Hierarchical Clustering for Cell Type Distinction
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Solution Overview
Problem
Current methods for classifying expression profiles and identifying targets to distinguish cell types are hindered by the complexity of large data sets generated from techniques like barcoding, which require further analysis to identify markers for cell type differentiation.
Innovation Solution
The method involves receiving a target counts data structure, hierarchically clustering expression profiles, and determining whether nodes in a dendrogram should be split or merged based on distance thresholds, ultimately identifying targets for distinguishing cell types through a merging cluster set process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hierarchical clustering is performed on large expression profile data sets, then cell types can be distinguished, but the complexity of data analysis increases
Solution Approach 1:
The patent applies segmentation by dividing the large expression profile data set into smaller, manageable clusters through hierarchical clustering. The dendrogram structure segments cells into distinct groups based on expression similarity, making the complex data analyzable in discrete units rather than as a monolithic data set.
Solution Approach 2:
The patent transforms the complex multi-dimensional expression profile data into a hierarchical tree structure (dendrogram), adding a structural dimension to the analysis. This dimensional transformation allows complex expression patterns to be visualized and analyzed through tree topology rather than raw numerical data.
2Measurement precision
If expression profiles are clustered to identify markers, then cell type differentiation can be achieved, but the time required for data analysis increases
Solution Approach 1:
The patent performs preliminary hierarchical clustering to pre-organize cells into clusters based on expression similarity before identifying markers. This preliminary grouping reduces the search space for marker identification, as markers can be focused on within-cluster variations rather than searching across all cells individually.
Solution Approach 2:
The patent uses the dendrogram structure as a copied representation of the original expression data, allowing analysis to be performed on the tree structure rather than the raw data. This copying enables efficient traversal and comparison of expression patterns through the hierarchical structure.
3Ease of operation
If a dendrogram structure is used to organize expression profiles, then data organization is improved, but the computational processing required increases
Solution Approach 1:
The patent merges individual expression profiles into hierarchical clusters, combining similar cells into parent nodes in the dendrogram. This merging reduces the number of individual comparisons needed, as once cells are grouped, only cluster-level comparisons are required at higher hierarchical levels.
Data Source
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AI summary
Disclosed herein are methods and systems for identifying targets for distinguishing cell types. In some embodiments, the method comprises: hierarchically clustering expression profiles of cells to generate a dendrogram with each leaf representing the expression profile of a different individual cell. The dendrogram can be pruned by eliminating invalid splits (and their children). The remaining leaves in the dendrogram can be merged, independent of their locations in the dendrogram, based on their distances to one another to generate clusters of expression profiles. The method can identify targets for distinguishing cell types based on targets that are expressed differentially in the clusters.