RUCova: Removal of Unwanted Covariance in mass cytometry data.

ABSTRACT:

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 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 path

SUBMITTER: Astaburuaga-Garcia R 

PROVIDER: S-EPMC11601163 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature