RxRx1: A Dataset for Evaluating Experimental Batch Correction Methods
This dataset addresses the problem of batch effects confounding biological conclusions for researchers in computational biology and bioinformatics, though it is incremental as it provides a new benchmark rather than a novel correction method.
The authors introduced RxRx1, a dataset of 125,510 high-resolution microscopy images across 51 experimental batches, designed to evaluate batch correction methods in high-throughput biological screening, and they proposed a classification task to assess these methods.
High-throughput screening techniques are commonly used to obtain large quantities of data in many fields of biology. It is well known that artifacts arising from variability in the technical execution of different experimental batches within such screens confound these observations and can lead to invalid biological conclusions. It is therefore necessary to account for these batch effects when analyzing outcomes. In this paper we describe RxRx1, a biological dataset designed specifically for the systematic study of batch effect correction methods. The dataset consists of 125,510 high-resolution fluorescence microscopy images of human cells under 1,138 genetic perturbations in 51 experimental batches across 4 cell types. Visual inspection of the images alone clearly demonstrates significant batch effects. We propose a classification task designed to evaluate the effectiveness of experimental batch correction methods on these images and examine the performance of a number of correction methods on this task. Our goal in releasing RxRx1 is to encourage the development of effective experimental batch correction methods that generalize well to unseen experimental batches. The dataset can be downloaded at https://rxrx.ai.