license: cc-by-sa-4.0
size_categories:
- 100K<n<1M
task_categories:
- image-classification
tags:
- biology
- drug
- cells
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype:
array3_d:
dtype: uint8
shape:
- 512
- 512
- 6
- name: site_id
dtype: string
- name: well_id
dtype: string
- name: cell_type
dtype: string
- name: experiment
dtype: string
- name: plate
dtype: int32
- name: well
dtype: string
- name: site
dtype: int32
- name: well_type
dtype:
class_label:
names:
'0': treatment
'1': positive_control
'2': negative_control
- name: sirna
dtype: string
- name: sirna_id
dtype: int32
- name: embeddings
sequence: float32
length: 128
splits:
- name: train
num_bytes: 213139738276
num_examples: 81224
- name: test
num_bytes: 116210798412
num_examples: 44286
dataset_size: 329350536688
paperswithcode_id: rxrx1
RxRx1: A Dataset for Evaluating Experimental Batch Correction Methods
Dataset Description
- Homepage: https://www.rxrx.ai/rxrx1
- DOI: https://doi.org/10.48550/arXiv.2301.05768
- Publication Date 2019-06
Description
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.
Citation
@misc{sypetkowski2023rxrx1,
title = {RxRx1: A Dataset for Evaluating Experimental Batch Correction Methods},
author = {Maciej Sypetkowski and Morteza Rezanejad and Saber Saberian and Oren Kraus and John Urbanik and James Taylor and Ben Mabey and Mason Victors and Jason Yosinski and Alborz Rezazadeh Sereshkeh and Imran Haque and Berton Earnshaw},
year = {2023},
eprint = {2301.05768},
archiveprefix = {arXiv},
primaryclass = {cs.CV}
}