biotmle

DOI: 10.18129/B9.bioc.biotmle  

This package is for version 3.17 of Bioconductor; for the stable, up-to-date release version, see biotmle.

Targeted Learning with Moderated Statistics for Biomarker Discovery

Bioconductor version: 3.17

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 nimahejazi.org>

Installation

To install this package, start R (version "4.3") and enter:

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("biotmle")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("biotmle")

 

HTML	R Script	Identifying Biomarkers from an Exposure Variable
PDF		Reference Manual
Text		NEWS
Text		LICENSE

Details

biocViews	DifferentialExpression, GeneExpression, ImmunoOncology, Microarray, RNASeq, Regression, Sequencing, Software
Version	1.24.0
In Bioconductor since	BioC 3.5 (R-3.4) (6.5 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
LinkingTo
Suggests	testthat, knitr, rmarkdown, BiocStyle, arm, earth, ranger, SuperLearner, Matrix, DBI, biotmleData(>= 1.1.1)
SystemRequirements
Enhances
URL	https://code.nimahejazi.org/biotmle
BugReports	https://github.com/nhejazi/biotmle/issues
Depends On Me
Imports Me
Suggests Me
Links To Me
Build Report

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package	biotmle_1.24.0.tar.gz
Windows Binary	biotmle_1.24.0.zip
macOS Binary (x86_64)	biotmle_1.24.0.tgz
macOS Binary (arm64)	biotmle_1.24.0.tgz
Source Repository	git clone https://git.bioconductor.org/packages/biotmle
Source Repository (Developer Access)	git clone git@git.bioconductor.org:packages/biotmle
Bioc Package Browser	https://code.bioconductor.org/browse/biotmle/
Package Short Url	https://bioconductor.org/packages/biotmle/
Package Downloads Report	Download Stats