DOI: 10.18129/B9.bioc.cytoKernel  

This package is for version 3.16 of Bioconductor; for the stable, up-to-date release version, see cytoKernel.

Differential expression using kernel-based score test

Bioconductor version: 3.16

cytoKernel implements a kernel-based score test to identify differentially expressed features in high-dimensional biological experiments. This approach can be applied across many different high-dimensional biological data including gene expression data and dimensionally reduced cytometry-based marker expression data. In this R package, we implement functions that compute the feature-wise p values and their corresponding adjusted p values. Additionally, it also computes the feature-wise shrunk effect sizes and their corresponding shrunken effect size. Further, it calculates the percent of differentially expressed features and plots user-friendly heatmap of the top differentially expressed features on the rows and samples on the columns.

Author: Tusharkanti Ghosh [aut, cre], Victor Lui [aut], Pratyaydipta Rudra [aut], Souvik Seal [aut], Thao Vu [aut], Elena Hsieh [aut], Debashis Ghosh [aut, cph]

Maintainer: Tusharkanti Ghosh <tusharkantighosh30 at>

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biocViews Clustering, DifferentialExpression, FlowCytometry, GeneExpression, ImmunoOncology, OneChannel, Proteomics, SingleCell, Software
Version 1.4.0
In Bioconductor since BioC 3.14 (R-4.1) (1.5 years)
License GPL-3
Depends R (>= 4.1)
Imports Rcpp, SummarizedExperiment, utils, methods, ComplexHeatmap, circlize, ashr, data.table, BiocParallel, dplyr, stats, magrittr, rlang, S4Vectors
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Suggests knitr, rmarkdown, BiocStyle, testthat
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