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✨
DESCRIPTION |NEWS
card.svg |card.png
nipalsMCIA/json (API)

# 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

6.10 score 7 stars 9 scripts 28 exports 78 dependencies

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

TargetResultTimeFilesSyslog
bioc-checksNOTE242
linux-devel-x86_64OK460
source / vignettesOK449
linux-release-x86_64OK426
macos-release-arm64OK235
macos-oldrel-arm64OK313
windows-develOK293
windows-releaseOK330
windows-oldrelOK312
wasm-releaseOK237

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
Introduction | Motivation | Overview | Installation | Preview of the NCI-60 dataset | Running and reviewing the MCIA output | Brief overview of the Global Scores Matrix ($F$) | Brief overview of the Global Loadings Matrix ($A$) | Part 1: Interpreting Global Factor Scores | nipals_multiblock() Generates Basic Visualizations | Visualizing a Factor Plot with Only Global Factor Scores | Visualizing the Clustering of Samples by Factor Scores | Part 2: Interpreting Global Loadings | Pseudoeigenvalues Representing the Contribution of Each Omic to the Global Factor Score | Visualize All Feature Loadings on Two Axes | Scree Plot: Visualizing the Top Features per Factor | Factor 1 | Factor 2 | Factor 4 | Pathway Analysis for the Top Factors using Data from Gene-Centric Omics Blocks | Gather Data and Generate the Report | Investigating the GSEA Summary Table | Session Info
Rendered fromAnalysis-of-MCIA-Decomposition.Rmdusingknitr::rmarkdown

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

Single Cell Analysis
Introduction | Installation | Vignette Pipeline | Data | All Sources | Bioconductor | 10x Genomics and Seurat | MCIA | Metadata | Running the decomposition | Visualization | Define colors | Eigenvalue scree plot | Projection plot | Global scores heatmap | Block weights heatmap | Loadings | Top features | Factor 1 | Factor 4 | Deep dive: Seurat analysis | Read in and process the data | Quality control | Metrics summary | GEX QC metrics | Before filtering | After filtering | Standard Seurat pipeline | Dimensionality reduction | Load in the processed object | PCA | UMAP | Marker overlays | Load marker genes | Dot plots | GEX | ADT | Feature plots | Violin plots | Annotate cell clusters | Annotations | UMAPs | Check the annotations | Save for MCIA | Session Info
Rendered fromSingle-Cell-Analysis.Rmdusingknitr::rmarkdown

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

Predicting New MCIA scores
Predicting MCIA global (factor) scores for new test samples | Installation | Split the data | Run nipalsMCIA on training data | Visualize model on training data using metadata on cancer type | Generate factor scores for test data using the MCIA_train model | Visualize new scores with old | Session Info
Rendered fromPredicting-New-Scores.Rmdusingknitr::rmarkdown

Last update: 2024-08-31
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