  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.
