Cell Subset Proportion Deconvolution with Regularized SVR

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Solution Overview

Problem

Existing methods for studying cell heterogeneity, such as immunohistochemistry and flow cytometry, are limited by reliance on a narrow repertoire of phenotypic markers and can lead to altered results due to tissue disaggregation, and computational methods struggle with mixtures of unknown content and noise in solid tumors, particularly in discriminating closely related cell types.

Innovation Solution

A method involving optimizing a regression between a feature profile and a reference matrix of feature signatures, minimizing a linear loss function and an L2-norm penalty, to estimate the fractional representation of distinct components in a sample, using support vector regression (SVR) and iterative optimization with different values of ν to select the lowest error solution.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If computational methods are used to predict cell type fractions in solid tumors, then measurement precision is improved, but reliability deteriorates due to unknown content and noise

Engineering Contradiction:

Improveprediction accuracyVSAvoidresult validity

Core Design Contradiction:

Measurement precisionVSReliability

Solution Approach 1:

The patent implements statistical significance testing as a feedback mechanism to validate deconvolution results. By calculating p-values and comparing them against thresholds, the method provides reliability feedback to confirm whether predicted cell type fractions are statistically significant, thereby addressing the reliability issue in noisy solid tumor data.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent transforms the deconvolution problem by changing parameters through regularization techniques and statistical testing frameworks. This allows the method to handle unknown content and noise by adjusting mathematical parameters to stabilize predictions and confirm their validity through significance testing.

Inventive Principle:

Principle #35Parameter changes

2Productivity

If computational methods are used to predict cell type fractions, then productivity is improved, but measurement precision deteriorates for closely related cell types

Engineering Contradiction:

Improveanalysis speedVSAvoiddiscrimination accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent segments the deconvolution process into distinct stages: reference profile generation, deconvolution calculation, and statistical significance testing. This segmentation allows computational efficiency in the mathematical operations while dedicating specific steps to precision through significance testing, thereby maintaining both productivity and measurement precision.

Inventive Principle:

Principle #1Segmentation

3Ease of operation

If tissue disaggregation is performed prior to flow cytometry, then ease of operation is improved, but loss of information increases due to cell damage

Engineering Contradiction:

Improvesample processingVSAvoidcell integrity

Core Design Contradiction:

Ease of operationVSLoss of information

Solution Approach 1:

The patent replaces mechanical tissue disaggregation methods with computational deconvolution. Instead of physically breaking down tissue which damages cells and loses information, the method uses mathematical algorithms to computationally separate cell type signatures from bulk tissue data, thereby maintaining cell integrity while achieving the same analytical goal.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

4Device complexity

If a limited repertoire of phenotypic markers is used, then device complexity is reduced, but measurement precision deteriorates

Engineering Contradiction:

Improvemarker set sizeVSAvoidcell type discrimination

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The patent creates a universal computational framework that can handle multiple cell types and markers through a single deconvolution algorithm. The reference profile matrix can incorporate any number of phenotypic markers, and the same computational method universally applies to different cell types, thereby achieving high measurement precision without increasing operational complexity.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20250263800A1Methods and Systems for Determining Proportions of Distinct Cell Subsets

Publication Date: 2025.08.21 THE BOARD OF TRUSTEES OF THE LELAND STANFORD JUNIOR UNIV

AI summary

Methods of deconvolving a feature profile of a physical system are provided herein. The present method may include: optimizing a regression between a) a feature profile of a first plurality of distinct components and b) a reference matrix of feature signatures for a second plurality of distinct components, wherein the feature profile is modeled as a linear combination of the reference matrix, and wherein the optimizing includes solving a set of regression coefficients of the regression, wherein the solution minimizes 1) a linear loss function and 2) an L2-norm penalty function; and estimating the fractional representation of one or more distinct components among the second plurality of distinct components present in the sample based on the set of regression coefficients. Systems and computer readable media for performing the subject methods are also provided.