Cell-Type RNA Profiling for Accurate Bulk Cancer Composition Analysis
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Solution Overview
Problem
Current RNA sequencing technologies lack structured information linking human genome data with patient/clinical information, leading to inaccurate diagnosis and treatment decisions due to varying tumor purities and cell maturation stages in bulk-cell sequencing.
Innovation Solution
Developed methods model RNA sequencing data as a sum of parts, using gamma distributions and machine-learning algorithms to identify and quantify cell types and their proportions, refining optimization models to determine cancer composition accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If bulk-cell sequencing is used to analyze RNA data, then throughput and coverage are improved, but measurement precision deteriorates due to varying tumor purities and cell maturation stages
Solution Approach 1:
The patent segments the bulk-cell sequencing data into distinct cell-type components by modeling the RNA data as a sum of parts, where each part represents a different cell type's contribution. This segmentation allows the system to separate tumor cells from non-tumor cells and differentiate between various cell maturation stages, thereby improving measurement precision while maintaining the high throughput of bulk sequencing.
2Loss of time
If bulk-cell sequencing data is analyzed without structured information, then analysis speed is improved, but reliability deteriorates due to lack of structured information between genome and clinical data
Solution Approach 1:
The patent performs preliminary action by pre-processing the bulk-cell sequencing data to extract and structure cell-type specific information before final analysis. The system pre-identifies different cell types and their proportions, and pre-structures the RNA data into cell-type specific profiles. This preliminary structuring enables both fast analysis and reliable results by organizing the data in a way that preserves critical biological information while maintaining computational efficiency.
3Measurement precision
If cell-type specific analysis is performed to improve diagnosis precision, then measurement precision is improved, but device complexity increases due to need for advanced computational models
Solution Approach 1:
The patent applies parameter changes by transforming the complex cell-type specific analysis problem into a series of manageable mathematical parameters. The system changes the parameters of the analysis by using optimization models with specific constraints (non-negative proportions summing to one) and by parameterizing the RNA data in terms of cell-type specific profiles. This parameter transformation simplifies the computational complexity while maintaining high diagnosis precision through mathematically rigorous cell-type decomposition.
Data Source
AI summary
Methods for determining a cancer composition of a subject are provided that include generating machine-learning models configured to identify cell types based on respective cell-type RNA expression profiles, and using the models to determine the cancer composition of the subject.