Package: mxfda 0.2.3

Alex Soupir

mxfda: A Functional Data Analysis Package for Spatial Single Cell Data

Methods and tools for deriving spatial summary functions from single-cell imaging data and performing functional data analyses. Functions can be applied to other single-cell technologies such as spatial transcriptomics. Functional regression and functional principal component analysis methods are in the 'refund' package <https://cran.r-project.org/package=refund> while calculation of the spatial summary functions are from the 'spatstat' package <https://spatstat.org/>.

Authors:Julia Wrobel [aut], Alex Soupir [aut, cre]

mxfda_0.2.3.tar.gz
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mxfda_0.2.3.tgz(r-4.6-any)mxfda_0.2.3.tgz(r-4.5-any)
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mxfda_0.2.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
mxfda/json (API)

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

Bug tracker:https://github.com/julia-wrobel/mxfda/issues

Datasets:
  • lung_df - Multiplex imaging data from a non-small cell lung cancer study.
  • lung_FDA - Multiplex imaging data from a non-small cell lung cancer study
  • ovarian_FDA - Multiplex imaging data from an ovarian cancer tumor microarray

On CRAN:

Conda:

4.62 score 1 stars 14 scripts 213 downloads 35 exports 130 dependencies

Last updated from:0d0d924229. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK236
source / vignettesOK467
linux-release-x86_64OK281
macos-release-arm64OK196
macos-oldrel-arm64OK287
windows-develOK202
windows-releaseOK187
windows-oldrelOK195
wasm-releaseOK214

Exports:%>%add_summary_functionbivariatecan_permuteentropyextract_cextract_entropyextract_fpca_objectextract_fpca_scoresextract_modelextract_spatial_summaryextract_summary_functionsextract_surfacefilter_datafilter_spatialGcrossGestget_datais.emptyKcrossKestLcrossLestmake_mxfdametric.existsone_zeroplot_fpcplot_mfpcrun_fcmrun_fpcarun_mfcmrun_mfpcarun_sofrspatialTIME_summary_functionsunivariate

Dependencies:abindashaudiobeeprbitopsbootbriocallrclassclicliprclustercodetoolscolorspacecpp11crayondeldirdescdeSolvediffobjdigestdplyre1071evaluatefarverfdafdsFNNfsfuturefuture.applygamm4genericsggplot2globalsgluegoftestgrpreggtablehdrcdeisobandjsonlitekernlabKernSmoothkslabelinglatticelifecyclelistenvlme4locfitmagicmagrittrMASSMatrixmclustmgcvminqamiraimulticoolmvtnormnanonextnlmenloptrotelparallellypbapplypbspcaPPpillarpkgbuildpkgconfigpkgloadplyrpolyclippracmapraiseprocessxprogressrproxypspurrrqs2R.methodsS3R.ooR.utilsR6rainbowrbibutilsRColorBrewerRcppRcppEigenRcppParallelRCurlRdpackreformulasrefundreshape2rlangRLRsimrpartrprojrootS7scalessessioninfoSimDesignSpatEntropyspatstatspatstat.dataspatstat.explorespatstat.geomspatstat.linnetspatstat.modelspatstat.randomspatstat.sparsespatstat.univarspatstat.utilsstringfishstringistringrtensortestthattibbletidyrtidyselectutf8vctrsviridisLitewaldowithr

Defining an mxFDA object
VectraPolarisData | Setting up the mxFDA object | Make mxFDAobject | Spatial summary functions based on point processes | Univariate summary functions | Plotting the mxFDA object | Bivariate summary functions | Plotting bivariate G | Entropy | Exploring the S4 object | SpatialTIME | References
Rendered frommx_fda.Rmdusingknitr::rmarkdown

Last update: 2024-10-04
Started: 2023-05-16

Functional principal component analysis for spatial summary functions
Functional data notation | Functional principal components analysis (FPCA) | Background on FPCA | Implementing FPCA | Load and visualize data | Run and visualize FPCA | Multilevel functional principal components analysis (MFPCA) | MFPCA Background | Implementing MFPCA | Run and visualize MFPCA | References
Rendered frommx_fpca.Rmdusingknitr::rmarkdown

Last update: 2024-10-04
Started: 2023-11-16

Functional regression with spatial summary functions as covariates
Ovarian cancer multiplex imaging data | Functional regression models for survival outcomes | Cox regression using functional principal components as covariates | Linear and additive functional Cox regression models | Linear functional Cox model (LFCM) | Additive functional Cox model (AFCM) | Model summaries and C-index | Functional regression models for binary and continuous outcomes | Continuous outcome | Binary outcome
Rendered frommx_funreg.Rmdusingknitr::rmarkdown

Last update: 2024-10-04
Started: 2023-05-16