Flow Cytometry Dot Plot Transformation for Cluster Separation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing flow cytometry systems face challenges in presenting complex data in a way that allows for easy identification and separation of data clusters corresponding to different components in a sample, particularly when multiple sensors are used, leading to increased complexity in analysis and data presentation.
Innovation Solution
The method involves transforming optical and physical characteristic values of flow cytometer data through rotation, translation, magnification, truncation, and principal component analysis to create a two-dimensional dot plot with separated data clusters, using axes that correspond to optical and physical characteristics, enabling easier interpretation and identification of abnormalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multiple light sensors are used to capture scattered light signals, then the amount of information obtained for identifying components is improved, but the complexity of analysis and data presentation increases
Solution Approach 1:
The patent transforms multi-dimensional sensor data into a two-dimensional dot plot representation. By projecting high-dimensional optical characteristic values onto two axes (e.g., forward scatter and side scatter), the system maintains information richness while enabling intuitive visual interpretation. This dimensional reduction allows multiple sensors to contribute meaningful data without overwhelming complexity in analysis.
2Loss of information
If multiple light sensors are used to capture scattered light signals, then the amount of information obtained for identifying components is improved, but the complexity of data presentation increases
Solution Approach 1:
The system converts complex multi-sensor data into a simplified two-dimensional visual format where each axis represents a specific optical characteristic. This allows intuitive visualization and interpretation by users without requiring expertise in flow cytometry, while still capturing the full information content from multiple sensors through the transformed data representation.
Solution Approach 2:
The patent employs color coding in the dot plot to represent different cell populations or characteristics. By assigning distinct colors to different data clusters or cell types, the system enhances the ease of data presentation and interpretation, allowing users to quickly identify and distinguish between different components in the sample without overwhelming complexity.
3Measurement precision
If optical characteristic values are transformed through rotation and translation, then data cluster separation is improved, but data processing complexity increases
Solution Approach 1:
The patent applies mathematical transformations (rotation and translation) to the optical characteristic values to optimize data cluster separation. By changing the parameter space through linear transformations, the system enhances the visual separation of data clusters corresponding to different cell types. These transformations are implemented through standard computational operations that, while increasing processing complexity, yield significant improvements in measurement precision and data interpretability.
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
This approach allows for clearer visualization of data clusters, facilitating easier identification of sample components and abnormalities by presenting them in a two-dimensional format that enhances interpretability even for non-experts.
Implementation Method 1
Light from the light source is absorbed and scattered by the components in a manner that is dictated by associated stains in the solution as well as the size and morphology of the components
Implementation Method 2
Light from the light source is absorbed and scattered by the components
Data Source
AI summary
A method for presenting flow cytometry data includes accessing sensed data of a flow cytometer used to sense optical responses from a sample including a plurality of components; estimating optical characteristic values, physical characteristic values, and counts of the plurality of components based on the sensed data; transforming at least some of the optical characteristic values, the physical characteristic values, or the counts, of at least one of the plurality of components, by at least one of rotating or translating, to provide transformed values; and presenting a two-dimensional (2D) dot plot base on the transformed values and based on at least some of the optical characteristic values, the physical characteristic values, and the counts, of the plurality of components. The 2D dot plot has a first axis corresponding to the optical characteristic values and a second axis corresponding to the physical characteristic values.