Immune Cell Clonotype Visualization for Interactive Selection
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Solution Overview
Problem
Current immune cell clonotype data analysis faces challenges in efficiently selecting cells of interest due to inefficient and mentally taxing data display, making interactive analysis difficult.
Innovation Solution
A method and system for visualizing immune cell clonotype data by obtaining a single cell dataset, identifying clonotype groups, selecting a schema to visualize amino acids based on position or chemical identity, and rendering these in a graphic representation to select cells of interest based on predefined criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional data display methods are used for immune cell clonotype data, then data can be presented, but the display becomes inefficient and mentally taxing, making interactive analysis difficult
Solution Approach 1:
The patent segments the complex immune cell clonotype data into distinct visual components including clonotype groups, amino acid sequences, and graphical representations. Each element is separated and presented in a structured format that reduces cognitive load while maintaining analytical capability.
Solution Approach 2:
The patent transforms tabular amino acid sequence data into a two-dimensional graphical representation where amino acids are displayed as colored blocks arranged by position and chemical identity. This dimensional transformation makes patterns and variations visually apparent without requiring users to mentally parse complex text tables.
2Loss of information
If detailed amino acid sequence data is displayed for all cells, then complete information is available, but the data display becomes inefficient and hard to interpret
Solution Approach 1:
The patent applies local quality by differentiating amino acid representation based on their chemical properties. Amino acids are colored and categorized by their chemical identity (e.g., charged, polar, hydrophobic), allowing users to quickly assess functional characteristics without memorizing or mentally processing each individual sequence.
Solution Approach 2:
The patent uses color coding to represent different chemical identities of amino acids. This visual encoding system allows rapid comprehension of amino acid properties and variations across clonotypes, maintaining complete information while dramatically improving interpretation speed and efficiency.
Data Source
AI summary
Methods and systems for selecting a cell of interest based on immune cell data are disclosed. For example, a method may comprise obtaining a single cell or spatial dataset, wherein the single cell or spatial dataset comprises a dataset of immune cell receptors, antibodies, or fragments thereof from a sample; identifying a clonotype group in the single cell or spatial dataset;selecting a schema to visualize selected amino acids in the clonotype group based on positions or chemical identity of the selected amino acids; visualizing the selected amino acids in the clonotype group in a graphic representation according to the schema; and selecting a cell of interest from the clonotype group based on a pre-defined criterion using the graphic representation.