Flow Cytometry Training Gate Warping for Population Alignment
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Solution Overview
Problem
Existing flow cytometry methods face challenges in accurately adjusting training gates to accommodate new sets of data, leading to inefficiencies in identifying and distinguishing populations of interest.
Innovation Solution
A method and system for adjusting a training gate using a processor-implemented algorithm that employs B-spline warping to align and overlay the gate from a first set of flow cytometer data onto a second set, ensuring the gate fits the population morphology of the new data set.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a training gate is manually adjusted to accommodate new flow cytometer data sets, then the accuracy of population identification improves, but the time and complexity of data analysis increases
Solution Approach 1:
The system pre-processes flow cytometer data by generating images and calculating cumulative distribution functions in advance, creating a standardized format that enables rapid gate adjustment. This preliminary processing of data into comparable formats allows the gate to be efficiently adapted to new data sets without manual re-analysis of raw data.
Solution Approach 2:
The patent replaces manual gate adjustment with an automated computer-implemented algorithm that uses image registration techniques. The system automatically warps the training gate to fit new populations by comparing cumulative distribution functions, substituting the mechanical/manual process with an automated computational approach that reduces analysis time while maintaining accuracy.
2Adaptability or versatility
If a training gate is adjusted to fit variations in different data sets, then the adaptability of the analysis method improves, but the complexity of the adjustment process increases
Solution Approach 1:
The system achieves adaptability by transforming data into a standardized parameter format (cumulative distribution function values) that enables consistent comparison across different data sets. By converting diverse flow cytometer data into comparable image formats with standardized parameters, the system can automatically adjust gates to fit various population variations without increasing procedural complexity.
Solution Approach 2:
The patent creates a standardized copy or representation of the training gate through image registration. The system generates an image representation of the training gate and automatically warps this copy to fit new populations by matching cumulative distribution functions, enabling versatile adaptation without requiring complex manual reconfiguration for each new data set.
3Productivity
If automated algorithms are used to adjust training gates, then the productivity of data analysis improves, but the precision of gate fitting may deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms by comparing cumulative distribution functions of the training data with new data sets. The automated algorithm uses this feedback to iteratively adjust the gate parameters, ensuring that the fitted gate accurately represents the new population while maintaining the speed of automated processing. The feedback loop validates that the automated adjustment achieves precise fitting.
Solution Approach 2:
The system performs preliminary processing of data into standardized image formats and pre-calculates cumulative distribution functions before the actual gate adjustment. This preliminary preparation enables the automated algorithm to work with pre-processed, comparable data, maintaining both high productivity and precision by avoiding repeated complex calculations during the gate fitting process itself.
Data Source
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AI summary
Methods for adjusting a training gate prepared from a first set of flow cytometer data to accommodate a second set of flow cytometer data are provided. In embodiments, methods include generating an image for each of the first and second sets of flow cytometer data. In some instances, generating an image includes organizing the data into two-dimensional bins, and assigning shades to each bin such that the bins are represented by pixels. In some instances, methods include warping with a computer implemented algorithm the generated image of the first set of flow cytometer data such that it maximizes resemblance to the second set of flow cytometer data, and applying the same transformation to the training gate. In some embodiments, methods include overlaying the adjusted training gate onto the generated image of the second set of flow cytometer data. Systems and computer-readable media for adjusting a training gate are also provided.