Outlier-Excluded Reference Spectrum for Flow Cytometry
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Solution Overview
Problem
The accuracy of fluorescence intensity calculation in flow cytometry is compromised by noises and abnormal values in the reference spectrum, which is typically generated from microparticles stained with a single fluorochrome in a measurement environment similar to the actual environment, leading to reduced calculation accuracy.
Innovation Solution
An information processing device and method that perform statistical processing to exclude outlier spectra from a group of spectra obtained from microparticles exhibiting a similar response to light, and then calculate a reference spectrum using the cleaned dataset to improve the accuracy of fluorescence intensity calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reference spectrum is generated from spectra obtained from microparticles simply stained with one fluorochrome in an environment identical to the measurement environment, then the reference spectrum reflects the actual measurement conditions, but noises or abnormal values may be included in the generated reference spectrum reducing its accuracy
Solution Approach 1:
The patent extracts and removes outlier spectra from the group of spectra used for reference spectrum generation. By identifying and excluding spectra that deviate significantly from the majority (outliers), the method eliminates the source of noises and abnormal values while preserving the representative spectra that accurately reflect measurement conditions.
Solution Approach 2:
The patent implements a feedback mechanism where the reference spectrum generation process includes statistical evaluation of individual spectra. Spectra are assessed based on their deviation from the group average, and this feedback information is used to determine whether to include or exclude each spectrum, thereby iteratively improving the quality of the reference spectrum.
2Measurement precision
If statistical processing is performed to exclude outlier spectra, then the accuracy of the reference spectrum is improved, but additional processing steps and computational complexity are introduced
Solution Approach 1:
The patent replaces complex manual or heuristic methods for identifying bad spectra with automated statistical processing. By using mathematical criteria (deviation from mean, standard deviation thresholds) to objectively identify outliers, the method eliminates the need for subjective judgment while maintaining simplicity and consistency in the selection process.
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
The proposed solution enhances the accuracy of the reference spectrum, reducing the impact of noises and abnormal values, thereby improving the calculation accuracy of fluorescence intensity per light-emitting element.
Implementation Method 1
excitation light such as laser light is applied to microparticles flowing in a channel, and fluorescence, scattered light, or the like emitted from the microparticles is detected
Implementation Method 2
The detected light is quantified by being converted into an electric signal
Data Source
AI summary
An information processing device includes a statistical processing unit that performs statistical processing for a group of spectra obtained by applying light to a group of microparticles that exhibit one response property with respect to light, and on a basis of a result of the statistical processing, exclude a spectrum indicating an outlier from the group of spectra, and a reference spectrum calculation unit that calculates a reference spectrum using the group of spectra from which the spectrum indicating the outlier has been excluded.