CytoBatchNorm: An R package with Graphical Interface for Batch Effects Correction of Cytometry Data

Author(s): Samuel Granjeaud, Naoill Abdellaoui, Anne-Sophie Chrétien, Eloise Woitrain, Laurent Pineau, Sandro Ninni, Alexandre Harari, Marion Arnaud, David Montaigne, Bart Staels, David Dombrowicz, Olivier Molendi-Coste.

Innovation in cytometry propelled it to an almost “omic” dimension technique during the last decade. The application fields concomitantly enlarged, resulting in generation of high-dimensional high-content data sets which have to be adequately designed, handled and analyzed. Experimental solutions and detailed data processing pipelines were developed to reduce both the staining conditions variability between samples and the number of tubes to handle. However, an unavoidable variability appears between samples, barcodes, series and instruments (in multicenter studies) contributing to "batch effects" that must be properly controlled. Computer aid to this aim is necessary, and several methods have been published so far, but configuring and carrying out batch normalization remains unintuitive for scientists with a purely biological university education. To address this challenge, we developed an R package called CytoBatchNorm that offers an intuitive and user-friendly graphical interface. Although the processing is based on the script by Schuyler et al., the graphical interface revolutionizes its use. CytoBatchNorm enables users to define a specific correction for each marker in a single run. It provides a visualization that guides you through quickly setting the correction for each marker. It allows corrections to be previewed and inter-marker effects to be checked as the settings are updated. CytoBatchNorm will help the cytometry community to adequately scale data between batches, reliably reducing batch effects and improving subsequent dimension reduction and clustering.