Chromosome Map Visualization for Gene Expression Data Correlation
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Solution Overview
Problem
Current visualization tools for genomic and chromosomal data lack the ability to seamlessly overlay and correlate gene-related data with chromosome maps, requiring manual processes and limiting the ability to maintain focus and context, especially when dealing with large datasets and multiple views.
Innovation Solution
The system allows for the import and manipulation of arbitrary gene-related data to be displayed relative to chromosome maps, using identifiers to match and reorder data according to gene locations, enabling automatic correlation and visualization with statistical assessments, and providing interactive capabilities for data manipulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If gene-related data is manually processed and displayed separately from chromosome maps, then data processing is simple, but the ability to correlate and visualize data in context is limited
Solution Approach 1:
The patent combines gene-related data visualization with chromosome map display into a single integrated view. The system overlays gene expression data, genotype information, and other genomic data directly onto the chromosome map coordinates, allowing users to see both the chromosomal context and the specific data points simultaneously without separate processing steps
Solution Approach 2:
The visualization system is designed to handle multiple types of gene-related data (expression data, genotype data, annotation data) and display them uniformly on chromosome maps. The system can import data from various sources and formats, automatically parse identifiers, and render different data types using the same chromosomal coordinate system, making it a universal tool for genomic data visualization
2Measurement precision
If detailed views of gene data are provided, then data analysis capability is improved, but the ability to maintain overview context is lost
Solution Approach 1:
The system implements a nested viewing structure where chromosome maps provide the outer context framework, and gene-related data points are nested within this framework at their appropriate chromosomal positions. Users can zoom into specific chromosomal regions to see detailed gene data while the overall chromosomal map remains visible, maintaining both overview and detail simultaneously
Solution Approach 2:
The patent uses multiple visual dimensions to display data: chromosomal position provides the primary spatial dimension, while data values (expression levels, genotype calls) are represented through color coding, size variations, and symbolic markers. This multi-dimensional approach allows detailed data analysis without sacrificing the chromosomal context, as users can perceive both position and data value simultaneously
3Adaptability or versatility
If multiple types of genomic data are visualized simultaneously, then data correlation capability is improved, but visualization complexity increases
Solution Approach 1:
The system applies different visual encoding strategies to different data types based on their local characteristics. Gene expression data uses color gradients to represent expression levels, genotype data uses symbolic markers for different alleles, and annotation data uses text labels. Each data type is optimized with appropriate visual properties while maintaining a unified chromosomal coordinate system, allowing multiple data types to be displayed without overwhelming complexity
4Productivity
If automated identifier matching is implemented, then data processing efficiency is improved, but system complexity increases
Solution Approach 1:
The system implements automated identifier matching where the visualization software automatically parses gene identifiers from imported data, matches them against chromosomal coordinates, and positions data points without manual intervention. The system self-corrects for different identifier formats and sources, automatically resolving identifiers to chromosomal positions through built-in databases and coordinate systems, eliminating the need for manual data preparation
Data Source
AI summary
Systems and methods for displaying gene- and/or protein-related data with respect to chromosome maps at locations identifying relevant positioning of the genes with which the gene- and/or protein related data are associated. Multiple experiments may be plotted onto the display adjacent one or more chromosome maps. Automatic extraction of genomic location, based on accession numbers or other unique identifiers and cross connection with expression data is provided. Statistical assessments of correlations between expression and genome localization may be performed. Zooming capabilities, thumbnail/fullview toggling, browsability and linked data may be included as features of the visualization systems described.