ArticleBioinformatics advances2022
Compositional Data Analysis using Kernels in mass cytometry data.
Article in Bioinformatics advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- Inferring absolute counts from proportions by constraining multivariate normal distributions.NPJ systems biology and applications · 2026Article
- Expansion of extrafollicular B and T cell subsets in childhood-onset systemic lupus erythematosus.Frontiers in immunology · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
motivationCell-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.
resultsWe 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 ( 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/. CONTACT: prudra@okstate.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.