Particle Data Noise Reduction via Power Function Transformation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current particle analyzers face challenges in reducing noise, particularly non-linear photon counting statistics error, which leads to false positives and inaccurate data analysis due to the limitations of linear compensation methods and the inability to handle large dynamic ranges in fluorescence data.
Innovation Solution
A computer-implemented method and system that transforms raw particle data to reduce noise, compensates for dye spectral overlap, and converts the data to re-establish its original dimensions, using mathematical models such as power functions to correct for photon counting statistics error and linear compensation for spillover, while allowing for inverse transformations to restore original data characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If linear compensation methods are used to correct spillover effects, then spectral overlap between dyes is reduced, but non-linear photon counting statistics error remains and causes false positives
Solution Approach 1:
The patent transforms the data using mathematical functions (such as square root transformation) to change the parameter space from linear photon counts to a transformed domain where noise becomes more uniform. This parameter change allows subsequent linear compensation to be more effective and reduces the impact of non-linear photon counting statistics error, thereby reducing false positives while maintaining spectral overlap correction.
2Adaptability or versatility
If multiple dyes with increased fluorescence range are used to identify more particle properties, then analysis capability is improved, but spectral overlap among bins increases
Solution Approach 1:
The patent applies data transformation and noise reduction processing before the compensation step. By preliminarily transforming the raw data to reduce non-linear noise effects, the subsequent compensation for spectral overlap becomes more accurate, allowing multiple dyes to be used effectively without excessive spectral interference.
3Reliability
If raw particle data is processed to reduce non-linear noise, then false positives are prevented, but data transformation complexity increases
Solution Approach 1:
The patent replaces complex iterative noise reduction algorithms with straightforward mathematical transformations (such as square root transformation followed by linear compensation). This substitution maintains the ability to prevent false positives while significantly reducing computational complexity and making the processing more efficient and easier to implement.
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
The method significantly reduces noise and spillover effects, improving the clarity and accuracy of particle analysis data, enabling more reliable identification of particle populations and preventing false positives by addressing the root causes of non-linear noise and dynamic range issues.
Implementation Method 1
these instruments are generally equipped with very sensitive detectors called photomultiplier tubes (PMTs) that can detect individual photons
Implementation Method 2
Prior to being exposed to an excitation light, particles may be labeled (also referred to as marked) with spectrally distinct fluorescent dyes or fluorescent dyes conjugated to molecule-specific ligands
Data Source
AI summary
In an embodiment, a computer-implemented method is provided for processing digital data from a particle analyzer. The method includes receiving digital particle data, transforming the received data to correct for noise, applying linear compensation for dye spectral overlap, and re-establishing the original dimensions of the data. Processed particle data may be used for display, analysis or storage. In another embodiment, a system comprises at least one particle analyzer coupled to a particle data corrector. The particle data corrector includes a transformer, a compensator, and a converter.