PT - JOURNAL ARTICLE AU - Pratyaydipta Rudra AU - Ryan Baxter AU - Elena WY Hsieh AU - Debashis Ghosh TI - Compositional Data Analysis using Kernels in Mass Cytometry Data AID - 10.1101/2021.05.08.443265 DP - 2021 Jan 01 TA - bioRxiv PG - 2021.05.08.443265 4099 - http://biorxiv.org/content/early/2021/05/10/2021.05.08.443265.short 4100 - http://biorxiv.org/content/early/2021/05/10/2021.05.08.443265.full AB - Motivation Cell type abundance data arising from mass cytometry experiments are compositional in nature. Classical association tests do not apply to the compositional data due to their non-Euclidean nature. Existing methods for analysis of cell type abundance data suffer from several limitations for high-dimensional mass cytometry data, especially when the sample size is small.Results We proposed a new multivariate statistical learning methodology, Compositional Data Analysis using Kernels (CODAK), based on the kernel distance covariance (KDC) framework to test the association of the cell type compositions with important predictors (categorical or continuous) such as disease status. CODAK scales well for high-dimensional data and provides satisfactory performance for small sample sizes (n < 25). We conducted simulation studies to compare the performance of the method with existing methods of analyzing cell type abundance data from mass cytometry studies. The method is also applied to a high-dimensional dataset containing different subgroups of populations including Systemic Lupus Erythematosus (SLE) patients and healthy control subjects.Availability and Implementation CODAK is implemented using R. The codes and the data used in this manuscript are available on the web at http://github.com/GhoshLab/CODAK/.Supplementary information Supplementary Materials.pdf.Competing Interest StatementThe authors have declared no competing interest.