Package: pathMED 1.5.2

Jordi Martorell-Marugán

pathMED: Scoring Personalized Molecular Portraits

PathMED is a collection of tools to facilitate precision medicine studies with omics data (e.g. transcriptomics). Among its funcionalities, genesets scores for individual samples may be calculated with several methods. These scores may be used to train machine learning models and to predict clinical features on new data. For this, several machine learning methods are evaluated in order to select the best method based on internal validation and to tune the hyperparameters. Performance metrics and a ready-to-use model to predict the outcomes for new patients are returned.

Authors:Jordi Martorell-Marugán [cre, aut], Daniel Toro-Domínguez [aut], Raúl López-Domínguez [aut], Iván Ellson [aut]

pathMED_1.5.2.tar.gz
pathMED_1.5.2.zip(r-4.7)pathMED_1.5.2.zip(r-4.6)pathMED_1.5.2.zip(r-4.5)
pathMED_1.5.2.tgz(r-4.6-any)pathMED_1.5.2.tgz(r-4.5-any)
pathMED_1.5.2.tar.gz(r-4.7-any)pathMED_1.5.2.tar.gz(r-4.6-any)
pathMED_1.5.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
pathMED/json (API)

# Install 'pathMED' in R:

install.packages('pathMED', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jordimartorell/pathmed/issues

Datasets:

genesetsData - Preloaded gene sets
pathMEDExampleData - Example of test gene expression data
pathMEDExampleMetadata - Metadata of test gene expression data
refData - Example of reference gene expression datasets

On BioConductor:pathMED-1.5.0(bioc 3.24)pathMED-1.4.0(bioc 3.23)

pathways classification featureextraction transcriptomics

5.15 score 5 stars 1 scripts 179 downloads 10 exports 234 dependencies

Last updated from:5b61242ff7. Checks:1 WARNING, 7 OK, 2 ERROR. Indexed: yes.

Target	Result	Time
bioc-checks	WARNING	232
linux-devel-x86_64	OK	470
source / vignettes	OK	366
linux-release-x86_64	OK	405
macos-release-arm64	ERROR	304
macos-oldrel-arm64	ERROR	282
windows-devel	OK	306
windows-release	OK	314
windows-oldrel	OK	299
wasm-release	OK	216

Exports:ann2term buildRefObject dissectDB getScores methodsML mScores_createReference mScores_filterPaths mScores_imputeFromReference predictExternal trainModel

Dependencies:abind annotate AnnotationDbi askpass assorthead backports base64enc beachmat BH Biobase BiocFileCache BiocGenerics biocmake BiocParallel BiocSingular Biostrings bit bit64 blob boot broom bslib cachem car carData caret caretEnsemble class cli clock cluster codetools colorspace corrplot cowplot cpp11 crayon crosstalk curl data.table DBI dbplyr decoupleR DelayedArray DelayedMatrixStats dendextend Deriv diagram digest dir.expiry doBy dplyr DT e1071 edgeR ellipse emmeans energy estimability evaluate factoextra FactoMineR farver fastmap filelock flashClust fontawesome foreach forecast formatR Formula fracdiff fs futile.logger futile.options future future.apply generics GenomicRanges ggplot2 ggpp ggpubr ggrepel ggsci ggsignif globals glue gower graph gridExtra GSEABase gsl GSVA gtable h5mread hardhat HDF5Array highr htmltools htmlwidgets httr httr2 ipred IRanges irlba isoband iterators jquerylib jsonlite KEGGREST KernSmooth knitr labeling lambda.r later lattice lava lazyeval leaps lifecycle limma listenv lme4 lmtest locfit lubridate magick magrittr MASS Matrix MatrixGenerics MatrixModels matrixStats memoise memuse metrica mgcv microbenchmark mime minerva minqa ModelMetrics modelr multcompView mvtnorm nlme nloptr nnet numDeriv openssl otel parallelly patchwork pbapply pbkrtest pillar pkgconfig plotly plyr png polynom pROC prodlim progressr promises proxy purrr quantreg R6 rappdirs rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen Rdpack recipes reformulas reshape reshape2 rhdf5 rhdf5filters Rhdf5lib rjson rlang rmarkdown rpart RSQLite rstatix rsvd S4Arrays S4Vectors S7 sass ScaledMatrix scales scatterplot3d Seqinfo shape SingleCellExperiment singscore snow SparseArray SparseM sparseMatrixStats sparsevctrs SpatialExperiment SQUAREM statmod stringi stringr SummarizedExperiment survival sys tibble tidyr tidyselect timechange timeDate tinytex tzdb urca utf8 vctrs viridis viridisLite withr xfun XML xtable xts XVector yaml zoo

A comprehensive framework for precision medicine with omics data

Jordi Martorell-Marugán, Iván Ellson, Raúl López-Domínguez, Daniel Toro-Domínguez, Pedro Carmona-Sáez

Rendered frompathMED.Rmdusingknitr::rmarkdownon Jun 05 2026.

Last update: 2026-06-05
Started: 2022-11-07

Help page	Topics
Annotate the pathways from a scores matrix	ann2term
Create a reference data object for input to the pathMED functions	buildRefObject
Split pathways into coexpressed subpathways	dissectDB
Preloaded gene sets	genesetsData
Calculate pathways scores for a dataset	getScores
Prepare the models parameter for the trainModel function	methodsML
Create a reference dataset based on M-scores	mScores_createReference
Filter pathways from the reference M-scores dataset	mScores_filterPaths
Estimate M-scores for a dataset without healthy controls	mScores_imputeFromReference
Example of test gene expression data	pathMEDExampleData
Metadata of test gene expression data	pathMEDExampleMetadata
Predict conditions in external datasets	predictExternal
Example of reference gene expression datasets	refData
Train ML models and perform internal validation	trainModel

Package: pathMED 1.5.2

pathMED: Scoring Personalized Molecular Portraits

A comprehensive framework for precision medicine with omics data

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Help Manual

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