Chan Zuckerberg Biohub
Amazon Web Services is collaborating with Chan Zuckerberg Biohub to share datasets in cell biology to help scientists study how cells operate, organize, and work as part of systems to understand why disease happens and how to correct it.
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CZ CELLxGENE Discover Census
bioinformaticscell biologylife sciencessingle-cell transcriptomicstranscriptomics
CZ CELLxGENE Discover (cellxgene.cziscience.com) is a free-to-use platform for the exploration, analysis, and retrieval of single-cell data. CZ CELLxGENE Discover hosts the largest aggregation of standardized single-cell data from the major human and mouse tissues, with modalities that include gene expression, chromatin accessibility, DNA methylation, and spatial transcriptomics. This year, CZ CELLxGENE Discover has made available all of its human and mouse RNA single-cell data through Census (https://chanzuckerberg.github.io/cellxgene-census/) – a free-to-use service with an API and data that...
Usage examples
- Querying data using the gget cellxgene module by CZI
- Querying and fetching the single-cell data and cell/gene metadata. by CZI
- Computing on the cell by gene matrix using online (incremental) algorithms by CZI
- Exploring the Census Datasets table by CZI
- Genes measured in each cell (dataset presence matrix) by CZI
OpenCell on AWS
biologycell biologycell imagingcomputer visionfluorescence imagingimaginglife sciencesmachine learningmicroscopy
The OpenCell project is a proteome-scale effort to measure the localization and interactions of human proteins using high-throughput genome engineering to endogenously tag thousands of proteins in the human proteome. This dataset consists of the raw confocal fluorescence microscopy images for all tagged cell lines in the OpenCell library. These images can be interpreted both individually, to determine the localization of particular proteins of interest, and in aggregate, by training machine learning models to classify or quantify subcellular localization patterns.
Usage examples
- OpenCell: proteome-scale endogenous tagging enables the cartography of human cellular organization by Nathan H. Cho, Keith C. Cheveralls, Andreas-David Brunner, Kibeom Kim, André C. Michaelis, Preethi Raghavan, et al.
- cytoself (an unsupervised ML model to quantify localization patterns) by Hirofumi Kobayashi, Keith C. Cheveralls, Manuel D. Leonetti, Loic A. Royer
- OpenCell web portal by OpenCell team
- Self-Supervised Deep-Learning Encodes High-Resolution Features of Protein Subcellular Localization by Hirofumi Kobayashi, Keith C. Cheveralls, Manuel D. Leonetti, Loic A. Royer
CZ Grand Challenges - Imaging MIT Licensed data and models
biodiversitybioinformaticsbiologybiomolecular modelingbrain imagescell biologycell imagingcziimaginglife sciencesmachine learningmicroscopymodelproteinzarr
This dataset contains a diverse range of imaging biological data and models. The data is sourced and curated by a team of experts at CZI and is made available as part of these datasets only when it is not publicly accessible or requires transformations to support model training.
Usage examples
- CELL-Diff: Unified diffusion modeling for protein sequences and microscopy images by Zheng Dihan, Bo Huang
- Quickstart Tutorial for CELL-Diff by CZI
- Documentation for CELL-Diff by CZI
- Documentation for SubCell by CZI
- SubCell: Vision foundation models for microscopy capture single-cell biology by Ankit Gupta, Zoe Wefers, Konstantin Kahnert, Jan N Hansen, William D. Leineweber, Anthony Cesnik, Dan Lu, Ulrika Axelsson, Frederic Ballllosera Navarro, Theofanis Karaletsos, Emma Lundberg
CZ Grand Challenges - Transcriptomic MIT Licensed data and models
biodiversitybiologybiomolecular modelingcell biologyczihdf5life sciencesmachine learningmodelproteintranscriptomics
This dataset contains a transcriptomics biological data and models. The models embed transcriptomic data and facilitate transcriptomic analysis. The data is sourced and curated by a team of experts at CZI and is made available as part of these datasets only when it is not publicly accessible or requires transformations to support model training.
Usage examples
- scGenePT: Is language all you need for modeling single-cell perturbations? by Ana-Maria Istrate, Donghui Li, Theofanis Karaletsos
- Documentation for Transcriptformer by CZI
- A Cross-Species Generative Cell Atlas Across 1.5 Billion Years of Evolution: The TranscriptFormer Single-cell Model by Pearce, J. D., et. al.
