Single-Cell Transition Features for Drug-Resistant Cell-State Detection
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Solution Overview
Problem
Medicine resistance poses a challenge in curing diseases like cancer, as bulk sequencing analysis fails to characterize individual cell states, leading to inaccuracies in determining cell state transitions.
Innovation Solution
Transform single cell data into a lower dimensional space using dimensionality reduction techniques, identify transition paths through topological data analysis, and extract features to train machine learning models for classifying cell state transitions, enabling personalized medicine development.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If bulk sequencing analysis is used, then analysis simplicity is maintained, but measurement precision of individual cell states deteriorates
Solution Approach 1:
The patent segments bulk sequencing data into individual cell-level analyses by identifying and analyzing transition paths through sequences of cell states. This segmentation allows the system to maintain operational simplicity while achieving precise measurement of individual cell state transitions that would be obscured in bulk analysis.
Solution Approach 2:
The patent introduces a temporal dimension to cell state analysis by modeling transitions as sequences of states over time. This dimensional transformation enables the system to capture dynamic cell state changes while maintaining the simplicity of bulk sequencing workflows, resolving the contradiction between ease of operation and measurement precision.
2Measurement precision
If single cell data analysis is performed, then measurement precision of cell states is improved, but device complexity increases
Solution Approach 1:
The patent extracts and focuses specifically on transition path information from single cell data, separating the critical transition state sequences from the broader dataset. This extraction approach maintains high measurement precision for cell state transitions while reducing the effective complexity of data processing by concentrating on relevant features.
Solution Approach 2:
The patent transforms the analysis focus from comprehensive single cell parameter evaluation to specific transition path parameter extraction. By changing the analytical parameters from examining all cell states to tracking state transitions, the system achieves precise cell state characterization with reduced computational complexity.
3Loss of information
If comprehensive single cell data processing is performed, then information completeness is improved, but loss of time in analysis increases
Solution Approach 1:
The patent performs preliminary identification of transition paths and key cell states before comprehensive analysis. By pre-identifying relevant transition sequences and marker genes, the system ensures information completeness is maintained while significantly reducing the time required for full data processing through targeted analysis of pre-identified features.
Data Source
AI summary
Methods and systems for training a machine learning model are described. A processor can transform single cell data in a first space into projection data in a second space having a dimensionality lower than or equal to the first space. The processor can produce a cover having a plurality of sets of the projection data. The processor can determine a plurality of transition paths among the plurality of sets. A transition path can represent a transition from one cell state to another cell state. The processor can translate the transition paths from the second dimensional space to the first dimensional space. The processor can extract features from the transition paths in the first dimensional space. The processor can generate training data using the features, and use the training data to train a machine learning model for classifying cell state transitions.