Gene Expression Profile Analysis for Tissue Classification
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Solution Overview
Problem
Current methods for analyzing microarray gene expression data, particularly in cancer diagnostics, struggle to compare single samples against reference databases effectively, often requiring predefined gene sets and control groups, which limits their ability to provide comprehensive and accurate interpretations, especially for personalized diseases like cancer where each case can have unique genetic mutations.
Innovation Solution
The Alignment of Gene Expression Profiles (AGEP) method, which calculates expression match scores and tissue specificity scores to compare a query sample against a large reference database, allowing for the identification of tissue categories and genes that define similarities without prior assumptions, enabling diagnosis and treatment recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If case-control study design is used to analyze microarray data, then statistically significant interpretation of differentially expressed genes is achieved, but interpretation of data from individual samples becomes impossible
Solution Approach 1:
The invention segments the reference database into multiple tissue categories with distinct gene expression profiles. Each category is characterized by a set of marker genes that define its biological identity. This segmentation allows individual samples to be compared against specific tissue categories rather than requiring large case-control groups, enabling both statistical reliability and individual sample interpretation.
Solution Approach 2:
The invention introduces tissue category profiles as intermediary references between individual samples and known biological states. These profiles serve as mediators that enable comparison of single samples against comprehensive reference databases, allowing interpretation without requiring matched control samples for each individual case.
2Ease of manufacture
If predefined gene sets and control groups are used, then analysis framework is established, but ability to handle unique genetic mutations in personalized diseases is limited
Solution Approach 1:
The invention dynamically adapts the reference framework by allowing individual samples to define their own characteristic gene expression profiles. Rather than forcing samples into predefined categories, the system enables samples to be compared against multiple tissue categories simultaneously, revealing unique patterns that may indicate personalized disease states or novel tissue types.
Solution Approach 2:
The invention creates a universal reference framework where the same tissue category profiles can be applied across different diseases and sample types. The marker gene sets and comparison methods are universally applicable, allowing the system to handle both well-known diseases and previously uncharacterized conditions using the same analytical approach.
3Quantity of substance
If large-scale databases from publicly available microarray datasets are used, then comprehensive reference samples are available, but no tools are available for comparing single samples against these databases
Solution Approach 1:
The invention performs preliminary organization of the reference database by pre-calculating and storing tissue category profiles, marker gene sets, and expression level thresholds. This preliminary processing transforms the raw comprehensive database into a structured format that enables straightforward single-sample comparison, making the large-scale reference accessible and easy to use without requiring complex real-time analysis.
Data Source
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AI summary
An aspect of the present invention is a computer executable method for characterizing, e.g. for diagnostic purposes, utilizing a reference database, a query sample tissue based on the gene expression data of the tissue. The method is characterized in that it comprises the steps of calculating an expression match score (EM-score) indicating the likelihood of having the gene expression level observed in the query sample in each of the tissue categories of the reference database, calculating for the genes of the sample tissue, using e.g. the EM- score, tissue specificity score (TS-score), that expresses how uniquely a gene identifies the query sample as belonging to a certain tissue category, calculating, utilizing e.g. the TS- score, overall similarity of the sample tissue in relation to a tissue category of the reference database, and storing at least some resulting characterization data to a memory device or outputting the data to an output device of a computer. An arrangement and a computer program product are also disclosed.