Package: biotmle 1.31.0

Nima Hejazi

biotmle: Targeted Learning with Moderated Statistics for Biomarker Discovery

Tools for differential expression biomarker discovery based on microarray and next-generation sequencing data that leverage efficient semiparametric estimators of the average treatment effect for variable importance analysis. Estimation and inference of the (marginal) average treatment effects of potential biomarkers are computed by targeted minimum loss-based estimation, with joint, stable inference constructed across all biomarkers using a generalization of moderated statistics for use with the estimated efficient influence function. The procedure accommodates the use of ensemble machine learning for the estimation of nuisance functions.

Authors:Nima Hejazi [aut, cre, cph], Alan Hubbard [aut, ths], Mark van der Laan [aut, ths], Weixin Cai [ctb], Philippe Boileau [ctb]

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biotmle.pdf |biotmle.html
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NEWS

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

Bug tracker:https://github.com/nhejazi/biotmle/issues

On BioConductor:biotmle-1.31.0(bioc 3.21)biotmle-1.30.0(bioc 3.20)

regressiongeneexpressiondifferentialexpressionsequencingmicroarrayrnaseqimmunooncologybioconductorbioconductor-packagebioconductor-packagesbioinformaticsbiomarker-discoverybiostatisticscausal-inferencecomputational-biologymachine-learningstatisticstargeted-learning

5.30 score 5 stars 5 scripts 342 downloads 8 exports 100 dependencies

Last updated 4 months agofrom:7b0119e180. Checks:3 OK, 4 NOTE, 1 WARNING. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKJan 28 2025
R-4.5-winNOTEJan 28 2025
R-4.5-macWARNINGJan 28 2025
R-4.5-linuxNOTEJan 28 2025
R-4.4-winNOTEJan 28 2025
R-4.4-macNOTEJan 28 2025
R-4.3-winOKJan 28 2025
R-4.3-macOKDec 29 2024

Exports:.biotmlebiomarkertmleeifheatmap_icmodtest_icrnaseq_ictoptablevolcano_ic

Dependencies:abindaskpassassertthatBHBiobaseBiocGenericsBiocParallelbitopsbootcaToolsclicodetoolscolorspacecpp11crayoncubaturecurlcvAUCdata.tableDelayedArraydigestdplyrdrtmlefansifarverforeachformatRfutile.loggerfutile.optionsfuturefuture.applygamgenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggdendroggplot2ggsciglobalsgluegplotsgtablegtoolshttrIRangesisobanditeratorsjsonliteKernSmoothlabelinglambda.rlatticelifecyclelimmalistenvmagrittrMASSMatrixMatrixGenericsMatrixModelsmatrixStatsmgcvmimemunsellnlmennlsnpopensslparallellypillarpkgconfigplyrquadprogquantregR6RColorBrewerRcpprlangROCRS4ArraysS4VectorsscalessnowSparseArraySparseMstatmodSummarizedExperimentsuperheatSuperLearnersurvivalsystibbletidyselectUCSC.utilsutf8vctrsviridisLitewithrXVector

Identifying Biomarkers from an Exposure Variable with biotmle

Rendered fromexposureBiomarkers.Rmdusingknitr::rmarkdownon Jan 28 2025.

Last update: 2021-10-12
Started: 2017-01-17