cytoKernel
This is the development version of cytoKernel; for the stable release version, see cytoKernel.
Differential expression using kernel-based score test
Bioconductor version: Development (3.24)
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 gmail.com>
citation("cytoKernel")):
Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, Bravo HC, Davis S, Gatto L, Girke T, Gottardo R, Hahne F, Hansen KD, Irizarry RA, Lawrence M, Love MI, MacDonald J, Obenchain V, Oleś AK, Pagès H, Reyes A, Shannon P, Smyth GK, Tenenbaum D, Waldron L, Morgan M (2015). "Orchestrating high-throughput genomic analysis with Bioconductor." Nature Methods, 12(2), 115–121. doi:10.1038/nmeth.3252.
Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JYH, Zhang J (2004). "Bioconductor: open software development for computational biology and bioinformatics." Genome Biology, 5(10), R80. doi:10.1186/gb-2004-5-10-r80.
Installation
To install this package, start R (version "4.6") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
## The following initializes the development version of Bioconductor
BiocManager::install(version = "devel")
BiocManager::install("cytoKernel")
For older versions of R, please refer to the appropriate Bioconductor release.
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("cytoKernel")| The CytoK user's guide | HTML | R Script |
| Reference Manual | ||
| NEWS | Text |
Details
| biocViews | Clustering, DifferentialExpression, FlowCytometry, GeneExpression, ImmunoOncology, OneChannel, Proteomics, SingleCell, Software |
| Version | 1.19.0 |
| In Bioconductor since | BioC 3.14 (R-4.1) (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 |
| System Requirements | |
| URL | |
| Bug Reports | https://github.com/Ghoshlab/cytoKernel/issues |
See More
| Suggests | knitr, rmarkdown, BiocStyle, testthat |
| Linking To | Rcpp |
| Enhances | |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Links To Me | |
| Build Report | Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Source Package | cytoKernel_1.19.0.tar.gz |
| Windows Binary (x86_64) | cytoKernel_1.19.0.zip |
| macOS Binary (big-sur-x86_64) | cytoKernel_1.19.0.tgz |
| macOS Binary (sonoma-arm64) | cytoKernel_1.19.0.tgz |
| Source Repository | git clone https://git.bioconductor.org/packages/cytoKernel |
| Source Repository (Developer Access) | git clone git@git.bioconductor.org:packages/cytoKernel |
| Bioc Package Browser | https://code.bioconductor.org/browse/cytoKernel/ |
| Package Short Url | https://bioconductor.org/packages/cytoKernel/ |
| Package Downloads Report | Download Stats |