|
--- |
|
dataset_info: |
|
features: |
|
- name: __key__ |
|
dtype: string |
|
- name: jp2 |
|
dtype: image |
|
splits: |
|
- name: train |
|
num_bytes: 17489993120.108 |
|
num_examples: 1335606 |
|
download_size: 17390577507 |
|
dataset_size: 17489993120.108 |
|
configs: |
|
- config_name: default |
|
data_files: |
|
- split: train |
|
path: data/train-* |
|
--- |
|
|
|
To accompany OpenPhenom, Recursion is releasing the **RxRx3-core** dataset, a challenge dataset in phenomics optimized for the research community. |
|
RxRx3-core includes labeled images of 735 genetic knockouts and 1,674 small-molecule perturbations drawn from the [RxRx3 dataset](https://www.rxrx.ai/rxrx3), |
|
image embeddings computed with [OpenPhenom](https://huggingface.co/recursionpharma/OpenPhenom), and associations between the included small molecules and genes. |
|
The dataset contains 6-channel Cell Painting images and associated embeddings from 222,601 wells but is less than 18Gb, making it incredibly accessible to the research community. |
|
|
|
Mapping the mechanisms by which drugs exert their actions is an important challenge in advancing the use of high-dimensional biological data like phenomics. |
|
We are excited to release the first dataset of this scale probing concentration-response along with a benchmark and model to enable the research community to |
|
rapidly advance this space. |
|
|
|
Loading the RxRx3-core image dataset |
|
``` |
|
from datasets import load_dataset |
|
rxrx3_core = load_dataset("recursionpharma/rxrx3-core") |
|
``` |
|
Loading OpenPhenom embeddings and metadata for RxRx3-core |
|
``` |
|
from huggingface_hub import hf_hub_download |
|
import pandas as pd |
|
|
|
file_path_metadata = hf_hub_download("recursionpharma/rxrx3-core", filename="metadata_rxrx3_core.csv",repo_type="dataset") |
|
file_path_embs = hf_hub_download("recursionpharma/rxrx3-core", filename="OpenPhenom_rxrx3_core_embeddings.parquet",repo_type="dataset") |
|
|
|
open_phenom_embeddings = pd.read_parquet(file_path_embs) |
|
rxrx3_core_metadata = pd.read_csv(file_path_metadata) |
|
``` |
|
Benchmarking code for this dataset is provided in the [EFAAR benchmarking repo](https://github.com/recursionpharma/EFAAR_benchmarking/tree/trunk/RxRx3-core_benchmarks). |
|
|
