AI Deconvolution for Immune Cell State Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods struggle to accurately analyze complex data signals from heterogeneous cancer tissue samples, particularly in determining the status of immune cells like TILs, due to signal distortion and interference from multiple sources.
Innovation Solution
A computer-implemented system using machine learning deconvolution algorithms, such as linear least-squares regression and support vector regression, to process RNA-seq data and identify distinct signal sources, enabling the classification of immune cell states and optimizing immunotherapy regimens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deconvolution algorithms are applied to heterogeneous tissue samples, then measurement precision of immune cell status is improved, but device complexity increases
Solution Approach 1:
The patent introduces deconvolution algorithms as intermediary computational tools that process the complex heterogeneous signals from tissue samples. These algorithms act as mediators between the raw confounded signals and the meaningful immune cell status information, separating the mixed signals to reveal the underlying cellular composition and states without requiring direct physical separation of cells
Solution Approach 2:
The patent replaces traditional mechanical or physical methods of cell separation and analysis with computational deconvolution algorithms. Instead of physically isolating immune cells from heterogeneous tissue samples through complex laboratory procedures, the system uses mathematical algorithms to computationally deconvolve the mixed signals, substituting mechanical complexity with information processing
2Loss of information
If multiple signal sources are deconvolved, then information completeness is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The patent applies segmentation by breaking down the complex heterogeneous signal into distinct component signals representing different cell populations and states. The deconvolution algorithm segments the mixed signal into individual contributions from various immune cell types, enabling complete information recovery about each cell population's presence and state without being overwhelmed by the complexity of the original mixed signal
3Productivity
If machine learning classifiers are used for status classification, then productivity of analysis is improved, but measurement precision may be compromised
Solution Approach 1:
The patent applies preliminary action by first performing deconvolution to extract pure signal components and identify distinct signal sources before applying machine learning classification. This preliminary processing step prepares the data by separating confounded signals and organizing them into meaningful categories, which then enables the machine learning classifier to work with pre-processed, more informative features, maintaining both speed and accuracy
Data Source
AI summary
Disclosed herein, are systems and methods for analyzing complex data signals using artificial intelligence and/or deconvolution algorithms to determine output pertaining to the state or status of one or more parameters. Data sets may include signals from various sources that can confound or distort the signals of interest. Accordingly, disclosed herein are deconvolution algorithms that enable the determination of the status of sources that correspond to the signals of interest.