Biomarker Expression Analysis via Cell Clustering Algorithm
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for analyzing and visualizing biomarker expression in cellular samples face challenges in efficiently processing and interpreting the vast biological complexity of multiplexing and image analysis data, limiting the ability to identify patterns and make meaningful diagnoses or prognoses.
Innovation Solution
A process and system that use numerical evaluation and computer algorithms to group cells with similar biomarker expression patterns by minimizing variance, creating new data points for each group and repeating the analysis until a predetermined number of groups is reached, allowing for enhanced visualization and analysis of biomarker data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiplexing and image analysis are used to examine multiple biomarkers simultaneously, then the quantity of biomarkers that can be analyzed is improved, but the complexity of data analysis increases
Solution Approach 1:
The patent segments the complex data analysis process into distinct computational steps: (1) extracting signal intensity values from images, (2) normalizing data across samples, (3) applying dimensionality reduction techniques, and (4) performing clustering analysis. This segmentation makes the analysis of multiple biomarkers manageable and systematic.
Solution Approach 2:
The patent introduces intermediate computational representations including normalized expression matrices, principal component scores, and clustering labels that mediate between the raw image data and the final biological interpretations. These intermediaries simplify the relationship between multiple biomarkers and enable easier analysis.
2Adaptability or versatility
If sequential staining and bleaching is used to test for multiple biomarkers, then the quantity of biomarkers that can be examined is improved, but the time required for analysis increases
Solution Approach 1:
The patent performs preliminary computational preparations including image registration, signal extraction, and data normalization before performing the actual clustering analysis. This preliminary action optimizes the data structure in advance, reducing the time required for subsequent analysis steps.
Solution Approach 2:
The patent implements automated continuous processing where image acquisition, signal extraction, normalization, and clustering are performed as continuous computational operations rather than discrete manual steps. This continuity maintains productivity throughout the analysis process.
3Ease of operation
If numerical analysis algorithms are applied to group cells with similar biomarker patterns, then the ease of data analysis is improved, but the computational resources required increase
Solution Approach 1:
The patent extracts and focuses computational resources on the most critical analysis steps: signal intensity extraction from images, data normalization, and clustering. By taking out only the essential computational operations and eliminating redundant processing, the system achieves ease of analysis while minimizing computational resource consumption.
Data Source
AI summary
The invention relates generally to a process of analyzing and visualizing the expression of biomarkers in individual cells wherein the cells are examined to develop patterns of expression by using a grouping algorithm, and a system to perform and display the analysis.