Package: nipalsMCIA 1.5.2
Maximilian Mattessich
nipalsMCIA: Multiple Co-Inertia Analysis via the NIPALS Method
Computes Multiple Co-Inertia Analysis (MCIA), a dimensionality reduction (jDR) algorithm, for a multi-block dataset using a modification to the Nonlinear Iterative Partial Least Squares method (NIPALS) proposed in (Hanafi et. al, 2010). Allows multiple options for row- and table-level preprocessing, and speeds up computation of variance explained. Vignettes detail application to bulk- and single cell- multi-omics studies.
Authors:
nipalsMCIA_1.5.2.tar.gz
nipalsMCIA_1.5.2.zip(r-4.5)nipalsMCIA_1.5.2.zip(r-4.4)nipalsMCIA_1.5.2.zip(r-4.3)
nipalsMCIA_1.5.2.tgz(r-4.4-any)nipalsMCIA_1.5.2.tgz(r-4.3-any)
nipalsMCIA_1.5.2.tar.gz(r-4.5-noble)nipalsMCIA_1.5.2.tar.gz(r-4.4-noble)
nipalsMCIA_1.5.2.tgz(r-4.4-emscripten)nipalsMCIA_1.5.1.tgz(r-4.3-emscripten)
nipalsMCIA.pdf |nipalsMCIA.html✨
nipalsMCIA/json (API)
NEWS
| # Install 'nipalsMCIA' in R: |
| install.packages('nipalsMCIA', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/muunraker/nipalsmcia/issues
- data_blocks - NCI-60 Multi-Omics Data
- metadata_NCI60 - NCI-60 Multi-Omics Metadata
On BioConductor:nipalsMCIA-1.5.1(bioc 3.21)nipalsMCIA-1.4.1(bioc 3.20)
softwareclusteringclassificationmultiplecomparisonnormalizationpreprocessingsinglecell
Last updated 4 days agofrom:cc5fde526d. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Dec 19 2024 |
| R-4.5-win | OK | Dec 19 2024 |
| R-4.5-linux | OK | Dec 19 2024 |
| R-4.4-win | OK | Dec 19 2024 |
| R-4.4-mac | OK | Dec 19 2024 |
| R-4.3-win | OK | Dec 19 2024 |
| R-4.3-mac | OK | Dec 19 2024 |
Exports:block_preprocblock_weights_heatmapcc_preproccol_preprocdeflate_block_bldeflate_block_gsextract_from_maeget_colorsget_metadata_colorsget_tvglobal_scores_eigenvalues_plotglobal_scores_heatmapgsea_reportnipals_iternipals_multiblocknmb_get_blnmb_get_bsnmb_get_bs_weightsnmb_get_eigsnmb_get_glnmb_get_gsnmb_get_metadataord_loadingspredict_gsprojection_plotsimple_maevis_load_ordvis_load_plot
Dependencies:abindaskpassBHBiobaseBiocBaseUtilsBiocGenericsBiocParallelcirclizecliclueclustercodetoolscolorspaceComplexHeatmapcowplotcpp11crayoncurldata.tableDelayedArraydigestdoParalleldplyrfansifarverfastmatchfgseaforeachformatRfutile.loggerfutile.optionsgenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesGetoptLongggplot2GlobalOptionsgluegtablehttrIRangesisobanditeratorsjsonlitelabelinglambda.rlatticelifecyclemagrittrMASSMatrixMatrixGenericsmatrixStatsmgcvmimeMultiAssayExperimentmunsellnlmeopensslpillarpkgconfigpngpracmapurrrR6RColorBrewerRcppRcppEigenrjsonrlangRSpectraS4ArraysS4VectorsscalesshapesnowSparseArraystringistringrSummarizedExperimentsystibbletidyrtidyselectUCSC.utilsutf8vctrsviridisLitewithrXVectorzlibbioc
Analysis of MCIA Decomposition
Rendered fromAnalysis-of-MCIA-Decomposition.Rmdusingknitr::rmarkdownon Dec 19 2024.Last update: 2024-08-31
Started: 2024-08-31
Predicting New MCIA scores
Rendered fromPredicting-New-Scores.Rmdusingknitr::rmarkdownon Dec 19 2024.Last update: 2024-08-31
Started: 2024-08-31
Single Cell Analysis
Rendered fromSingle-Cell-Analysis.Rmdusingknitr::rmarkdownon Dec 19 2024.Last update: 2024-08-31
Started: 2024-08-31
