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.