Transitive prediction of small molecule function through alignment of high-content screening resources

# Datasets used in Transitive prediction of small molecule function through alignment of high-content screening resources

This dataset supports the development of CLIPⁿ, a contrastive-learning framework designed to align heterogeneous high-content screening (HCS) profile datasets.

***GitHub link:*** https://github.com/AltschulerWu-Lab/CLIPn

## Directory Structure

### Data Files
- **HCS_datasets.pkl**: Contains 13 high-content screening (HCS) datasets from multiple studies across 20 years.
- **Hypoxia.pkl**: Contains 8 profile datasets using different assays and treated under diverse hypoxia durations.
- **Expression.pkl**: Contains 2 transcriptional profile datasets and 6 image profile datasets for multimodal analysis.

### Folders
#### raw_profiles
##### HCS13/
- Contains raw data from 13 high-content screening (HCS) datasets. Each dataset includes meta and feature files.

##### L1000/
- **CDRP_feature_exp.csv**: Raw L1000 expression data from the CDRP dataset.
- **CDRP_meta_exp.csv**: Metadata associated with the CDRP expression data.
- **LINCS_feature_exp.csv**: Raw L1000 expression data from the LINCS dataset.
- **LINCS_meta_exp.csv**: Metadata associated with the LINCS expression data.

##### RxRx3/
- **RxRx3_feature_final.csv**: Profile data from the RxRx3 dataset.
- **RxRx3_meta_final.csv**: Metadata from the RxRx3 dataset.

##### Uncharacterized_compounds/
- **NCI_cpnData.csv**: Feature data for uncharacterized compounds from the NCI dataset.
- **NCI_cpnInfo.csv**: Information about uncharacterized compounds in the NCI dataset.
- **Prestwick_UTSW_cpnData.csv**: Feature data for uncharacterized compounds from the Prestwick UTSW dataset.
- **Prestwick_UTSW_cpnInfo.csv**: Information about uncharacterized compounds from the Prestwick UTSW dataset.