available at Bioinformatics online (PDF).
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter R Package ; Cell Size ; Covariance ; Mass Cytometry; Morphological-changes; Control Genes; Apoptosis
ISSN (print) / ISBN 1367-4803
Zeitschrift Bioinformatics
Quellenangaben Band: 40, Heft: 11, Seiten: , Artikelnummer: btae669 Supplement: ,
Verlag Oxford University Press
Verlagsort Oxford
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) CCG Personalized Radiotherapy in Head and Neck Cancer (KKG-KRT)
Förderungen Deutsche Forschungsgemeinschaft (DFG)
Bundesministerium fur Bildung und Forschung (BMBF)