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. Usage import picklewith open('data.pkl', 'rb') as f: data = pickle.load(f) X = data['X']y = data['y'] Data Reference For raw datasets from 13 HCS database, data and analysis pipeline for dataset 1 was obtained from https://www.science.org/doi/suppl/10.1126/science.1100709/suppl_file/perlman.som.zip; for datasets 2-3, data were shared by authors; For datasets 4-5, analysis code was downloaded from https://static-content.springer.com/esm/art%3A10.1038%2Fnbt.3419/MediaObjects/41587_2016_BFnbt3419_MOESM21_ESM.zip and data were shared by authors; For datasets 6-7, processed dataset was downloaded from AWS following instructions from https://github.com/carpenter-singh-lab/2022_Haghighi_NatureMethods, and replicate_level_cp_normalized.csv.gz features were used. For project datasets 8-13, datasets and analysis results were downloaded from https://zenodo.org/records/7352487. For RxRx3, dataset was obtained from https://www.rxrx.ai/rxrx3. L1000 transcript datasets were downloaded using the same link as datasets 6-7 and the processed transcript data files (named “replicate_level_l1k.csv”) were used.