Package: nipalsMCIA 1.11.0

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:Maximilian Mattessich [cre], Joaquin Reyna [aut], Edel Aron [aut], Ferhat Ay [aut], Steven Kleinstein [aut], Anna Konstorum [aut]

nipalsMCIA_1.11.0.tar.gz
nipalsMCIA_1.11.0.zip(r-4.7)nipalsMCIA_1.11.0.zip(r-4.6)nipalsMCIA_1.11.0.zip(r-4.5)
nipalsMCIA_1.11.0.tgz(r-4.6-any)nipalsMCIA_1.11.0.tgz(r-4.5-any)
nipalsMCIA_1.11.0.tar.gz(r-4.7-any)nipalsMCIA_1.11.0.tar.gz(r-4.6-any)
nipalsMCIA_1.11.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
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

Datasets:

On BioConductor:nipalsMCIA-1.11.0(bioc 3.24)nipalsMCIA-1.10.0(bioc 3.23)

softwareclusteringclassificationmultiplecomparisonnormalizationpreprocessingsinglecell

6.30 score 7 stars 16 scripts 308 downloads 28 exports 78 dependencies

Last updated from:e4c24cee0d. Checks:1 NOTE, 9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksNOTE233
linux-devel-x86_64OK474
source / vignettesOK442
linux-release-x86_64OK497
macos-release-arm64OK230
macos-oldrel-arm64OK243
windows-develOK301
windows-releaseOK422
windows-oldrelOK350
wasm-releaseOK183

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:abindBHBiobaseBiocBaseUtilsBiocGenericsBiocParallelcirclizecliclueclustercodetoolscolorspaceComplexHeatmapcowplotcpp11crayondata.tableDelayedArraydigestdoParalleldplyrfarverfastmatchfgseaforeachformatRfutile.loggerfutile.optionsgenericsGenomicRangesGetoptLongggplot2GlobalOptionsgluegtableIRangesisobanditeratorslabelinglambda.rlatticelifecyclemagrittrMatrixMatrixGenericsmatrixStatsMultiAssayExperimentpillarpkgconfigpngpracmapurrrR6RColorBrewerRcppRcppEigenrjsonrlangRSpectraS4ArraysS4VectorsS7scalesSeqinfoshapesnowSparseArraystringistringrSummarizedExperimenttibbletidyrtidyselectutf8vctrsviridisLitewithrXVector

Analysis of MCIA Decomposition

Max Mattessich, Joaquin Reyna, Edel Aron, Anna Konstorum

Rendered fromAnalysis-of-MCIA-Decomposition.Rmdusingknitr::rmarkdownon May 30 2026.

Last update: 2026-01-19
Started: 2024-08-31

Predicting New MCIA scores

Max Mattessich, Joaquin Reyna, Edel Aron, Anna Konstorum

Rendered fromPredicting-New-Scores.Rmdusingknitr::rmarkdownon May 30 2026.

Last update: 2024-08-31
Started: 2024-08-31

Single Cell Analysis

Max Mattessich, Joaquin Reyna, Edel Aron, Anna Konstorum

Rendered fromSingle-Cell-Analysis.Rmdusingknitr::rmarkdownon May 30 2026.

Last update: 2026-01-19
Started: 2024-08-31

Readme and manuals

Help Manual

Help pageTopics
Block-level preprocessingblock_preproc
block_weights_heatmapblock_weights_heatmap
Centered Column Profile Pre-processingcc_preproc
Centered Column Profile Pre-processingcol_preproc
NCI-60 Multi-Omics Datadata_blocks
Deflation via block loadingsdeflate_block_bl
Deflation via global scoresdeflate_block_gs
Extract a list of harmonized data matrices from an MAE objectextract_from_mae
Assigning colors to different omicsget_colors
Assigning colors to different values of a metadata columnget_metadata_colors
Computes the total variance of a multi-omics datasetget_tv
global_scores_eigenvalues_plotglobal_scores_eigenvalues_plot
Plotting a heatmap of global factors scores (sample v. factors)global_scores_heatmap
Perform biological annotation-based comparisongsea_report
NCI-60 Multi-Omics Metadatametadata_NCI60
NIPALS Iterationnipals_iter
Main NIPALS computation loopnipals_multiblock
An S4 class to contain results computed with `nipals_multiblock()`NipalsResult NipalsResult-class
Accessor function for block loadingsnmb_get_bl
Accessor function for block scoresnmb_get_bs
Accessor function for block score weightsnmb_get_bs_weights
Accessor function for eigenvaluesnmb_get_eigs
Accessor function for global loadingsnmb_get_gl
Accessor function for global scoresnmb_get_gs
Accessor function for metadatanmb_get_metadata
Ranked global loadings dataframeord_loadings
Prediction of new global scores based on block loadings and weightspredict_gs
projection_plotprojection_plot
Create an MAE object from a list of data matrices and column datasimple_mae
Visualize ranked loadingsvis_load_ord
Visualize all loadings on two factor axesvis_load_plot