MapMyCells
MapMyCells transforms cell types from a concept in publications to a tool for public research. Scientists worldwide can discover what cell types their transcriptomics and spatial data corresponds with by comparing their data to massive, high-quality reference datasets, including the Allen Institute’s whole mouse brain multi-omics atlas, which was developed as part of the NIH’s Brain Initiative Cell Census Network and an atlas of cell types in the cortex of aged humans, which was developed as part of the Seattle Alzheimer’s Disease Brain Cell Atlas (SEA-AD) consortium. Both datasets are also available Allen Brain Cell Atlas.
One key advantage to MapMyCells is scale: using our cloud-based Brain Knowledge Platform and reference datasets with millions of cells, researchers can provide up to 327 million cell-gene pairs from their own data, which is a huge leap forward for working with whole-brain datasets.
MapMyCells will enable the neuroscience community to
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Compare their own data to massive, high-quality, and high-resolution cell type taxonomies.
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Speed up the creation of brain reference atlases by facilitating the integration of datasets from the scientific community with a shared reference.
Data Usage and Privacy
Allen Institute does not use, retain, or aggregate any data uploaded to MapMyCells for its own internal purposes, nor will we publish your data publicly. Allen Institute database administrators can access any uploaded dataset for debugging and other error remediation purposes. All files will be deleted one week after upload. Please do not submit any sensitive data, personally identifiable data, or protected health data that could put an individuals' privacy at risk into MapMyCells. See the Allen Institute Privacy Policy for more information on our privacy practices.
Input Files
Learn how to create MapMyCells input file (h5ad) using Python or R.
Algorithims & Output Files
Learn more about the algorithims options and what the output files means
Explore the data
MapMyCells offers researchers a simple two-step process to map their gene expression data against massive reference taxonomies of brain cell types. Researchers can choose from available mapping algorithms, based on their needs for speed and robustness. Outputs feature a cell identity from the reference taxonomy and corresponding confidence score for each cell in the researcher’s dataset. Learn more about MapMyCells outputs here.
MapMyCells Use case: single cell genomics in mouse
Learn how to use MapMyCells with this tutorial notebook. Get a walkthrough from input file creation to initial output analysis for an example of single cell genomics in mouse.
MapMyCells Use case: single nucleus RNA-seq from human MTG
Learn how to use MapMyCells with this tutorial notebook. Get a walkthrough from input file creation to initial output analysis for an example of single nucleus RNA-seq in human MTG.
Webinar & tutorial
Learn from one of our scientists behind MapMyCells on how to use it, what type of data it accepts, what taxonomies are built of fit, which algorithms to select, and peek under the hood on how it works.
Watch a short tutorial here:
The Brain Knowledge Platform
With funding from the National Institutes of Health (NIH) and technology from Amazon Web Services (AWS), the Allen Institute is leading an effort to build the Brain Knowledge Platform (BKP) – soon to be the largest open-source database of brain cell data in the world. The BKP will host the ABC Atlas including single-cell resolution neuroscience datasets from collaborative teams worldwide. It will be a premier resource to compile and standardize massive datasets on the structure and function of mammalian brains.
The ultimate goal of the BKP is to enable better diagnosis and treatment of the mental and neurological disorders and diseases.
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