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# R/`biotmle`
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> Targeted Learning with Moderated Statistics for Biomarker Discovery
**Author:** [Nima Hejazi](https://nimahejazi.org)
-----
## What’s `biotmle`?
`biotmle` is an R package that facilitates biomarker discovery by
generalizing the moderated t-statistic (Smyth 2004) for use with target
parameters that have asymptotically linear representations (van der Laan
and Rose 2011). The set of methods implemented in this R package rely on
the use of targeted minimum loss-based estimates (TMLE) to transform
biological sequencing data (e.g., microarray, RNA-seq) based on the
influence curve representation of a particular causal target parameter
(e.g., average treatment effect). The transformed data (rotated into
influence curve space) may then be subjected to a moderated test for
differences between the statistical estimate of the target parameter and
a hypothesized value of said parameter (usually a null value defined in
relation to the parameter itself). Such an approach provides a valid
statistical hypothesis test of a statistically estimable causal
parameter while controlling the variance such that the error rate (of
the test) is more strongly controlled relative to testing procedures
that do not moderate the variance estimate (Hejazi et al., n.d.).
-----
## Installation
For standard use, install from
[Bioconductor](https://bioconductor.org/packages/biotmle) using
[`BiocManager`](https://CRAN.R-project.org/package=BiocManager):
``` r
if (!("BiocManager" %in% installed.packages())) {
install.packages("BiocManager")
}
BiocManager::install("biotmle")
```
To contribute, install the bleeding-edge *development version* from
GitHub via
[`devtools`](https://www.rstudio.com/products/rpackages/devtools/):
``` r
devtools::install_github("nhejazi/biotmle")
```
Current and prior [Bioconductor](https://bioconductor.org) releases are
available under branches with numbers prefixed by “RELEASE\_”. For
example, to install the version of this package available via
Bioconductor 3.6, use
``` r
devtools::install_github("nhejazi/biotmle", ref = "RELEASE_3_6")
```
-----
## Example
For details on how to best use the `biotmle` R package, please consult
the most recent [package
vignette](https://bioconductor.org/packages/release/bioc/vignettes/biotmle/inst/doc/exposureBiomarkers.html)
available through the [Bioconductor
project](https://bioconductor.org/packages/biotmle).
-----
## Issues
If you encounter any bugs or have any specific feature requests, please
[file an issue](https://github.com/nhejazi/biotmle/issues).
-----
## Contributions
Contributions are very welcome. Interested contributors should consult
our [contribution
guidelines](https://github.com/nhejazi/biotmle/blob/master/CONTRIBUTING.md)
prior to submitting a pull request.
-----
## Citation
After using the `biotmle` R package, please cite it:
```
@article{hejazi2017biotmle,
author = {Hejazi, Nima S and Cai, Weixin and Hubbard, Alan E},
title = {biotmle: Targeted Learning for Biomarker Discovery},
journal = {The Journal of Open Source Software},
volume = {2},
number = {15},
month = {July},
year = {2017},
publisher = {The Open Journal},
doi = {10.21105/joss.00295},
url = {https://doi.org/10.21105/joss.00295}
}
```
-----
## Related
- [R/`biotmleData`](https://github.com/nhejazi/biotmleData) - R
package with example experimental data for use with this analysis
package.
-----
## Funding
The development of this software was supported in part through grants
from the National Institutes of Health: [P42
ES004705-29](https://projectreporter.nih.gov/project_info_details.cfm?aid=9260357&map=y)
and [R01
ES021369-05](https://projectreporter.nih.gov/project_info_description.cfm?aid=9210551&icde=37849782&ddparam=&ddvalue=&ddsub=&cr=1&csb=default&cs=ASC&pball=).
-----
## License
© 2016-2018 [Nima S. Hejazi](https://nimahejazi.org)
The contents of this repository are distributed under the MIT license.
See file `LICENSE` for details.
-----
## References
<div id="refs" class="references">
<div id="ref-hejazi2018variance">
Hejazi, Nima S, Sara Kherad-Pajouh, Mark J van der Laan, and Alan E
Hubbard. n.d. “Variance Stabilization of Targeted Estimators of Causal
Parameters in High-Dimensional Settings.”
<https://arxiv.org/abs/1710.05451>.
</div>
<div id="ref-smyth2004linear">
Smyth, Gordon K. 2004. “Linear Models and Empirical Bayes Methods for
Assessing Differential Expression in Microarray Experiments.”
*Statistical Applications in Genetics and Molecular Biology* 3 (1).
Walter de Gruyter: 1–25. <https://doi.org/10.2202/1544-6115.1027>.
</div>
<div id="ref-vdl2011targeted">
van der Laan, Mark J., and Sherri Rose. 2011. *Targeted Learning: Causal
Inference for Observational and Experimental Data*. Springer Science &
Business Media.
</div>
</div>