Deep learning and alignment of spatially-resolved whole transcriptomes of single cells
AssigneesThe Broad Institute, Inc., Massachusetts Institute of Technology
InventorsAviv Regev, Tommaso Biancalani, Gabriele Scalia, Lorenzo Buffoni
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Abstract
The present invention provides for methods, systems and computer products for aligning single cell data with spatial data to generate spatial maps of cell types and gene expression at single cell resolution. The invention further provides for mapping to common coordinate frameworks.
Claims30
A method of aligning single cell data with spatial data to generate spatial maps of cell types and gene expression at single cell resolution comprising:
- by one or more computing devices:
- receiving single cell data obtained from a specimen from a specific anatomical region or tissue type;
- receiving spatial data obtained from the same specimen or the same anatomical region or tissue type from a different specimen,
- wherein the single cell data and spatial data comprises at least one shared measured features;
- aligning the single cell data to the spatial data based on one or more of the shared features using unsupervised deep learning nonlinear optimization;
- and
- generating a spatial map wherein the single cell data constitutes the new spatial data.
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A system to align single cell data with spatial data to generate spatial maps of cell types at single cell resolution comprising:
- a storage device;
- and
- a processor communicatively coupled to the storage device, wherein the processor executes application code instructions that are stored in the storage device to cause the system to:
- receive single cell data obtained from a specimen from a specific anatomical region or tissue type;
- receive spatial data obtained from the same specimen or the same anatomical region or tissue type from a different specimen,
- wherein the single cell data and spatial data comprises at least one shared feature;
- align the single cell data to the spatial data based on one or more of the shared features using unsupervised deep learning non linear optimization;
- and
- generate a spatial map wherein the single cell data constitutes the new spatial data.
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A computer program product to align single cell data with spatial data to generate spatial maps of cell types at single cell resolution, comprising:
- a non-transitory computer-readable medium having computer-readable program instructions embodied thereon that, when executed by a computer, cause the computer to:
- receive single cell data obtained from a specimen from a specific anatomical region or tissue type;
- receive spatial data obtained from the same specimen or the same anatomical region or tissue type from a different specimen,
- wherein the single cell data and spatial data comprises at least one shared feature;
- align the single cell data to the spatial data based on one or more of the shared features using unsupervised deep learning non linear optimization;
- and
- generate a spatial map wherein the single cell data constitutes the new spatial data.
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Dependencies are inferred from the claim text.