title: 'biotmle: Targeted Learning for Biomarker Discovery'
tags:
- targeted learning
- variable importance
- causal inference
- bioinformatics
- genomics
- R
authors:
- name: Nima S. Hejazi
orcid: 0000-0002-7127-2789
affiliation: 1
- name: Weixin Cai
orcid: 0000-0003-2680-3066
affiliation: 1
- name: Alan E. Hubbard
orcid: 0000-0002-3769-0127
affiliation: 1
affiliations:
- name: Division of Biostatistics, University of California, Berkeley
index: 1
date: 26 July 2017
bibliography: paper.bib

Summary

The biotmle package provides an implementation of a biomarker discovery
methodology based on targeted minimum loss-based estimation (TMLE)
[@vdl2011targeted] and a generalization of the moderated t-statistic of
[@smyth2004linear], designed for use with biological sequencing data (e.g.,
microarrays, RNA-seq). The statistical approach made available in this package
relies on the use of TMLE to rigorously evaluate the association between a set
of potential biomarkers and another variable of interest while adjusting for
potential confounding from another set of user-specified covariates. The
implementation is in the form of a package for the R language for statistical
computing [@R].

There are two principal ways in which the biomarker discovery techniques in
the biotmle R package can be used: to evaluate the association between (1) a
phenotypic measure (say, environmental exposure) and a biomarker of interest,
and (2) an outcome of interest (e.g., survival status at a given time) and a
biomarker measurement, both while controlling for background covariates (e.g.,
BMI, age). By using an estimation procedure based on TMLE, the package produces
results based on the Average Treatment Effect (ATE), a statistical parameter
with a well-studied causal interpretation (see @vdl2011targeted for extended
discussions), making the biotmle R package well-suited for applications in
bioinformatics, epidemiology, and genomics.

After adjusting our data set to be consistent with the expect input format --
please consult the vignette accompanying the R package for details -- we would
call the principal function of this R package: biomarkertmle.

We would perform a moderated test on the output of the biomarkertmle function
using the function modtest_ic.

While the principal table of results produced by this R package matches those
produced by the well-known limma R package [@smyth2005limma], there are also
several plot methods made available for the bioTMLE S4 class -- subclassed
from the popular SummarizedExperiment class -- introduced by this package
[@huber2015orchestrating]. For illustrative purposes, we demonstrate the ouput
of two such functions on anonymized experimental data below:

\newpage

References