Flow Cytometer Spillover Analysis Without Population Identification
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Solution Overview
Problem
Conventional methods for quantifying spillover spreading in flow cytometer data require the identification of positive and negative populations, which is error-prone and time-consuming.
Innovation Solution
A method involving partitioning fluorescent flow cytometer data into quantiles based on the intensity of a first fluorochrome, estimating a zero-adjusted standard deviation, and calculating spillover spreading coefficients through linear regression to characterize spillover spreading without the need for population identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to quantify spillover spreading by identifying positive and negative populations, then spillover spreading can be characterized, but the process becomes error-prone and time-consuming
Solution Approach 1:
The patent extracts the spillover spreading quantification process from the population identification step. By using a mathematical model that directly calculates spillover spreading coefficients from raw flow cytometry data without requiring manual or automated gating of positive and negative populations, the method eliminates the time-consuming and error-prone population identification step while maintaining characterization accuracy
Solution Approach 2:
The patent replaces the manual/mechanical process of population identification and gating with an automated mathematical model. The model uses linear regression and covariance analysis to compute spillover spreading coefficients directly from the data matrix, substituting human-operated gating procedures with algorithmic processing that is both faster and more consistent
2Reliability
If population identification is performed to quantify spillover spreading, then spillover effects can be assessed, but errors increase and process complexity increases
Solution Approach 1:
The patent removes the population identification step entirely from the analysis pipeline. The mathematical model operates directly on the raw fluorescence intensity data matrix, extracting spillover spreading coefficients through covariance analysis and linear regression without requiring intermediate population segmentation
Solution Approach 2:
The patent changes the approach from qualitative population-based analysis to quantitative parameter-based analysis. By treating spillover spreading as a mathematical parameter that can be directly computed from data covariances and standard deviations, the method simplifies the process while improving reliability through consistent mathematical operations
Data Source
AI summary
Methods for characterizing spillover spreading originating from a first fluorochrome in fluorescent flow cytometer data collected for a second fluorochrome are provided. In some embodiments, methods include partitioning the fluorescent flow cytometer data according to the intensity of the data relative to the first fluorochrome. In embodiments, methods also include estimating with a first linear regression a zero-adjusted standard deviation for the intensity of light collected from the second fluorochrome for each of the partitioned quantiles based on the assumption that the intensity of light collected from the first fluorochrome is zero, and obtaining with a second linear regression a spillover spreading coefficient from the zero-adjusted standard deviations. Systems and computer-readable media for characterizing spillover spreading originating from a first fluorochrome in fluorescent flow cytometer data collected for a second fluorochrome are also provided.