DOI: 10.18129/B9.bioc.biotmle  

This package is for version 3.16 of Bioconductor; for the stable, up-to-date release version, see biotmle.

Targeted Learning with Moderated Statistics for Biomarker Discovery

Bioconductor version: 3.16

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.

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

Maintainer: Nima Hejazi <nh at>

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biocViews DifferentialExpression, GeneExpression, ImmunoOncology, Microarray, RNASeq, Regression, Sequencing, Software
Version 1.22.0
In Bioconductor since BioC 3.5 (R-3.4) (6 years)
License MIT + file LICENSE
Depends R (>= 4.0)
Imports stats, methods, dplyr, tibble, ggplot2, ggsci, superheat, assertthat, drtmle (>= 1.0.4), S4Vectors, BiocGenerics, BiocParallel, SummarizedExperiment, limma
Suggests testthat, knitr, rmarkdown, BiocStyle, arm, earth, ranger, SuperLearner, Matrix, DBI, biotmleData(>= 1.1.1)
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