Flow Cytometer Signal Peak Identification Dynamic Thresholding
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Solution Overview
Problem
Identifying valid signal peaks in flow cytometer data traces is challenging, especially when multiple stains or dyes are used, leading to cross-talk among signal data traces, which complicates the detection of particles in sample fluids.
Innovation Solution
A method and system that batch-process time series signal data traces from flow cytometers, determining batch-specific noise characteristics and signal peak thresholds to identify peaks indicative of particle attributes, while accounting for cross-talk by separately processing data from different wavelength ranges and comparing temporal coincidences of signal peaks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple stains or dyes are used to evaluate particle attributes, then the quantity of particle attributes detected increases, but cross-talk among signal data traces increases making peak identification more difficult
Solution Approach 1:
The patent divides the signal processing into separate channels for each wavelength range detected by different photodetectors. Each channel processes its signal independently with dedicated peak identification algorithms, preventing cross-talk from one stain affecting another channel's measurements. This segmentation allows simultaneous detection of multiple particle attributes while maintaining clear signal separation.
2Reliability
If signal peak thresholding is applied to identify particles, then particle detection capability improves, but false identification increases due to noise and cross-talk
Solution Approach 1:
The patent implements dynamic thresholding where the signal peak threshold is not fixed but adapts based on the specific signal characteristics of each batch of data points. The system calculates optimal thresholds considering the noise profile and signal distribution of each wavelength channel, allowing accurate peak identification even when cross-talk causes varying signal levels across different measurement conditions.
3Productivity
If batch processing is used to reduce computational complexity, then processing speed improves, but noise characteristics vary between batches requiring adaptive thresholding
Solution Approach 1:
The patent changes the threshold parameter dynamically for each batch based on the noise characteristics observed in that specific batch. Rather than using a single fixed threshold for all batches, the system recalculates appropriate threshold values considering the signal-to-noise ratio and distribution characteristics of each batch, maintaining high detection accuracy across varying experimental conditions while preserving batch processing efficiency.
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
Effectively enhances the accuracy of particle attribute detection by reducing cross-talk interference and improving the identification of signal peaks, leading to more reliable evaluation of particle presence in sample fluids.
Implementation Method 1
The fluorescent response light from the sample fluid may be detected by one or more photodetectors of the flow cytometry instrument, which in turn, generate one or more electrical signal data traces
Data Source
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AI summary
Methods of evaluating particle attributes in a sample fluid subjected to flow cytometry investigation in a flow cytometer instrument, methods of processing time series signal data traces output by a flow cytometer instrument, and a flow cytometer system are provided. In the methods and systems, data points comprising time series signal data traces corresponding with detection during the flow cytometry investigation of light from the sample fluid in one or more wavelength ranges indicative of the presence of one or more particle attributes in the sample fluid are batch-processed using a batch-specific signal peak threshold determined as a function of a batch-specific noise characteristic to identify signal peaks in the batch of data points indicative of the presence of the one or more particle attributes in the sample fluid.