biotmle
Targeted Learning with Moderated Statistics for Biomarker Discovery
Bioconductor version: Release (3.20)
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>
citation("biotmle")):
Installation
To install this package, start R (version "4.4") 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 | ||
| NEWS | Text | |
| LICENSE | Text |
Details
| biocViews | DifferentialExpression, GeneExpression, ImmunoOncology, Microarray, RNASeq, Regression, Sequencing, Software |
| Version | 1.30.0 |
| In Bioconductor since | BioC 3.5 (R-3.4) (7.5 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 |
| System Requirements | |
| URL | https://code.nimahejazi.org/biotmle |
| Bug Reports | https://github.com/nhejazi/biotmle/issues |
See More
| 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 |
Package Archives
Follow Installation instructions to use this package in your R session.
| Source Package | biotmle_1.30.0.tar.gz |
| Windows Binary (x86_64) | biotmle_1.30.0.zip |
| macOS Binary (x86_64) | biotmle_1.30.0.tgz |
| macOS Binary (arm64) | biotmle_1.30.0.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 |