RUCova

RUCova removes unwanted covariance from high-dimensional mass cytometry data using multivariate linear regression on Surrogates of Unwanted Covariance (SUCs) and Principal Component Analysis (PCA) to preserve genuine biological signals.

Key Features:

• Unwanted Covariance Removal: Implements multivariate linear regression on Surrogates of Unwanted Covariance (SUCs) to identify and remove technical variation.
• Principal Component Analysis Integration: Uses PCA as part of the framework to assist in removing unwanted covariance.
• Technical Noise Modeling: Systematically models and corrects for technical noise arising from differences in cell size, staining efficiency, and instrument-specific artifacts.
• Biological Signal Preservation: Removes unwanted covariance while preserving genuine biological signals in mass cytometry data.

Scientific Applications:

• Signalling Pathway Analysis: Facilitates the identification of activated signalling pathways by removing confounding technical variation.
• Cell Population Classification: Improves the classification of important cell populations in mass cytometry datasets.
• Cellular Heterogeneity Studies: Enhances the quality and interpretability of mass cytometry data for studying cellular heterogeneity and function.
• Disease Mechanism Research: Contributes to advancements in understanding cellular biology and disease mechanisms through improved data normalization.

Methodology:

RUCova uses multivariate linear regression on Surrogates of Unwanted Covariance (SUCs) combined with Principal Component Analysis (PCA) to remove unwanted covariance from mass cytometry data.