Cell Maps for Artificial Intelligence - Data Release

Version 1.2
Clark, Timothy; Schaffer, Leah; Obernier, K; Al Manir, Sadnan; Churas, Christopher; Dailamy, A; Doctor, Y; Forget, A; Hansen, JN; Hu, M; Lenkiewicz, J; Levinson, MA; Marquez, C; Mohan, J; Nourreddine, S; Niestroy, J; Pratt, D; Qian, G; Thaker, S; Belisle-Pipon, J-C; Brandt, C; Chen, J; Ding, Y; Fodeh, S; Krogan, N; Lundberg, E; Mali, P; Payne-Foster, P; Ratcliffe, S; Ravitsky, V; Sali, A; Schulz, W; Ideker, T, 2024, "Cell Maps for Artificial Intelligence - Data Release", https://doi.org/10.18130/V3/DXWOS5, University of Virginia Dataverse, V1
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Dataset Description
This collection is the 0.5 alpha data release of the the Cell Maps for Artificial Intelligence (CM4AI) Functional Genomics Data Generation Project, a component of the U.S. National Institute of Health’s (NIH) Bridge2AI program. CM4AI’s objective is to deliver machine-readable hierarchical maps of cell architecture as AI-Ready data produced from multimodal interrogation of 100 chromatin modifiers and 100 metabolic enzymes involved in cancer, neuropsychiatric, and cardiac disorders in disease-relevant cell lines under perturbed and unperturbed conditions, utilizing state-of-the-art mass spectrometry based proteomics, spatial proteomics / cell imaging, and genetic perturbations using CRISPR. CM4AI input data streams are generated using immunofluorescence (IF) subcellular microscopy for spatial proteomics data; affinity purification mass spectroscopy (AP-MS) and size exclusion mass spectroscopy (SEC-MS) methods for protein-protein interaction (PPI) data; and single-cell CRISPR-Cas perturbation screens by cell type. Input data streams are integrated via the multi-scale integrated cell (MuSIC) software pipeline employing deep learning models and community detection algorithms2, and output cell maps are packaged with provenance graphs and rich metadata as AI-Ready datasets in RO-Crate format using an extended, client-server version of the FAIRSCAPE framework. This data is Copyright (c) 2024 The Regents of the University of California except where otherwise noted. Spatial proteomics raw image data is copyright (c) 2024 The Board of Trustees of the Leland Stanford Junior University. It is licensed for reuse under Creative Commons Attribution ShareAlike NonCommercial 4.0 International License (https://creativecommons.org/licenses/by-nc-sa/4.0/). Attribution is required to the copyright holders and the authors. Any publications referencing this data or derived products. should cite the related article as well as directly citing this data collection.
Subject Medicine, Health and Life Sciences
Related Publication Clark T, Schaffer L, Obernier K, Al Manir S, Churas CP, Dailamy A, Doctor Y, Forget A, Hansen JN, Hu M, Lenkiewicz J, Levinson MA, Marquez C, Mohan J, Nourreddine S, Niestroy J, Pratt D, Qian G, Thaker S, Belisle-Pipon J-C, Brandt C, Chen J, Ding Y, Fodeh S, Krogan N, Lundberg E, Mali P, Payne-Foster P, Ratcliffe S, Ravitsky V, Sali A, Schulz W, Ideker T. Cell Maps for Artificial Intelligence: AI-Ready Maps of Human Cell Architecture from Disease-Relevant Cell Lines. BioRXiv 2024.
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