Package: parafac4microbiome 1.1.2.9000

parafac4microbiome: Parallel Factor Analysis Modelling of Longitudinal Microbiome Data

Creation and selection of PARAllel FACtor Analysis (PARAFAC) models of longitudinal microbiome data. You can import your own data with our import functions or use one of the example datasets to create your own PARAFAC models. Selection of the optimal number of components can be done using assessModelQuality() and assessModelStability(). The selected model can then be plotted using plotPARAFACmodel(). The Parallel Factor Analysis method was originally described by Caroll and Chang (1970) <doi:10.1007/BF02310791> and Harshman (1970) <https://www.psychology.uwo.ca/faculty/harshman/wpppfac0.pdf>.

Authors:Geert Roelof van der Ploeg [aut, cre], Johan Westerhuis [ctb], Anna Heintz-Buschart [ctb], Age Smilde [ctb], University of Amsterdam [cph, fnd]

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parafac4microbiome.pdf |parafac4microbiome.html✨
parafac4microbiome/json (API)
NEWS

# Install 'parafac4microbiome' in R:
install.packages('parafac4microbiome', repos = c('https://grvanderploeg.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/grvanderploeg/parafac4microbiome/issues

Pkgdown site:https://grvanderploeg.com

Datasets:

On CRAN:

Conda:

dimensionality-reductionmicrobiomemicrobiome-datamultiwaymultiway-algorithmsparallel-factor-analysis

6.26 score 7 stars 13 scripts 252 downloads 32 exports 87 dependencies

Last updated 2 hours agofrom:f1bf2dc6e1. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKApr 02 2025
R-4.5-winOKApr 02 2025
R-4.5-macOKApr 02 2025
R-4.5-linuxOKApr 02 2025
R-4.4-winOKApr 02 2025
R-4.4-macOKApr 02 2025
R-4.4-linuxOKApr 02 2025
R-4.3-winOKApr 02 2025
R-4.3-macOKApr 02 2025

Exports:%>%assessModelQualityassessModelStabilitycalculateFMScalculateSparsitycalculateVarExpcalcVarExpPerComponentcorcondiafac_to_vectflipLoadingsimportMicrobiotaProcessimportPhyloseqimportTreeSummarizedExperimentinitializePARAFACmultiwayCentermultiwayCLRmultiwayScaleparafacparafac_core_alsparafac_funparafac_gradientplotModelMetricplotModelStabilityplotModelTCCsplotPARAFACmodelprocessDataCubereinflateFacreinflateTensorreshapeDatasortComponentstransformPARAFACloadingsvect_to_fac

Dependencies:abindbackportsbayesmbootbroomcarcarDatacliCMLScodetoolscolorspacecompositionscorrplotcowplotcpp11DEoptimRDerivdoBydoParalleldplyrfansifarverforeachFormulagenericsggplot2ggpubrggrepelggsciggsignifgluegridExtragtableisobanditeratorslabelinglatticelifecyclelme4magrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamizemodelrmultiwaymunsellnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigpolynompracmapurrrquadprogquantregR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangrobustbaserstatixrTensorscalesSparseMstringistringrsurvivaltensorAtibbletidyrtidyselectutf8vctrsviridisLitewithr

Fujita2023_analysis

Rendered fromFujita2023_analysis.Rmdusingknitr::rmarkdownon Apr 02 2025.

Last update: 2025-03-21
Started: 2024-03-08

Introduction to PARAFAC modelling

Rendered fromPARAFAC_introduction.Rmdusingknitr::rmarkdownon Apr 02 2025.

Last update: 2025-03-21
Started: 2024-03-08

Shao2019_analysis

Rendered fromShao2019_analysis.Rmdusingknitr::rmarkdownon Apr 02 2025.

Last update: 2025-03-21
Started: 2024-03-08

vanderPloeg2024_analysis

Rendered fromvanderPloeg2024_analysis.Rmdusingknitr::rmarkdownon Apr 02 2025.

Last update: 2025-03-21
Started: 2024-03-08

Readme and manuals

Help Manual

Help pageTopics
Create randomly initialized models to determine the correct number of components by assessing model quality metrics.assessModelQuality
Bootstrapping procedure to determine PARAFAC model stability for a given number of components.assessModelStability
Calculate Factor Match Score for all initialized models.calculateFMS
Calculate sparsity across the feature mode of a multi-way array.calculateSparsity
Calculate the variation explained by a PARAFAC model.calculateVarExp
Calculate the variance explained of a PARAFAC model, per componentcalcVarExpPerComponent
Core Consistency Diagnostic (CORCONDIA) calculationcorcondia
Vectorize Fac objectfac_to_vect
Sign flip the loadings of many randomly initialized models to make consistent overview plots.flipLoadings
Fujita2023 longitudinal microbiome dataFujita2023
Import MicrobiotaProcess object for PARAFAC modellingimportMicrobiotaProcess
Import Phyloseq object for PARAFAC modellingimportPhyloseq
Import TreeSummarizedExperiment object for PARAFAC modellingimportTreeSummarizedExperiment
Initialize PARAFAC algorithm input vectorsinitializePARAFAC
Center a multi-way arraymultiwayCenter
Perform a centered log-ratio transform over a multi-way arraymultiwayCLR
Scale a multi-way arraymultiwayScale
Parallel Factor Analysisparafac
Internal PARAFAC alternating least-squares (ALS) core algorithmparafac_core_als
PARAFAC loss function calculationparafac_fun
Calculate gradient of PARAFAC model.parafac_gradient
Plot diagnostics of many initialized PARAFAC models.plotModelMetric
Plot a summary of the loadings of many initialized parafac models.plotModelStability
Plots Tucker Congruence Coefficients of randomly initialized models.plotModelTCCs
Plot a PARAFAC modelplotPARAFACmodel
Process a multi-way array of count data.processDataCube
Calculate Xhat from a model Fac objectreinflateFac
Create a tensor out of a set of matrices similar to a component model.reinflateTensor
Reorganize longitudinal microbiome into a data cube ready for PARAFAC modelling.reshapeData
Shao2019 longitudinal microbiome dataShao2019
Sort PARAFAC components based on variance explained per component.sortComponents
Transform PARAFAC loadings to an orthonormal basis. Note: this function only works for 3-way PARAFAC models.transformPARAFACloadings
vanderPloeg2024 longitudinal datasetvanderPloeg2024
Convert vectorized output of PARAFAC to a Fac list object with all loadings per mode.vect_to_fac