Cytometric Data Scaling for Signal-to-Noise Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Flow-type particle detection and analysis systems face challenges in effectively distinguishing between signal and noise, particularly with high dimensionality cytometric data, which affects the accuracy of particle characterization and population identification.
Innovation Solution
The method involves obtaining cytometric data, identifying a parameter of interest, specifying positive and negative measurement intervals, and scaling the data by transforming the parameter based on these intervals, using techniques such as adaptive scaling to improve signal-to-noise ratios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If high dimensionality cytometric data is analyzed without scaling, then more parameters can be measured and analyzed, but signal-to-noise ratio deteriorates and noise affects particle clustering
Solution Approach 1:
The patent applies parameter changes by transforming the cytometric data through scaling operations. Specifically, it scales the data based on the coefficient of variation (CV) of each parameter, converting raw values into standardized scores that account for the variability inherent in high-dimensional measurements. This transformation changes the parameter representation from raw intensity values to scaled scores, thereby improving signal-to-noise ratio while preserving the high dimensionality information.
Solution Approach 2:
The patent introduces an additional dimensional transformation by adding a scaled score dimension to the existing high-dimensional cytometric data. Each parameter is enhanced with a scaled score that represents its signal-to-noise characteristics, creating an extended feature space that maintains the original dimensional information while adding discriminative power for noise reduction.
2Ease of operation
If conventional scaling methods are applied to cytometric data, then data normalization is achieved, but signal-to-noise characteristics are not optimized
Solution Approach 1:
The patent improves upon conventional normalization by changing the scaling parameter from simple min-max or z-score normalization to CV-based scaling. The scaling factor is derived from the coefficient of variation (standard deviation divided by mean) of each parameter, which captures the relative variability specific to cytometric measurements. This parameter change optimizes the scaling to reflect the actual signal-to-noise characteristics of the data.
Solution Approach 2:
The patent incorporates feedback mechanisms by using the observed distribution characteristics (mean and standard deviation) of each parameter to dynamically adjust the scaling factor. The scaling process uses feedback from the data itself - parameters with higher variability receive different scaling treatment than those with lower variability, creating an adaptive normalization that optimizes signal-to-noise ratio based on the actual data characteristics.
Data Source
AI summary
Aspects of the present disclosure include methods for processing and scaling cytometric data. Methods according to certain embodiments include obtaining cytometric data for a sample, wherein the cytometric data comprises measurements of a plurality of parameters from particles irradiated in the sample flowing in a flow stream; identifying a parameter of interest; specifying positive and negative measurement intervals on the parameter of interest; scaling the cytometric data by transforming the parameter of interest based at least in part on the corresponding specified positive and negative intervals. Systems for practicing the subject methods are also provided. Non-transitory computer readable storage mediums are also described.