Package: MCbiclust 1.31.0
Robert Bentham
MCbiclust: Massive correlating biclusters for gene expression data and associated methods
Custom made algorithm and associated methods for finding, visualising and analysing biclusters in large gene expression data sets. Algorithm is based on with a supplied gene set of size n, finding the maximum strength correlation matrix containing m samples from the data set.
Authors:
MCbiclust_1.31.0.tar.gz
MCbiclust_1.31.0.zip(r-4.5)MCbiclust_1.31.0.zip(r-4.4)MCbiclust_1.31.0.zip(r-4.3)
MCbiclust_1.31.0.tgz(r-4.4-any)MCbiclust_1.31.0.tgz(r-4.3-any)
MCbiclust_1.31.0.tar.gz(r-4.5-noble)MCbiclust_1.31.0.tar.gz(r-4.4-noble)
MCbiclust_1.31.0.tgz(r-4.4-emscripten)MCbiclust_1.31.0.tgz(r-4.3-emscripten)
MCbiclust.pdf |MCbiclust.html✨
MCbiclust/json (API)
| # Install 'MCbiclust' in R: |
| install.packages('MCbiclust', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
- CCLE_samples - Clinical information for CCLE data
- CCLE_small - Subset of expression levels of CCLE data
- Mitochondrial_genes - List of known mitochondrial genes
On BioConductor:MCbiclust-1.31.0(bioc 3.21)MCbiclust-1.30.0(bioc 3.20)
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
immunooncologyclusteringmicroarraystatisticalmethodsoftwarernaseqgeneexpression
Last updated 2 months agofrom:990c62b8e3. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 29 2024 |
| R-4.5-win | OK | Nov 29 2024 |
| R-4.5-linux | OK | Nov 29 2024 |
| R-4.4-win | OK | Nov 29 2024 |
| R-4.4-mac | OK | Nov 29 2024 |
| R-4.3-win | OK | Nov 29 2024 |
| R-4.3-mac | OK | Nov 29 2024 |
Exports:CorScoreCalcCVEvalCVPlotFindSeedForkClassifierGOEnrichmentAnalysisHclustGenesHiCorMultiSampleSortPrepPC1AlignPC1VecFunPointScoreCalcSampleSortSilhouetteClustGroupsThresholdBic
Dependencies:AnnotationDbiaskpassbackportsbase64encBHBiobaseBiocGenericsBiocParallelBiostringsbitbit64blobbslibcachemcheckmatecliclustercodetoolscolorspacecpp11crayoncurldata.tableDBIdigestdoParalleldplyrdynamicTreeCutevaluatefansifarverfastclusterfastmapfontawesomeforcatsforeachforeignformatRFormulafsfutile.loggerfutile.optionsgenericsGenomeInfoDbGenomeInfoDbDataGGallyggplot2ggstatsglueGO.dbgridExtragtablehighrHmischmshtmlTablehtmltoolshtmlwidgetshttrimputeIRangesisobanditeratorsjquerylibjsonliteKEGGRESTknitrlabelinglambda.rlatticelifecyclemagrittrMASSMatrixmatrixStatsmemoisemgcvmimemunsellnlmennetopensslorg.Hs.eg.dbpatchworkpillarpkgconfigplogrplyrpngpreprocessCoreprettyunitsprogresspurrrR6rappdirsRColorBrewerRcpprlangrmarkdownrpartRSQLiterstudioapiS4VectorssassscalessnowstringistringrsurvivalsystibbletidyrtidyselecttinytexUCSC.utilsutf8vctrsviridisviridisLiteWGCNAwithrxfunXVectoryamlzlibbioc
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Clinical information for CCLE data | CCLE_samples |
| Subset of expression levels of CCLE data | CCLE_small |
| Calculate correlation score | CorScoreCalc |
| Method for the calculation of a correlation vector | CVEval |
| Make correlation vector plot | CVPlot |
| Find highly correlated seed of samples for gene expression matrix | FindSeed |
| Calculate gene set enrichment of correlation vector using Mann-Whitney test | GOEnrichmentAnalysis |
| Find the most highly correlated genes using hierarchical clustering | HclustGenesHiCor |
| MCbiclust: Massively Correlated biclustering | MCbiclust-package MCbiclust |
| List of known mitochondrial genes | Mitochondrial_genes |
| Calculate PC1 vector of found pattern | PC1VecFun |
| Calculate PointScore | PointScoreCalc |
| Methods for ordering samples | MultiSampleSortPrep SampleSort |
| Slihouette validation of correlation vector clusters | SilhouetteClustGroups |
| Methods for defining a bicluster | ForkClassifier PC1Align ThresholdBic |
