DECODER NMF Deconvolution of Tumor Gene Expression Data
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Solution Overview
Problem
Current methods for analyzing tumor gene expression in bulk tumor samples are confounded by nonneoplastic cell types and lack efficient tools for separating tumor microenvironment contributions, with limitations in laser-capture microdissection, single-cell sequencing, and computational deconvolution techniques, particularly in pancreatic ductal adenocarcinoma (PDAC) characterized by low tumor purity and high stroma content.
Innovation Solution
The DECODER framework employs nonnegative matrix factorization (NMF) for de novo deconvolution of tumor samples, automatically determining the number of factors and estimating compartment weights without prior knowledge, using a stable gene weight seed and multiplicative update solver to generate gene and compartment weight matrices, enabling accurate identification of tumor subtypes and compartment-specific signatures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If laser-capture microdissection is used to separate tumor cells from nonneoplastic cells, then compartment separation is improved, but labor intensity and quality degradation increase
Solution Approach 1:
The patent replaces the mechanical laser-capture microdissection system with a computational deconvolution algorithm that processes bulk tumor gene expression data. The NMF-based DECODER method computationally separates tumor and stromal compartments without physical manipulation, eliminating labor-intensive manual operations while maintaining compartment separation accuracy through mathematical factorization of the gene expression matrix.
2Measurement precision
If single-cell sequencing is used to analyze tumor heterogeneity, then compartment resolution is improved, but cost and computing resources increase
Solution Approach 1:
The patent creates a computational model that copies and simulates single-cell resolution analysis using bulk tumor data. The NMF algorithm generates virtual single-cell profiles by factorizing the bulk expression matrix, producing compartment-specific gene expression signatures without actually sequencing individual cells. This approach achieves comparable compartment resolution while consuming minimal computing resources compared to genuine single-cell sequencing.
3Productivity
If computational deconvolution methods are used to estimate compartment fractions, then productivity is improved, but measurement precision deteriorates due to incomplete compartment knowledge
Solution Approach 1:
The DECODER algorithm performs self-service by automatically determining the number of compartments and their identities without requiring external input about compartment composition. The method uses the gene expression data itself to identify compartment-specific marker genes and determine the optimal number of factors through internal validation metrics, eliminating the need for researchers to pre-specify compartment knowledge while maintaining accurate compartment fraction estimation.
4Adaptability or versatility
If NMF algorithm is used for de novo deconvolution, then adaptability is improved, but device complexity increases due to automatic K determination
Solution Approach 1:
The patent implements automatic determination of the number of compartments (K) by dynamically adjusting this parameter based on the specific cancer type and data characteristics. The method tests multiple K values and selects the optimal number using silhouette scores and biological validation, allowing the algorithm to adapt to different cancer types without manual configuration. This parameter automation increases adaptability across cancer types while the modular implementation keeps computational complexity manageable.
Data Source
AI summary
Provided are methods for de novo deconvoluting of datasets with multiple samples and/or estimating compartment weights for single samples. In some embodiments, the methods include applying the process or processes to a dataset with multiple samples or to a single sample, whereby the dataset is deconvoluted and/or a compartment weight is estimated. In some embodiments, the dataset relates to RNA sequence and/or gene expression data, optionally tumor RNA sequence and/or gene expression data, wherein the tumor is in some embodiments a pancreatic tumor, a prostate cancer, a bladder cancer, or a breast cancer.