Plugin Framework for Real-Time Single Cell Data Analysis
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Solution Overview
Problem
Conventional data analysis tools for single cell experimentation are inadequate in handling high-dimensional data and identifying biologically relevant patterns without requiring highly skilled experts, leading to knowledge discordance and missed important phenotypes that could impact disease or cellular function.
Innovation Solution
A framework and interface for invoking and assimilating external algorithms in real-time, allowing data-driven analysis through reproducible and updatable nodes that direct algorithm choice and presentation, enabling automated phenotype identification and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual clustering and analysis methods are used, then expert knowledge can be applied to identify cell phenotypes, but the analysis cannot scale to handle the exponential increase in high-dimensional data from single cell experimentation instruments
Solution Approach 1:
The patent introduces an intermediary software layer that sits between the raw high-dimensional data and the expert analyst. This software automatically performs clustering, dimensionality reduction, and phenotype identification algorithms, translating complex computational tasks into intuitive visual displays that experts can interpret without needing to master the underlying computational methods.
Solution Approach 2:
The patent replaces manual, mechanical analysis methods with automated computational algorithms. Instead of experts manually clustering thousands of cells across multiple parameters, the system uses computer-executed algorithms to perform clustering, gating, and phenotype identification, dramatically increasing throughput while reducing the mechanical burden on analysts.
2Measurement precision
If experts focus their study on a subset of cell phenotypes they know strongly, then they can maintain deep expertise in those areas, but they miss important phenotypes that could have big impact on disease or cell function
Solution Approach 1:
The patent creates a universal analysis platform that can handle diverse cell types, markers, and experimental designs within a single system. The software automatically adapts to different data configurations and can identify phenotypes across the entire parameter space, enabling experts to maintain deep knowledge in their specialty areas while the system simultaneously provides comprehensive coverage of all phenotypes including those outside their expertise.
Solution Approach 2:
The system incorporates feedback mechanisms where automated algorithms continuously refine phenotype identification based on data patterns, and expert input is integrated to improve algorithm performance. This allows the system to learn from expert knowledge while automatically discovering novel phenotypes, creating a feedback loop that enhances both precision and versatility over time.
3Adaptability or versatility
If the number of markers and cells examined is increased to overcome knowledge discordance, then more complete phenotype identification is possible, but the number of possible clusters becomes astronomically large (e.g., 33,554,432 clusters) making manual analysis impossible
Solution Approach 1:
The patent applies dimensionality reduction techniques that transform high-dimensional data into lower-dimensional visual representations. By projecting thousands of parameters and millions of potential clusters into intuitive 2D or 3D visual displays, the system maintains the ability to analyze comprehensive phenotype space while making the results operationally feasible for experts to interpret and interact with.
Solution Approach 2:
The system segments the astronomically large phenotype space into manageable hierarchical groups. Instead of presenting all 33 million clusters at once, the software automatically hierarchically clusters and organizes phenotypes at multiple levels, allowing experts to navigate from broad cell type categories down to specific subpopulations, making the analysis operationally feasible while maintaining comprehensive phenotype discovery capability.
Data Source
AI summary
A framework and interface for invoking and assimilating external algorithms and interacting with said algorithms in-session and real-time are described herein. An example embodiment also includes reproducible, updatable nodes that can be leveraged for data-driven analysis whereby the data itself can direct the algorithm choice, variables, and presentation leading to iteration and optimization in an analysis workflow. With example embodiments, an entire discovery or diagnosis process may be executed on a particular data set, thereby divorcing the discovery or diagnosis process from a specific data set such that the same discovery or diagnosis process, phenotype identification, and visualizations may be repeated on future experiments, published, validated, or shared with another investigator.