Joint Gene Expression and ATAC Visualization for Cell Cluster Discovery
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Solution Overview
Problem
Conventional methods struggle to effectively discover patterns in large datasets of gene expression and DNA chromatin accessibility due to limited replicate information and biological processes that are not well understood, making it difficult to identify unknown classes and variables that discriminate between cell states.
Innovation Solution
A method for visualizing patterns in datasets using a computer system with processing cores and memory, employing clustering algorithms like Louvain modularity, k-means, and UMAP embedding to project cells in two dimensions, and displaying differential gene and ATAC peak values in heat maps, allowing for the identification of cell clusters and their characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional statistical methods are used to analyze large datasets, then the analysis process is simple, but the methods fail to discover patterns due to limited replicate information
Solution Approach 1:
The patent transforms the analysis approach by changing from classical statistical parameters to dimensionality-reduced representations (principal components). This allows pattern discovery in high-dimensional single-cell data where traditional statistics fail due to limited replicates, while maintaining computational tractability through mathematical transformation of the data parameters.
Solution Approach 2:
The patent introduces dimensionality reduction techniques (PCA, t-SNE, UMAP) as intermediary steps between raw high-dimensional data and final pattern analysis. These intermediaries transform the data into lower-dimensional spaces where patterns become visible and analyzable, bridging the gap between complex data and interpretable results.
2Measurement precision
If high throughput transcript identification is performed for hundreds or thousands of cells, then gene expression quantification capability is improved, but the data complexity increases making pattern discovery difficult
Solution Approach 1:
The patent addresses the complexity of high-dimensional single-cell data by applying dimensionality reduction techniques that project data from thousands of dimensions (genes) into 2-3 visualizable dimensions while preserving important biological variations. This allows precise gene expression measurements to be maintained while making the data interpretable through visual patterns in reduced spaces.
3Productivity
If classical statistical methods are applied to datasets with limited replicate information, then the analysis is computationally efficient, but the methods are not amendable to such data
Solution Approach 1:
The patent changes the fundamental parameters of analysis by moving from replicate-based statistical inference to dimensionality-reduced pattern recognition. This allows efficient computation on single-cell data with limited replicates by transforming the problem from statistical hypothesis testing to geometric pattern discovery in high-dimensional space.
Data Source
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AI summary
Systems and methods for visualizing patterns in discrete attribute value datasets are provided. A dataset comprises a discrete attribute value for each gene in a plurality of genes, for each cell in a plurality of cells. The dataset further comprises ATAC counts for each ATAC peak in a plurality of peaks, for each of the cells. Cells are assigned cluster groups in a first plurality of cluster groups based on a first clustering of discrete attribute values for the genes across the cells. Cell are also assigned cluster groups in a second plurality of cluster groups based on a second clustering of ATAC fragment count values for the ATAC peaks across the cells. A projection of the cells uses one of the first or second cluster group assignments. There is indicated, for each cell within the projection, membership in the other of the first or second cluster group assignments.