ccfindR

DOI: 10.18129/B9.bioc.ccfindR    

This package is for version 3.15 of Bioconductor; for the stable, up-to-date release version, see ccfindR.

Cancer Clone Finder

Bioconductor version: 3.15

A collection of tools for cancer genomic data clustering analyses, including those for single cell RNA-seq. Cell clustering and feature gene selection analysis employ Bayesian (and maximum likelihood) non-negative matrix factorization (NMF) algorithm. Input data set consists of RNA count matrix, gene, and cell bar code annotations. Analysis outputs are factor matrices for multiple ranks and marginal likelihood values for each rank. The package includes utilities for downstream analyses, including meta-gene identification, visualization, and construction of rank-based trees for clusters.

Author: Jun Woo [aut, cre], Jinhua Wang [aut]

Maintainer: Jun Woo <jwoo at umn.edu>

Installation

To install this package, start R (version "4.2") and enter:

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("ccfindR")

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("ccfindR")

 

HTML	R Script	ccfindR: single-cell RNA-seq analysis using Bayesian non-negative matrix factorization
PDF		Reference Manual
Text		NEWS

Details

biocViews	Bayesian, Clustering, ImmunoOncology, SingleCell, Software, Transcriptomics
Version	1.16.0
In Bioconductor since	BioC 3.7 (R-3.5) (4.5 years)
License	GPL (>= 2)
Depends	R (>= 3.6.0)
Imports	stats, S4Vectors, utils, methods, Matrix, SummarizedExperiment, SingleCellExperiment, Rtsne, graphics, grDevices, gtools, RColorBrewer, ape, Rmpi, irlba, Rcpp, Rdpack (>= 0.7)
LinkingTo	Rcpp, RcppEigen
Suggests	BiocStyle, knitr, rmarkdown
SystemRequirements
Enhances
URL	http://dx.doi.org/10.26508/lsa.201900443
Depends On Me
Imports Me
Suggests Me	MutationalPatterns
Links To Me
Build Report

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package	ccfindR_1.16.0.tar.gz
Windows Binary	ccfindR_1.16.0.zip (64-bit only)
macOS Binary (x86_64)	ccfindR_1.16.0.tgz
Source Repository	git clone https://git.bioconductor.org/packages/ccfindR
Source Repository (Developer Access)	git clone git@git.bioconductor.org:packages/ccfindR
Package Short Url	https://bioconductor.org/packages/ccfindR/
Package Downloads Report	Download Stats