Package: ccfindR 1.27.0

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_1.27.0.tar.gz
ccfindR_1.27.0.zip(r-4.5)ccfindR_1.27.0.zip(r-4.4)ccfindR_1.27.0.zip(r-4.3)

ccfindR_1.27.0.tar.gz(r-4.5-noble)ccfindR_1.27.0.tar.gz(r-4.4-noble)
ccfindR.pdf |ccfindR.html✨
ccfindR/json (API)
NEWS

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

Peer review:

Uses libs:
  • gsl– GNU Scientific Library (GSL)
  • c++– GNU Standard C++ Library v3

On BioConductor:ccfindR-1.27.0(bioc 3.21)ccfindR-1.26.0(bioc 3.20)

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

transcriptomicssinglecellimmunooncologybayesianclusteringgslcpp

4.00 score 10 scripts 238 downloads 45 exports 42 dependencies

Last updated 2 months agofrom:8f11dda04f. Checks:OK: 1 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 14 2024
R-4.5-win-x86_64NOTEDec 14 2024
R-4.5-linux-x86_64NOTEDec 14 2024
R-4.4-win-x86_64NOTEDec 14 2024
R-4.3-win-x86_64NOTEDec 14 2024

Exports:assignCelltypebasisbasis<-build_treecell_mapcluster_idcoeffcoeff<-colDatacolData<-countscounts<-dbasisdbasis<-dcoefffactorizefeature_mapfilter_cellsfilter_genesgene_mapmeasuremeasure<-meta_gene.cvmeta_genesnewicknormalize_countoptimal_rankplotplot_genesplot_treeranksranks<-read_10xremove_zerosrename_tipsrowDatarowData<-scNMFSetshowsimulate_datasimulate_whxvb_factorizevisualize_clusterswrite_10xwrite_meta

Dependencies:abindapeaskpassBiobaseBiocGenericscrayoncurlDelayedArraydigestgenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesgtoolshttrIRangesirlbajsonlitelatticeMatrixMatrixGenericsmatrixStatsmimenlmeopensslR6rbibutilsRColorBrewerRcppRcppEigenRdpackRmpiRtsneS4ArraysS4VectorsSingleCellExperimentSparseArraySummarizedExperimentsysUCSC.utilsXVectorzlibbioc

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

Jun Woo, Constantin Aliferis, Jinhua Wang

Rendered fromccfindR.Rmdusingknitr::rmarkdownon Dec 14 2024.

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

Readme and manuals

Help Manual

Help pageTopics
Subsetting scNMFSet object[,scNMFSet,ANY,ANY,ANY-method [,scNMFSet-method
Cell type assignment via GSEAassignCelltype
Basis matrices in an Objectbasis
Basis matrix accessorbasis,scNMFSet-method
Generics for basis matrix assignmentbasis<-
Modify basis matricesbasis<-,scNMFSet-method
Build tree connecting clusters at different ranksbuild_tree
ccfindR: Cancer Clone FindeRccfindR-package ccfindR
Plot heatmap of clustering coefficient matrixcell_map
Assign cells into clusterscluster_id
Coefficient matrices in an Objectcoeff
Coefficient matrix accessorcoeff,scNMFSet-method
Generics for coefficient matrix assignmentcoeff<-
Modify coefficient matricescoeff<-,scNMFSet-method
Sample annotation accessorcolData,scNMFSet-method
Cell annotation assignmentcolData<-,scNMFSet,ANY-method
Accessor for count matrixcounts,scNMFSet-method
Assignment of count matrixcounts<-,scNMFSet-method
Basis SD matrix accessordbasis
Basis SD matrix accessordbasis,scNMFSet-method
Basis SD matrix assignmentdbasis<-
Modify dbasis matricesdbasis<-,scNMFSet-method
Coeff SD matrix accessordcoeff
Coeffcient SD matrix accessordcoeff,scNMFSet-method
Coeff SD matrix assignmentdcoeff<-
Modify dcoeff matricesdcoeff<-,scNMFSet-method
Maximum likelihood factorizationfactorize
Plot heatmap of basis matrixfeature_map
Filter cells with quality control criteriafilter_cells
Filter genes with quality control criteriafilter_genes
Plot heatmap of metagene matrixgene_map
Factorization measures in an Objectmeasure
Rank measure accessormeasure,scNMFSet-method
Generics for factorization measure assignmentmeasure<-
Modify factorization measuremeasure<-,scNMFSet-method
Meta gene table with CVmeta_gene.cv
Find metagenes from basis matrixmeta_genes
Generate Newick format tree string from tree list objectnewick
Normalize count datanormalize_count
Determine optimal rankoptimal_rank
Plot gene variance distributionsplot_genes
Plot cluster treeplot_tree
Rank values in an Objectranks
Rank accessorranks,scNMFSet-method
Generics for ranks assignmentranks<-
Modify ranksranks<-,scNMFSet-method
Read 10x data and generate scNMF objectread_10x
Remove rows or columns that are empty from an objectremove_zeros
Rename tips of trees with cell typesrename_tips
Feature annotation accessorrowData,scNMFSet-method
Gene annotation assignmentrowData<-,scNMFSet-method
Create 'scNMFSet' objectscNMFSet
Class 'scNMFSet' for storing input data and resultsplot,scNMFSet,ANY-method scNMFSet-class
Display objectshow,scNMFSet-method
Generate simulated data for factorizationsimulate_data
Simulate factor matrices and data using priorssimulate_whx
Bayesian NMF inference of count matrixvb_factorize
Visualize clustersvisualize_clusters
Write 10x data fileswrite_10x
Write meta genes to a filewrite_meta