- Documentation for scGenePT by CZI
- scGenePT Perturbation Prediction Tutorial by CZI
CryoET Data Portal
cell biologycryo electron tomographyczielectron tomographylife sciencesmachine learningsegmentationstructural biology
Cryo-electron tomography (cryoET) is a powerful technique for visualizing 3D structures of cellular macromolecules at near atomic resolution in their native environment. Observing the inner workings of cells in context enables better understanding about the function of healthy cells and the changes associated with disease. However, the analysis of cryoET data remains a significant bottleneck, particularly the annotation of macromolecules within a set of tomograms, which often requires a laborious and time-consuming process of manual labelling that can take months to complete. Given the current...
Usage examples
Tabula Sapiens
biologyencyclopedicgeneticgenomichealthlife sciencesmedicinesingle-cell transcriptomics
Tabula Sapiens is a benchmark, first-draft human cell atlas of over 1.1M cells from 28 organs of 24 normal human subjects. This work is the product of the Tabula Sapiens Consortium. Taking the organs from the same individual controls for genetic background, age, environment, and epigenetic effects, and allows detailed analysis and comparison of cell types that are shared between tissues. Our work creates a detailed portrait of cell types as well as their distribution and variation in gene expression across tissues and within the endothelial, epithelial, stromal and immune compartments. We...
Usage examples
- Tabula Sapiens reveals transcription factor expression, senescence effects, and sex-specific features in cell types from 28 human organs and tissues by The Tabula Sapiens Consortium, Stephen R Quake
- The Tabula Sapiens: a multiple organ single cell transcriptomic atlas of humans by The Tabula Sapiens Consortium
Tabula Muris
biologyencyclopedicgenomichealthlife sciencesmedicine
Tabula Muris is a compendium of single cell transcriptomic data from the model organism Mus musculus comprising more than 100,000 cells from 20 organs and tissues. These data represent a new resource for cell biology, reveal gene expression in poorly characterized cell populations, and allow for direct and controlled comparison of gene expression in cell types shared between tissues, such as T-lymphocytes and endothelial cells from different anatomical locations. Two distinct technical approaches were used for most organs: one approach, microfluidic droplet-based 3’-end counting, enabled the s...
Usage examples
Tabula Muris Senis
biologyencyclopedicgenomichealthlife sciencesmedicinesingle-cell transcriptomics
Tabula Muris Senis is a comprehensive compendium of single cell transcriptomic data from the model organism Mus musculus comprising more than 500,000 cells from 18 organs and tissues across the mouse lifespan. We discovered cell-specific changes occurring across multiple cell types and organs, as well as age related changes in the cellular composition of different organs. Using single-cell transcriptomic data we were able to assess cell type specific manifestations of different hallmarks of aging, such as senescence, changes in the activity of metabolic pathways, depletion of stem-cell populat...
Usage examples
- Fast queries of scRNAseq datasets with Amazon Athena by Andrew Ang, James Golden, Lisa McFerrin, and Lee Pang
CZ Grand Challenges - Imaging BSD licensed data and models
biodiversitybioinformaticsbiologybiomolecular modelingbrain imagescell biologycell imagingcziimaginglife sciencesmachine learningmicroscopymodelproteinzarr
This dataset contains a diverse range of imaging biological data and models. The data is sourced and curated by a team of experts at CZI and is made available as part of these datasets only when it is not publicly accessible or requires transformations to support model training.
Usage examples
- Quickstart Tutorial for Cytoland by CZI
- Cytoland: robust virtual staining of landmark organelles by Liu, Hirata-Miyasaki, et al.
- Documentation for Cytoland by CZI
CZ Grand Challenges - Model Benchmarking
benchmarkbiologybiomolecular modelingcell biologyczilife sciencesmachine learningmodel
This dataset includes data and models relevant to benchmarking multimodal biological models. The data has been sourced and curated by a team of experts at CZI and is provided as part of these datasets only when it is not publicly available or requires transformation to support effective model benchmarking.
Usage examples
- The molecular evolution of spermatogenesis across mammals by Murat, F., et al.
- Evaluating SubCell and Related Imaging Models by CZI
- Tabula Sapiens reveals transcription factor expression, senescence effects, and sex-specific features in cell types from 28 human organs and tissues by Tabula Sapiens Consortium et al.