Cell Subset Deconvolution Using SVR for Noisy Tissue Profiles
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Solution Overview
Problem
Existing methods for studying cell heterogeneity, such as immunohistochemistry and flow cytometry, rely on limited phenotypic markers and can lead to altered results due to tissue disaggregation, and computational methods struggle with mixtures of unknown content and noise, especially in solid tumors, and fail to discriminate closely related cell types effectively.
Innovation Solution
A method involving optimizing a regression between a feature profile of a sample and a reference matrix of feature signatures using support vector regression (SVR) to minimize a linear loss function and L2-norm penalty, allowing for accurate estimation of fractional representations of distinct components in the sample.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computational methods are used to predict cell fractions in mixtures with unknown content and noise, then measurement precision is improved, but reliability deteriorates due to inability to discriminate closely related cell types
Solution Approach 1:
The patent transforms the cell fraction prediction problem into a statistical hypothesis testing framework. By changing the approach from direct prediction to testing whether predicted fractions differ significantly from null predictions, the method achieves reliable discrimination of closely related cell types while maintaining measurement precision in noisy conditions.
Solution Approach 2:
The patent implements an iterative hypothesis testing process where statistical significance tests provide feedback on the reliability of predictions. The method continuously evaluates whether predicted cell fractions are statistically distinguishable from random expectations, allowing the system to refine its discrimination capability based on statistical feedback.
2Ease of operation
If tissue disaggregation is performed prior to flow cytometry, then ease of operation is improved, but loss of substance occurs due to lost or damaged cells
Solution Approach 1:
The patent replaces mechanical tissue disaggregation methods with computational deconvolution of gene expression profiles. Instead of physically breaking apart tissue which damages cells, the method uses mathematical models to infer cell composition from intact tissue transcriptome data, eliminating cell loss while maintaining analytical capability.
Solution Approach 2:
The patent introduces gene expression profiles as an intermediary between tissue structure and cell composition analysis. Rather than directly analyzing physical tissue structures that require disruptive disaggregation, the method uses transcriptomic intermediaries that can be measured in intact tissue, thereby avoiding cell damage while still enabling cell type identification.
3Device complexity
If limited phenotypic markers are used in immunohistochemistry and flow cytometry, then device complexity is reduced, but measurement precision deteriorates due to inability to accurately quantify cell heterogeneity
Solution Approach 1:
The patent transitions from two-dimensional phenotypic marker analysis to high-dimensional gene expression profile analysis. By moving from limited markers to comprehensive transcriptomic profiles, the method gains the ability to accurately quantify cell heterogeneity while the computational deconvolution algorithms manage the increased dimensionality, effectively resolving the complexity-precision tradeoff.
Data Source
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.