Particle Analyzer Tree Classification for Flow Cytometry
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Solution Overview
Problem
Current particle analysis methods face challenges in efficiently classifying and sorting particles due to the complexity of multidimensional data and the need for precise graphical display of large datasets, particularly in flow cytometry, where identifying specific cell populations within a sample is cumbersome and prone to contamination.
Innovation Solution
A computer-implemented method that generates a tree representing groups of related particles based on measurements, calculates the relatedness between these groups using probability density functions, and configures the particle analyzer to classify subsequent particles, incorporating unsupervised learning and threshold settings to improve sorting accuracy and reduce contamination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual gating methods are used to identify cell populations in multidimensional flow cytometry data, then flexibility in analysis is maintained, but the process becomes cumbersome and prone to contamination
Solution Approach 1:
The system performs automated unsupervised learning to self-identify particle groups and classification boundaries without requiring manual gating operations. The particle analyzer automatically processes multidimensional data, generates trees representing particle groups, and configures classification parameters, eliminating the need for operator intervention in the gating process while improving reliability and reducing contamination risks
Solution Approach 2:
The system transforms the classification approach by changing from manual parameter setting to automated statistical analysis. It calculates measures of relatedness between particle groups using probability density functions and automatically determines classification thresholds, converting the operational process from subjective manual gating to objective parameter-driven automated classification
2Productivity
If traditional visualization methods are used for multidimensional particle data, then data can be displayed, but efficient classification and sorting become difficult
Solution Approach 1:
The system segments the complex multidimensional data by generating a hierarchical tree structure that divides particles into distinct groups based on their characteristics. This tree representation breaks down the complexity of multidimensional data into manageable segments, enabling efficient classification and sorting by organizing particles into hierarchical categories that can be processed systematically
Solution Approach 2:
The system introduces an intermediary computational layer that processes raw multidimensional particle data before final classification. This intermediary layer generates the tree structure and calculates measures of relatedness, acting as a mediator that transforms complex raw data into organized groupings that facilitate efficient sorting while managing data complexity
3Productivity
If automated gating methods are implemented, then classification speed improves, but precision in identifying specific cell populations may be compromised
Solution Approach 1:
The system incorporates feedback mechanisms where the measured relatedness between particle groups informs the classification process. By calculating probability density functions and measures of relatedness, the system continuously refines its understanding of particle group boundaries and adjusts classification decisions based on statistical evidence, maintaining precision while achieving automated processing speeds
Solution Approach 2:
The system performs preliminary analysis by generating the tree structure and calculating group relatedness measures before final classification decisions are made. This preliminary action of organizing data into groups and assessing their relationships enables the system to make faster, more accurate classification decisions by having pre-computed information about particle groupings and their statistical relationships
Data Source
AI summary
Some embodiments of the methods provided herein relate to sample analysis and particle characterization methods. Some such embodiments include receiving, from a particle analyzer, measurements for a first portion of particles associated with an experiment. Some embodiments also include generating a tree representing groups of related particles based at least in part on the measurements, wherein the tree includes at least three groups. Some embodiments also include generating a measure of relatedness between a first group and a second group of the tree based at least in part on the measurements. Some embodiments also include and configuring the particle analyzer to classify a subsequent particle associated with the experiment with the first group real-time, wherein the subsequent particle is not included in the first portion of particles. Some embodiments also include sorting the subsequent particle.