NEW BIOINFORMATICS ADVANCES Compositional Data Analysis using Kernels in Mass Cytometry Data Pratyaydipta Rudra , Ryan Baxter , Elena W Y Hsieh , Debashis Ghosh Bioinformatics Advances , vbac003, https://doi.org/10.1093/bioadv/vbac003 Abstract 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-dime...
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