Synthetic Spatial Omics Imputation From Histopathology Images
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Solution Overview
Problem
Spatial omics data is expensive and time-consuming to obtain, limiting its widespread availability and use in clinical and discovery settings.
Innovation Solution
An end-to-end computational pipeline using machine learning techniques, specifically a generative adversarial network (GAN) model, to impute spatially resolved analyte concentration from histopathology images, enabling the generation of synthetic spatial omics data from cheaper and more accessible histology images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spatial omics assays are performed to obtain spatially resolved molecular data, then measurement precision and spatial context are improved, but cost and time consumption increase significantly
Solution Approach 1:
The patent creates synthetic spatial omics data by training a machine learning model on paired histopathology and spatial omics images. The model learns to generate spatial omics data that mimics real spatial omics images, allowing researchers to obtain spatially resolved molecular information without performing expensive and time-consuming spatial omics assays on every sample
Solution Approach 2:
The patent performs preliminary action by training the machine learning model on a subset of samples with both histopathology and spatial omics data available. Once trained, the model can rapidly generate synthetic spatial omics data for larger cohorts using only histopathology images, eliminating the need to perform spatial omics assays on all samples
2Measurement precision
If spatial omics assays are performed to obtain spatially resolved molecular data, then measurement precision and spatial context are improved, but cost increases significantly
Solution Approach 1:
The patent creates synthetic spatial omics data by training a machine learning model on paired histopathology and spatial omics images. The model learns to generate spatial omics data that mimics real spatial omics images, allowing researchers to obtain spatially resolved molecular information without performing expensive and time-consuming spatial omics assays on every sample
Solution Approach 2:
The patent replaces expensive spatial omics assays with cheaper histopathology imaging followed by computational prediction. Histopathology images are inexpensive to acquire and can be processed rapidly through the trained machine learning model to generate synthetic spatial omics data at a fraction of the cost of actual spatial omics assays
3Measurement precision
If machine learning model is trained on registered image pairs, then imputation accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent segments the computational pipeline into distinct modules: image registration module that aligns histopathology and spatial omics images using landmark matching, data preprocessing module that prepares training data, model training module that trains the machine learning architecture, and inference module that generates synthetic spatial omics data. This modular approach manages complexity while maintaining high imputation accuracy
Data Source
AI summary
The present disclosure relates generally to machine learning techniques, and more specifically to machine learning techniques for generating synthetic spatial omics data based on histopathology image data. An exemplary system for generating synthetic spatial omics images comprises: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for: receiving a histopathology image depicting a diseased region of interest of an input tissue sample; and generating a synthetic spatial omics image depicting one or more stained structures of interest within the diseased region of interest by inputting the histopathology image into a generator of a trained generative adversarial network (GAN) model.