biotmle
Targeted Learning with Moderated Statistics for Biomarker Discovery
Bioconductor version: 3.23 · Package version: 1.36.1
Other Bioconductor versions
devel is the development version; release is the current stable one.
3.24 (devel), 3.23 (release)
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.
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("biotmle")Details
| Maintainer | Nima Hejazi <nh@nimahejazi.org> |
| Author | Nima Hejazi [aut, cre, cph] (ORCID: <https://orcid.org/0000-0002-7127-2789>), Alan Hubbard [aut, ths] (ORCID: <https://orcid.org/0000-0002-3769-0127>), Mark van der Laan [aut, ths] (ORCID: <https://orcid.org/0000-0003-1432-5511>), Weixin Cai [ctb] (ORCID: <https://orcid.org/0000-0003-2680-3066>), Philippe Boileau [ctb] (ORCID: <https://orcid.org/0000-0002-4850-2507>) |
| License | MIT + file LICENSE |
| URL | https://code.nimahejazi.org/biotmle |
| Bug Reports | https://github.com/nhejazi/biotmle/issues |
| Source branch | RELEASE_3_23 |
| Build report | Bioconductor build system, r-universe |
| biocViews | DifferentialExpression, GeneExpression, ImmunoOncology, Microarray, RNASeq, Regression, Sequencing, Software |
| Package Short Url | https://bioconductor.org/packages/biotmle/ |
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.
Documentation
Download
Follow the 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 (arm64) | biotmle_1.36.1.tgz |
| macOS binary (x86_64) | biotmle_1.36.1.tgz |
Dependencies
Depends: R (>= 4.0)
Imports: stats, methods, dplyr, tibble, ggplot2, ggsci, 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)