easyplater: The easy way to generate microplate designs deconvolved from multivariate clinical data

Abstract

Microplate-based 'omic studies of large clinical cohorts can massively accelerate biomedical research, but experimental power and veracity may be negatively impacted when plate positional effects confound clinical variables of interest. Plate designs must therefore deconvolve this technical and biological variation, and several computational approaches now exist to achieve this. However, even the most advanced of these methods requires too much user intervention to ensure designs adhere to spatial constraints. Here, we aim to significantly reduce researcher-hours spent in plate design with three innovations: First, we propose a weighted, multivariate plate design score that uses a novel metric of spatial autocorrelation to reward designs where similar samples are in distal wells, and which also incorporates penalties for local, variable-wise homogeneous regions; Next, we use a network-based approach to identify clinically similar samples, and then generate an initial plate design randomized under the constraint that similar samples are allocated to distal wells; Lastly, we propose a novel method to quickly search plate-design space for an improvement on the initial design, as measured by the plate design score. We have implemented this method in easyplater, an R package for generating 96-well plate designs which takes sample clinical data and user-assigned clinical variable weights as input, and outputs the most deconvolved plate design it finds in CSV or XLSX formats. Overall, easyplater reduces the need for user intervention in plate design, outperforms currently available methods, and is an important advancement as large, well-phenotyped cohorts become available for high-throughput 'omic studies and numbers of plates and clinical variables increase.