Flow Cytometry Gating Standardization via Machine Learning
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Solution Overview
Problem
Current flow cytometry data analysis lacks standardization, leading to inconsistent gating definitions across different assays and laboratories, making data sharing and cross-study analysis difficult due to the use of unstructured text strings and incomplete ontologies.
Innovation Solution
A machine learning-based system that receives inconsistent gating definitions from multiple flow cytometry devices and generates standardized cell types and functional markers by using a training dataset manually curated from various assays, implemented with an automated ML pipeline to preprocess and tokenize data, and map to standardized ontologies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If flow cytometry devices use different gating definitions across multiple assays and laboratories, then the versatility and applicability of flow cytometry assays increase, but data consistency and comparability deteriorate
Solution Approach 1:
The patent introduces an intermediary layer (standardization framework including ontologies and mapping algorithms) that sits between diverse gating definitions and the need for consistent data interpretation. This intermediary translates various gating strategies into a common standardized language, allowing data from different assays and laboratories to be compared while preserving the original assay diversity
Solution Approach 2:
The patent changes the parameter representation by transforming unstructured gating definition text into structured standardized parameters using ontologies. This parameter transformation enables consistent data comparison across different assays while maintaining the ability to handle diverse gating strategies through flexible mapping rules
2Measurement precision
If manual curation of gating definitions is used to ensure accuracy, then measurement precision improves, but the time and resource investment increases
Solution Approach 1:
The patent performs preliminary action by pre-defining standardized ontologies and mapping relationships before actual data analysis. This upfront preparation creates a reusable framework that automatically processes new gating definitions without requiring manual curation for each new assay, significantly reducing processing time while maintaining precision
Solution Approach 2:
The patent uses copying by creating standardized ontology representations that can be replicated and applied across multiple assays and laboratories. Once a gating definition is standardized once, the standardized version can be copied and applied consistently across different datasets, eliminating repeated manual curation efforts
3Ease of manufacture
If unstructured text strings are used for gating definitions, then the ease of implementation improves, but data sharing and cross-study analysis become difficult
Solution Approach 1:
The patent segments unstructured gating definition text into structured components using ontologies. By breaking down the text into discrete standardized elements (cell types, markers, gating criteria), the system maintains the simplicity of text-based input while enabling sophisticated data sharing and cross-study analysis through structured representation
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables the integration of data from diverse assays by automatically determining cell types and functional markers, improving data consistency and reproducibility across different laboratories, with high prediction accuracy and reduced reliance on rule-based methods.
Implementation Method 1
The light, which is scattered or emitted by the cell, is characteristic to the cells and its components. Based on the scattered light the cell type and one or more of functional markers of the cell can be determined.
Implementation Method 2
Flow cytometry is a technique used to detect and measure physical and chemical characteristics of a population of cells, in particular cell type and functional markers... Using fluorescent-labeled molecular probes
Data Source
AI summary
A system for determining a cell type and/or one or more functional markers of a cell using flow cytometry. A plurality of flow cytometry devices respectively perform flow cytometry of cells and use gating definitions at least in part different among each other, thereby generating gating definitions as respective results of the flow cytometry devices, which are at least partly inconsistent such that a same set of biomarkers detected by two different flow cytometry devices results in different gating definitions being output. A machine learning component receives the gating definitions as inputs, and generates a set of cell types and/or functional markers as an output and as a result of the flow cytometry analysis performed by the flow cytometry devices. The machine learning component has been trained using a set of manually curated training data comprising gating definitions resulting from the flow cytometry and corresponding cell types and/or functional markers.