Particle Analysis Data Flagging to Exclude Detector-Saturated Events
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing particle analysis systems face reliability issues due to light detectors exceeding detection limits, leading to inaccurate data acquisition and affecting other detectors through fluorescence compensation, necessitating unreliable data analysis.
Innovation Solution
An information processing apparatus that stores a flag for light intensity data exceeding a threshold and processes event data items excluding flagged data, improving data reliability by selectively removing unreliable data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fluorescence compensation is performed to improve measurement accuracy, then data reliability is improved, but when a light detector exceeds its detection limit, the compensation process propagates errors to other detectors
Solution Approach 1:
The patent applies preliminary action by setting flags on light intensity data before processing, marking data that exceeds detection limits. This allows the system to identify problematic data points in advance and exclude them from fluorescence compensation calculations, preventing error propagation to other detectors while maintaining accurate measurements for valid data points
Solution Approach 2:
The patent extracts and removes flagged light intensity data from the processing pipeline. By separating unreliable data (those exceeding detection limits) from reliable data and processing them differently, the system prevents contaminated data from affecting the fluorescence compensation calculations for other detectors, thus maintaining overall data reliability
2Productivity
If all event data is processed to maximize productivity, then data processing throughput is improved, but including flagged data degrades analysis reliability
Solution Approach 1:
The patent segments event data into two categories: flagged data (exceeding detection limits) and unflagged data (within detection limits). This segmentation allows the system to process unflagged data for reliable analysis while handling flagged data separately or excluding it, thus maintaining both high productivity through comprehensive processing and reliability through selective exclusion of contaminated data
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
Enhances data analysis reliability by excluding flagged light intensity data, ensuring accurate and reliable data processing and fractionation of particles.
Implementation Method 1
apparatuses (e.g., flow cytometers) that label particles such as cells using a fluorescent dye, irradiate a laser beam to the labeled particles, and detect fluorescent or scattered light from the irradiated particles
Implementation Method 2
the light reaching a light detector is converted to electrical signals (voltage pulses) and digitized
Data Source
AI summary
A technology is provided to improve reliability of data analysis.The technology provides, among others, an information processing apparatus including a storage section configured to store event data including light intensity data obtained by irradiating light to one of multiple particles, and a processing section configured to process multiple event data items acquired from the multiple particles. The storage section stores a flag to be given to the light intensity data in a case where the light intensity data exceeds a threshold value. In accordance with an instruction to exclude the flagged light intensity data, the processing section processes the multiple event data items other than the flagged light intensity data.