combat 0.2.0

pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods

pyComBat

pyComBat [1] is a Python 3 implementation of ComBat [2], one of the most widely used tool for correcting technical biases, called batch effects, in microarray expression data.

More detailed documentation can be found at this address.

TO DO

• Benchmark with scanpy
• [ ] Add imputation of missing values
• Add unit testing on covariates and model matrix

Minimum dependencies

We list here the versions of the packages that have been used for development/testing of pyComBat, as well as for writing the documentation.

pyComBat dependencies

• python 3.6

• numpy 1.16.4

• mpmath 1.1.0

• pandas 0.24.2

Documentation

• sphinx 2.1.2

Usage example

Installation

You can install pyComBat directly with:

pip install combat

You can upgrade pyComBat to its latest version with:

pip install combat --upgrade

Running pyComBat

The simplest way of using pyComBat is to first import it, and then simply use the pycombat function with default parameters:

from combat.pycombat import pycombat
data_corrected = pycombat(data,batch)

• data: The expression matrix as a dataframe. It contains the information about the gene expression (rows) for each sample (columns).

• batch: List of batch indexes. The batch list describes the batch for each sample. The list of batches contains as many elements as the number of columns in the expression matrix.

How to contribute

Please refer to CONTRIBUTING.md to learn more about the contribution guidelines.

References

[1] Behdenna A, Haziza J, Azencot CA and Nordor A. 2020. pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods. bioRxiv doi: 10.1101/2020.03.17.995431

[2] Johnson,W.E. et al. (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 8, 118–127