Cell Type-Weighted Dimensionality Reduction for Intra-Cell Co-Expression
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Solution Overview
Problem
Conventional dimensionality reduction methods in single-cell analysis fail to effectively separate biologically meaningful cell types, leading to overlap and difficulty in understanding differential marker expression due to the competition between lineage markers and other markers, and variability across samples.
Innovation Solution
A facility that applies dimensionality reduction weighted by cell type, using techniques like t-SNE or UMAP, to ensure similar cell types are closer together in lower-dimensional space, emphasizing cell types relative to constituent expression levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional dimensionality reduction methods are used, then computational simplicity is maintained, but cell type separation and neighborhood purity deteriorate
Solution Approach 1:
The patent modifies the dimensionality reduction process by changing the parameter weighting scheme. Instead of treating all markers equally, the system adjusts the weight parameters to emphasize lineage markers and cell type identity markers during the t-SNE or UMAP computation. This parameter adjustment resolves the contradiction by improving cell type separation precision while maintaining the same computational framework, thus not significantly increasing device complexity.
Solution Approach 2:
The patent performs preliminary actions by pre-computing cell type assignments and lineage relationships before executing the dimensionality reduction. By preparing weighted distance matrices and pre-identifying cell type boundaries in advance, the system improves subsequent visualization accuracy without adding significant computational complexity during the actual reduction process.
2Manufacturing precision
If dimensionality reduction emphasizes constituent expression levels, then marker expression detail is improved, but cell type separation deteriorates due to overlap
Solution Approach 1:
The patent applies local quality by treating different regions of the data space differently. Lineage markers and cell type identity markers are assigned higher weights in regions where they define cell type boundaries, while other markers are weighted appropriately in regions where they provide discriminatory power. This localized weighting strategy maintains cell type separation while preserving important marker expression information.
Solution Approach 2:
The patent creates a composite weighting scheme that combines multiple marker types (lineage markers, cell type identity markers, and other discriminatory markers) into a unified dimensionality reduction objective. This composite approach integrates information from all marker types while emphasizing those most critical for cell type separation, thus resolving the contradiction between separation precision and information preservation.
3Manufacturing precision
If conventional dimensionality reduction is used, then processing speed is maintained, but neighborhood purity and visualization quality deteriorate
Solution Approach 1:
The patent performs preliminary computation of cell type assignments, lineage relationships, and weighted distance matrices before executing dimensionality reduction. By preparing these components in advance, the system improves neighborhood purity in the final visualization without significantly increasing the processing time of the main dimensionality reduction step, thus maintaining productivity.
Data Source
AI summary
A facility for analyzing a sample of cells is described. The facility accesses an emphasis weight specifying a degree to which cell type is to be emphasized relative to cell constituent expression levels in determining visualization coordinates for each cell of the sample. The facility generates a cell matrix comprising a grid of values in which each row represents a cell of the sample, in which a first group of the columns correspond to constituent expression levels detected for the cell, and a second group of the columns correspond to a representation established for the cell's cell type, the values in the first group of the columns being weighted against the values in the second group of the columns in accordance with the accessed emphasis weight. The facility performs dimensionality reduction on the rows of the generated cell matrix to obtain visualization coordinates for each cell of the sample.