Flow Cytometry Data Compression Using Deterministic Neural Mapping
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Solution Overview
Problem
Existing flow cytometers and fluorescence microscopes face challenges in rapidly and accurately performing dimension compression on multi-dimensional data, leading to difficulties in analyzing and comparing large datasets.
Innovation Solution
Utilizing a neural network-based learning model to generate dimension-compressed data without stochastic processes, enabling rapid and reproducible dimension compression of multi-dimensional data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional dimension compression methods are used on multi-dimensional flow cytometry data, then the data can be processed, but the processing speed is slow and accuracy is insufficient
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network model offline with large datasets before actual dimension compression tasks. The model learns optimal compression mappings in advance, enabling rapid and accurate real-time compression without performing heavy computations during the actual measurement process.
Solution Approach 2:
The patent replaces conventional mechanical/mathematical dimension compression algorithms with a neural network-based intelligent system. This substitution enables the system to learn complex non-linear relationships in the data and perform dimension compression with both high speed and high accuracy simultaneously.
2Reliability
If dimension compression is performed on large datasets, then analysis becomes possible, but reproducibility is poor due to stochastic processes
Solution Approach 1:
The patent performs the complex learning and model training in advance offline, creating a deterministic compression model. During actual data processing, only the pre-trained model is applied without stochastic elements, ensuring high reproducibility while maintaining manageable system complexity.
Solution Approach 2:
The patent creates a learned representation (copy) of the high-dimensional data structure in the compressed space through neural network training. This learned mapping can be repeatedly applied to different datasets with consistent results, ensuring reproducibility without requiring complex processing systems during actual use.
3Ease of operation
If multi-dimensional data is compressed to lower dimensions, then analysis becomes easier, but information loss occurs
Solution Approach 1:
The patent replaces conventional linear dimension compression methods with a neural network-based non-linear transformation system. This intelligent system learns to preserve critical information patterns and relationships during compression, achieving both ease of analysis and minimal information loss simultaneously.
Solution Approach 2:
The patent changes the parameter representation from raw high-dimensional measurements to learned compressed features that capture essential biological variations. The neural network transforms the data parameters into a compressed space that maintains discriminative power for downstream analysis while reducing dimensionality.
Data Source
AI summary
An information processing apparatus including a dimension compression section that generates dimension-compressed data for input data on the basis of a learning model generated by a neural network in which same data acquired from a biologically derived substance is applied to an input layer and an output layer.