batchtma R package: Methods to address batch effects

The goal of the batchtma package is to provide functions for batch effect-adjusting biomarker data. It implements different methods that address batch effects while retaining differences between batches that may be due to “true” underlying differences in factors that drive biomarker values.

Installation

batchtma can be installed from CRAN using:

install.packages("batchtma")

To install a potentially newer version from GitHub, use:

# install.packages("remotes") # The "remotes" package needs to be installedremotes::install_github("stopsack/batchtma")

Usage

Load the package:

library(batchtma)

Define example data with batch effects:

df<-data.frame(
tma= rep(1:2, times=30),
biomarker= rep(1:2, times=30) + runif(max=3, n=60)
)

Run the adjust_batch() function to adjust for batch effects:

df_adjust<- adjust_batch(
data=df, markers=biomarker, batch=tma, method=simple
)
plot_batch(
data=df, marker=biomarker, batch=tma, title="Raw data"
)
plot_batch(
data=df_adjust, marker=biomarker_adj2, batch=tma, title="Adjusted data"
)

• The “Get Started” vignette has details on how to use the package.

Methodology

The package implements five different approaches to obtaining batch effect-adjusted biomarker values. The methods differ depending on what distributional property of batch effects they address and how they handle “true” between-batch differences. Such differences can result from confounding when batches include samples with different characteristics that are expected to lead to differences in biomarker levels. They should ideally be retained when performing batch effect adjustments.

#	method =	Approach	Addressed	Retains “true” between-batch differences
1	—	Unadjusted	—	Yes
2	simple	Simple means	Means	No
3	standardize	Standardized batch means	Means	Yes
4	ipw	Inverse-probability weighting	Means	Yes
5	quantreg	Quantile regression	Low and high quantiles	Yes
6	quantnorm	Quantile normalization	All ranks	No

• “Get Started” shows general examples on the different methods in absence and presence of confounding.

Reference

Stopsack KH, Tyekucheva S, Wang M, Gerke TA, Vaselkiv JB, Penney KL, Kantoff PW, Finn SP, Fiorentino M, Loda M, Lotan TL, Parmigiani G*, Mucci LA* (* equal contribution). Extent, impact, and mitigation of batch effects in tumor biomarker studies using tissue microarrays. eLife 2021;10:e71265. doi:10.7554/elife.71265.