Package: ccfindR 1.33.0

Jun Woo

ccfindR: Cancer Clone Finder

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.

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

ccfindR_1.33.0.tar.gz
ccfindR_1.33.0.zip(r-4.7)ccfindR_1.33.0.zip(r-4.6)ccfindR_1.33.0.zip(r-4.5)

ccfindR_1.33.0.tar.gz(r-4.7-arm64)ccfindR_1.33.0.tar.gz(r-4.7-x86_64)ccfindR_1.33.0.tar.gz(r-4.6-arm64)ccfindR_1.33.0.tar.gz(r-4.6-x86_64)
ccfindR_1.31.0.tgz(r-4.5-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
ccfindR/json (API)

# Install 'ccfindR' in R:

install.packages('ccfindR', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Uses libs:

gsl– GNU Scientific Library (GSL)
c++– GNU Standard C++ Library v3

On BioConductor:ccfindR-1.33.0(bioc 3.24)ccfindR-1.32.0(bioc 3.23)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

transcriptomics singlecell immunooncology bayesian clustering gsl cpp

4.04 score 11 scripts 45 exports 30 dependencies

Last updated from:71cf4a4452. Checks:8 NOTE, 1 OK, 5 FAIL. Indexed: yes.

Target	Result	Time
bioc-checks	NOTE	205
linux-devel-arm64	NOTE	257
linux-devel-x86_64	NOTE	342
source / vignettes	OK	246
linux-release-arm64	NOTE	272
linux-release-x86_64	NOTE	390
macos-release-arm64	FAIL	90
macos-release-x86_64	FAIL	201
macos-oldrel-arm64	FAIL	83
macos-oldrel-x86_64	FAIL	230
windows-devel	NOTE	269
windows-release	NOTE	263
windows-oldrel	NOTE	243
wasm-release	FAIL	156

Exports:assignCelltype basis basis<-build_tree cell_map cluster_id coeff coeff<-colData colData<-counts counts<-dbasis dbasis<-dcoeff factorize feature_map filter_cells filter_genes gene_map measure measure<-meta_gene.cv meta_genes newick normalize_count optimal_rank plot plot_genes plot_tree ranks ranks<-read_10x remove_zeros rename_tips rowData rowData<-scNMFSet show simulate_data simulate_whx vb_factorize visualize_clusters write_10x write_meta

Dependencies:abind ape Biobase BiocGenerics DelayedArray digest generics GenomicRanges gtools IRanges irlba lattice Matrix MatrixGenerics matrixStats nlme rbibutils RColorBrewer Rcpp RcppEigen Rdpack Rmpi Rtsne S4Arrays S4Vectors Seqinfo SingleCellExperiment SparseArray SummarizedExperiment XVector

ccfindR: single-cell RNA-seq analysis using Bayesian non-negative matrix factorization

Rendered fromccfindR.Rmdusingknitr::rmarkdown

Last update: 2019-07-08
Started: 2018-03-17

Help page	Topics
Subsetting scNMFSet object	[,scNMFSet,ANY,ANY,ANY-method [,scNMFSet-method
Cell type assignment via GSEA	assignCelltype
Basis matrices in an Object	basis
Basis matrix accessor	basis,scNMFSet-method
Generics for basis matrix assignment	basis<-
Modify basis matrices	basis<-,scNMFSet-method
Build tree connecting clusters at different ranks	build_tree
ccfindR: Cancer Clone FindeR	ccfindR-package ccfindR
Plot heatmap of clustering coefficient matrix	cell_map
Assign cells into clusters	cluster_id
Coefficient matrices in an Object	coeff
Coefficient matrix accessor	coeff,scNMFSet-method
Generics for coefficient matrix assignment	coeff<-
Modify coefficient matrices	coeff<-,scNMFSet-method
Sample annotation accessor	colData,scNMFSet-method
Cell annotation assignment	colData<-,scNMFSet,ANY-method
Accessor for count matrix	counts,scNMFSet-method
Assignment of count matrix	counts<-,scNMFSet-method
Basis SD matrix accessor	dbasis
Basis SD matrix accessor	dbasis,scNMFSet-method
Basis SD matrix assignment	dbasis<-
Modify dbasis matrices	dbasis<-,scNMFSet-method
Coeff SD matrix accessor	dcoeff
Coeffcient SD matrix accessor	dcoeff,scNMFSet-method
Coeff SD matrix assignment	dcoeff<-
Modify dcoeff matrices	dcoeff<-,scNMFSet-method
Maximum likelihood factorization	factorize
Plot heatmap of basis matrix	feature_map
Filter cells with quality control criteria	filter_cells
Filter genes with quality control criteria	filter_genes
Plot heatmap of metagene matrix	gene_map
Factorization measures in an Object	measure
Rank measure accessor	measure,scNMFSet-method
Generics for factorization measure assignment	measure<-
Modify factorization measure	measure<-,scNMFSet-method
Meta gene table with CV	meta_gene.cv
Find metagenes from basis matrix	meta_genes
Generate Newick format tree string from tree list object	newick
Normalize count data	normalize_count
Determine optimal rank	optimal_rank
Plot gene variance distributions	plot_genes
Plot cluster tree	plot_tree
Rank values in an Object	ranks
Rank accessor	ranks,scNMFSet-method
Generics for ranks assignment	ranks<-
Modify ranks	ranks<-,scNMFSet-method
Read 10x data and generate scNMF object	read_10x
Remove rows or columns that are empty from an object	remove_zeros
Rename tips of trees with cell types	rename_tips
Feature annotation accessor	rowData,scNMFSet-method
Gene annotation assignment	rowData<-,scNMFSet-method
Create 'scNMFSet' object	scNMFSet
Class 'scNMFSet' for storing input data and results	plot,scNMFSet,ANY-method scNMFSet-class
Display object	show,scNMFSet-method
Generate simulated data for factorization	simulate_data
Simulate factor matrices and data using priors	simulate_whx
Bayesian NMF inference of count matrix	vb_factorize
Visualize clusters	visualize_clusters
Write 10x data files	write_10x
Write meta genes to a file	write_meta

Package: ccfindR 1.33.0

ccfindR: Cancer Clone Finder

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Help Manual

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