Dataset Homogenization via Eigenvector Adaptation Factors
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Solution Overview
Problem
Current methods for transferring molecular predictors across laboratories face challenges due to batch-specific biases and data privacy concerns, requiring access to sample-level data, which is often restricted by regulations like GDPR, and existing batch correction methods are not suitable for transferring classifiers trained on gene expression datasets.
Innovation Solution
A system and method that utilize eigenvectors to adapt the dataset-specific nature of one dataset to another, allowing for the transfer of molecular predictors without sharing sample-level data, by generating adaptation factors that align the datasets and correct for biases, domain shifts, and other dataset-specific phenomena, while maintaining patient data privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If batch correction methods are used to harmonize datasets, then data homogeneity is improved, but sample-level data access is required which violates privacy regulations
Solution Approach 1:
The patent extracts only the necessary statistical information (means, variances, and transformation parameters) from the source dataset to create adaptation factors, while leaving the actual sample-level data private. This allows batch correction to be applied without transferring or accessing sensitive patient information, resolving the contradiction between achieving data homogeneity and maintaining privacy compliance.
Solution Approach 2:
The patent introduces adaptation factors as an intermediary mechanism that mediates between source and target datasets. These factors capture the transformation needed to align datasets without requiring direct access to sample-level data, enabling privacy-preserving batch correction by acting as a bridge that transmits only essential statistical characteristics.
2Measurement precision
If full sample-level data is accessed for integration or correction, then correction accuracy is improved, but data transfer is restricted by ownership and regulatory concerns
Solution Approach 1:
The patent extracts only the essential statistical parameters (means, variances, and transformation factors) from the source dataset to create adaptation factors. This extraction allows correction accuracy to be maintained by preserving the necessary transformation information while eliminating the need to transfer sensitive sample-level data, thus resolving the contradiction between precision and data transfer capability.
Solution Approach 2:
The patent segments the data correction process into two independent parts: (1) computing adaptation factors from source data locally, and (2) applying these factors to target data. This segmentation allows each party to work with their own private data while achieving accurate correction through the shared transformation parameters, eliminating the need for sample-level data transfer.
3Stability of the object's composition
If existing batch integration methods are used, then data integration is achieved, but they do not output expression profiles suitable for classifier transfer
Solution Approach 1:
The patent changes the parameters of data transformation by using adaptation factors that specifically adjust means and variances to match target distribution characteristics. This parameter-based approach preserves expression profile information while achieving data integration, making the output suitable for classifier transfer unlike black-box integration methods.
Solution Approach 2:
The patent applies local quality adjustment by correcting each gene's expression profile individually using gene-specific adaptation factors. This preserves the biological meaning and expression profiles of individual genes while achieving overall data integration, making the corrected data suitable for classifier transfer.
Data Source
AI summary
A method for transferring a dataset-specific nature of a first dataset with sequencing results for a first plurality of specimen to a second dataset with sequencing results for a second plurality of specimen includes receiving a first set of adaptation factors of the first dataset that include two or more eigenvectors, where the sequencing cannot be reconstructed from the first set of adaptation factors without access to the first dataset. The method also includes generating a second set of adaptation factors of the second dataset that include two or more eigenvectors of the second dataset. The method also includes generating an adapted second dataset by adapting the dataset-specific nature of the second dataset to the dataset-specific nature of the second dataset based at least in part on the first and second sets of adaptation factors, and providing the adapted second dataset to the first entity.