Compressed Spectral Data Clustering for Flow Cytometry Analysis
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Solution Overview
Problem
Flow cytometers using spectrum systems face increased processing times and the curse of dimensionality due to the acquisition of extensive feature amounts per cell, leading to prolonged data clustering and analysis times.
Innovation Solution
An information processing apparatus and method that includes a compression unit to reduce data volume, an acquisition unit for clustering results on compressed data, and an output unit to display the clustering process, utilizing dimension and tone compression techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spectral data is measured for each wavelength in a spectrum system, then measurement precision and information completeness are improved, but processing time increases and the curse of dimensionality occurs
Solution Approach 1:
The patent extracts and removes redundant information from spectral data through compression processing. The compression unit identifies and eliminates redundant wavelength information while retaining essential spectral characteristics, thereby reducing data dimensionality and processing time without sacrificing measurement precision.
Solution Approach 2:
The patent applies compression processing as a preliminary step before clustering analysis. By pre-compressing the spectral data to reduce dimensionality beforehand, the system avoids the computational burden of processing full-dimensional spectral data during clustering, thus reducing overall processing time while maintaining analysis accuracy.
2Loss of information
If spectral data is measured for each wavelength in a spectrum system, then information completeness is improved, but device complexity increases due to the curse of dimensionality
Solution Approach 1:
The compression unit extracts and removes redundant spectral information while preserving essential characteristics. This extraction process reduces data dimensionality and simplifies subsequent processing operations, thereby reducing device complexity without compromising information completeness.
Solution Approach 2:
The patent transforms the spectral data by changing its parameter representation through compression. By representing spectral information in a compressed parameter space rather than full wavelength space, the system reduces computational complexity while maintaining the essential information needed for accurate analysis.
3Loss of time
If compression processing is applied to spectral data, then processing time is reduced, but data amount increases before compression
Solution Approach 1:
The system performs compression processing as a preliminary step before clustering analysis. Although this initially increases data handling requirements, the compressed data size is ultimately smaller than the original, and the time savings during the subsequent clustering operation far exceed the initial compression overhead.
Solution Approach 2:
The compression processing enables the system to rush through the computationally intensive clustering operation much faster. By accepting the initial compression step, the system skips through the time-consuming full-dimensional clustering process, achieving net time reduction overall.
Data Source
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AI summary
An information processing apparatus capable of reducing a processing time at the time of analysis of information obtained from a measurement device of a spectrum system is provided. Provided is an information processing apparatus that includes a compression unit (120) that performs a compression process on a data amount of spectral data that is measured when a particle is irradiated with a laser beam, an acquisition unit (110) that acquires a result of a clustering process performed on the compressed spectral data, and an output unit (140) that outputs the result of the clustering process acquired by the acquisition unit.