Package: mistyR 1.15.0

Jovan Tanevski

mistyR: Multiview Intercellular SpaTial modeling framework

mistyR is an implementation of the Multiview Intercellular SpaTialmodeling framework (MISTy). MISTy is an explainable machine learning framework for knowledge extraction and analysis of single-cell, highly multiplexed, spatially resolved data. MISTy facilitates an in-depth understanding of marker interactions by profiling the intra- and intercellular relationships. MISTy is a flexible framework able to process a custom number of views. Each of these views can describe a different spatial context, i.e., define a relationship among the observed expressions of the markers, such as intracellular regulation or paracrine regulation, but also, the views can also capture cell-type specific relationships, capture relations between functional footprints or focus on relations between different anatomical regions. Each MISTy view is considered as a potential source of variability in the measured marker expressions. Each MISTy view is then analyzed for its contribution to the total expression of each marker and is explained in terms of the interactions with other measurements that led to the observed contribution.

Authors:Jovan Tanevski [cre, aut], Ricardo Omar Ramirez Flores [ctb], Philipp Schäfer [ctb]

mistyR_1.15.0.tar.gz
mistyR_1.15.0.zip(r-4.5)mistyR_1.15.0.zip(r-4.4)mistyR_1.15.0.zip(r-4.3)
mistyR_1.15.0.tgz(r-4.4-any)mistyR_1.15.0.tgz(r-4.3-any)
mistyR_1.15.0.tar.gz(r-4.5-noble)mistyR_1.15.0.tar.gz(r-4.4-noble)
mistyR_1.15.0.tgz(r-4.4-emscripten)mistyR_1.15.0.tgz(r-4.3-emscripten)
mistyR.pdf |mistyR.html
mistyR/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/saezlab/mistyr/issues

Pkgdown site:https://saezlab.github.io

Datasets:
  • synthetic - Synthetic benchmark data for mistyR

On BioConductor:mistyR-1.15.0(bioc 3.21)mistyR-1.14.0(bioc 3.20)

softwarebiomedicalinformaticscellbiologysystemsbiologyregressiondecisiontreesinglecellspatialbioconductorbiologyintercellularmachine-learningmodularmolecular-biologymultiviewspatial-transcriptomics

7.81 score 51 stars 160 scripts 230 downloads 20 exports 99 dependencies

Last updated 3 months agofrom:89f8c5de3f. Checks:1 OK, 4 NOTE, 2 ERROR. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKDec 29 2024
R-4.5-winNOTEDec 29 2024
R-4.5-linuxNOTEDec 29 2024
R-4.4-winNOTEDec 29 2024
R-4.4-macERRORDec 29 2024
R-4.3-winNOTEDec 29 2024
R-4.3-macERRORDec 29 2024

Exports:%>%add_juxtaviewadd_paraviewadd_viewsclear_cachecollect_resultscreate_initial_viewcreate_viewextract_signaturefilter_viewsplot_contrast_heatmapplot_contrast_resultsplot_improvement_statsplot_interaction_communitiesplot_interaction_heatmapplot_view_contributionsremove_viewsrename_viewrun_mistyselect_markers

Dependencies:assertthatbitbit64caretclassclicliprclockcodetoolscolorspacecpp11crayondata.tabledeldirdiagramdigestdistancesdplyre1071fansifarverfilelockforeachfurrrfuturefuture.applygenericsggplot2globalsgluegowergtablehardhathmsipredisobanditeratorsjsonliteKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmgcvModelMetricsmunsellnlmennetnumDerivparallellypillarpkgconfigplyrprettyunitspROCprodlimprogressprogressrproxypurrrR.methodsS3R.ooR.utilsR6rangerRColorBrewerRcppRcppEigenreadrrecipesreshape2ridgerlangrlistrpartscalesshapeSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitevroomwithrXMLyaml

Modeling spatially resolved omics with mistyR

Rendered frommistyR.Rmdusingknitr::rmarkdownon Dec 29 2024.

Last update: 2022-12-13
Started: 2021-04-20