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ccfindR

Cancer Clone Finder


Bioconductor version: Release (3.18)

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>

Citation (from within R, enter citation("ccfindR")):

Installation

To install this package, start R (version "4.3") 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")
ccfindR: single-cell RNA-seq analysis using Bayesian non-negative matrix factorization HTML R Script
Reference Manual PDF
NEWS Text

Details

biocViews Bayesian, Clustering, ImmunoOncology, SingleCell, Software, Transcriptomics
Version 1.22.0
In Bioconductor since BioC 3.7 (R-3.5) (6 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)
System Requirements
URL http://dx.doi.org/10.26508/lsa.201900443
See More
Suggests BiocStyle, knitr, rmarkdown
Linking To Rcpp, RcppEigen
Enhances
Depends On Me
Imports Me
Suggests Me MutationalPatterns
Links To Me
Build Report Build Report

Package Archives

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

Source Package ccfindR_1.22.0.tar.gz
Windows Binary ccfindR_1.22.0.zip
macOS Binary (x86_64)
macOS Binary (arm64)
Source Repository git clone https://git.bioconductor.org/packages/ccfindR
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/ccfindR
Bioc Package Browser https://code.bioconductor.org/browse/ccfindR/
Package Short Url https://bioconductor.org/packages/ccfindR/
Package Downloads Report Download Stats