| --- |
| 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**](https://arxiv.org/abs/2503.20158) 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), [MAE-L/8](https://arxiv.org/pdf/2404.10242), [MAE-G/8](https://arxiv.org/pdf/2411.02572), 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. |
|
|
| Paper published at LMRL Workshop at ICLR 2025 [RxRx3-core: Benchmarking drug-target interactions in High-Content Microscopy](https://arxiv.org/abs/2503.20158). |
| Benchmarking code for this dataset is provided in the [EFAAR benchmarking repo](https://github.com/recursionpharma/EFAAR_benchmarking/tree/trunk/RxRx3-core_benchmarks) and [Polaris](https://polarishub.io/benchmarks/recursion/rxrx-compound-gene-activity-benchmark). |
|
|
| --- |
| 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) |
| ``` |
| --- |
| Metadata |
|
|
| The metadata can be found in `metadata_rxrx3_core.csv` in this repository. The schema of the metadata is as follows: |
|
|
| | Attribute | Description | |
| |-------------------|-----------------------------------------------------------------------------------------------------------------------| |
| | well_id | Experiment Name - Plate - Well (compound-004_1_AA04 or gene-088_9_C15) | |
| | experiment_name | Experiment Name: Experiment number (compound-004 or gene-088) |
| | plate | Plate number in the experiment (1-48) | | |
| | address | Well location on the plate - "A01" to "AF48". | |
| | gene | Unblinded or anonymized gene name, or a control | |
| | treatment | Compound synonym or gene-name - guide-number (Narlaprevir or <gene_name>_guide_1) |
| | SMILES | Canonical SMILES or blank for non-compounds |
| | concentration | Compound concentration tested (in uM) | |
| | perturbation_type | CRISPR or COMPOUND | |
| | cell_type | HUVEC | | |
| | well_type_label | Indicates experimental control information | | |
|
|
| The `well_type_label` column includes the following values: |
|
|
| | well_type_label | Description | |
| |-------------------|-----------------------------------------------------------------------------------------------------------------------| |
| | Query guides |CRISPR guides that target a query gene | |
| | Exon controls | Exon-targeting CRISPR guides that are used as controls |
| | Intron controls | Intron-targeting CRISPR guides that are used as controls |
| | Query Compounds + Intron control | Query compounds on an intron-targeting CRISPR background |
| | CRISPR Gene Positive Controls | Control genes that are exon-targeting CRISPR guides that are used as controls, there are five genes with 6 guides each that target the exon region of the gene |
| | Control Compounds + Intron control | Control compound on an intron-targeting CRISPR background |
|
|
| To help understand the metadata, we have included some samples to enable parser testing and validation |
|
|
| well_id,experiment_name,plate,address,gene,treatment,SMILES,concentration,perturbation_type,cell_type,well_type_label |
| compound-001_10_AA12,compound-001,10,AA12,,Esomeprazole,"COC1=CC2=C([N-]C(=N2)[S@@](=O)CC2=C(C)C(OC)=C(C)C=N2)C=C1 |r,c:7,13,21,24,t:2,4,18|",2.5,COMPOUND,HUVEC,Control Compounds + Intron control |
| gene-077_3_L32,gene-077,3,L32,CENPC,CENPC_guide_3,,,CRISPR,HUVEC,Query guides |
