DOI: 10.18129/B9.bioc.biotmle    

This package is for version 3.10 of Bioconductor; for the stable, up-to-date release version, see biotmle.

Targeted Learning with Moderated Statistics for Biomarker Discovery

Bioconductor version: 3.10

This package facilitates the discovery of biomarkers from biological sequencing data (e.g., microarrays, RNA-seq) based on the associations of potential biomarkers with exposure variables by implementing an inferential procedure that combines a generalization of moderated statistics with targeted minimum loss estimates of the average treatment effect whose estimator admits an asymptotically linear representations (in terms of an efficient influence function).

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

Maintainer: Nima Hejazi <nh at>

Citation (from within R, enter citation("biotmle")):


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HTML R Script Identifying Biomarkers from an Exposure Variable
HTML R Script Processing and Analyzing RNA-Seq Data
PDF   Reference Manual
Text   NEWS


biocViews DifferentialExpression, GeneExpression, ImmunoOncology, Microarray, RNASeq, Sequencing, Software
Version 1.10.0
In Bioconductor since BioC 3.5 (R-3.4) (3 years)
License file LICENSE
Depends R (>= 3.4)
Imports stats, methods, dplyr, tibble, ggplot2, ggsci, superheat, assertthat, future, doFuture, tmle (>=, S4Vectors, BiocGenerics, BiocParallel, SummarizedExperiment, limma
Suggests testthat, knitr, rmarkdown, BiocStyle, earth, glmnet, randomForest, SuperLearner, Matrix, DBI, biotmleData(>= 1.1.1)
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