biotmle

This is the released version of biotmle; for the devel version, see biotmle.

All versions

3.24 (devel), 3.23 (release), 3.22, 3.21, 3.20, 3.19, 3.18, 3.17, 3.16, 3.15, 3.14, 3.13, 3.12, 3.11, 3.10, 3.9, 3.8, 3.7, 3.6, 3.5

Targeted Learning with Moderated Statistics for Biomarker Discovery

Bioconductor version: Release (3.23)

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] ORCID: 0000-0002-7127-2789 , Alan Hubbard [aut, ths] ORCID: 0000-0002-3769-0127 , Mark van der Laan [aut, ths] ORCID: 0000-0003-1432-5511 , Weixin Cai [ctb] ORCID: 0000-0003-2680-3066 , Philippe Boileau [ctb] ORCID: 0000-0002-4850-2507

Maintainer: Nima Hejazi <nh at nimahejazi.org>

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

Nima Hejazi, Alan Hubbard, Mark van der Laan. biotmle: Targeted Learning with Moderated Statistics for Biomarker Discovery. doi:10.18129/B9.bioc.biotmle, R package version 1.36.1, https://bioconductor.org/packages/biotmle.

Generated from the package metadata; it may differ from the package's own citation.

Installation

To install this package, start R (version "4.6") 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")

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

Need some help? Ask on the Bioconductor Support site!

Details

biocViews	DifferentialExpression, GeneExpression, ImmunoOncology, Microarray, RNASeq, Regression, Sequencing, Software
Version	1.36.1
In Bioconductor since	BioC 3.5 (R-3.4) (9.5 years)
License	MIT + file LICENSE
Depends	R (>= 4.0)
Imports	stats, methods, dplyr, tibble, ggplot2, ggsci, assertthat, drtmle (>= 1.0.4), S4Vectors, BiocGenerics, BiocParallel, SummarizedExperiment, limma
System Requirements
URL	https://code.nimahejazi.org/biotmle
Bug Reports	https://github.com/nhejazi/biotmle/issues

Suggests	testthat, knitr, rmarkdown, BiocStyle, arm, earth, ranger, SuperLearner, Matrix, DBI, biotmleData (>= 1.1.1)
Linking To
Enhances
Depends On Me
Imports Me
Suggests Me
Links To Me
Build Report	Build Report, r-universe

Package Archives

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

Source Package	biotmle_1.36.1.tar.gz
Windows Binary (x86_64)	biotmle_1.36.1.zip
macOS Binary (big-sur-x86_64)	biotmle_1.36.1.tgz
macOS Binary (sonoma-arm64)	biotmle_1.36.1.tgz
Source Repository	git clone https://git.bioconductor.org/packages/biotmle
Source Repository (Developer Access)	git clone git@git.bioconductor.org:packages/biotmle
Package Short Url	https://bioconductor.org/packages/biotmle/
Package Downloads Report	Download Stats
Old Source Packages for BioC 3.23	Source Archive