Transcriptome Correlation Index for Gene Expression Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current gene expression analyses are limited by high variation in expression profiles when analyzing genetically diverse specimens, particularly in identifying differentially expressed genes associated with disease pathogenesis, as they focus on the magnitude of over- or under-expression rather than coordination of gene expression.
Innovation Solution
A transcriptome correlation method is developed, calculating a composite correlation index to assess the coordination or loss of coordination in gene expression, using pairwise comparisons and coupling with gene ontology platforms to identify biological implications and disease-related changes, even in the absence of histopathological evidence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional differential expression analysis is used to identify disease-related genes, then the magnitude of over- or under-expression can be measured, but the high variation in expression profiles between genetically diverse specimens limits the reliability of identifying truly disease-associated genes
Solution Approach 1:
The patent changes the analytical parameter from measuring absolute expression magnitude to measuring coordination patterns among gene transcripts. By calculating composite correlation indexes that assess how groups of genes co-express across specimens, the method transforms the data analysis approach to overcome the limitation of high inter-specimen variation, thereby improving both measurement precision and reliability in identifying disease-associated genes
Solution Approach 2:
The patent introduces composite correlation indexes as an intermediary metric between raw gene expression data and disease gene identification. These indexes serve as a mediator that captures coordination patterns across multiple transcripts, filtering out the noise of individual gene variation while preserving the signal of coordinated dysregulation associated with disease states
2Ease of operation
If focus is placed on the magnitude of differential expression to identify disease genes, then simple comparison between groups is possible, but subtle coordinated changes in gene expression are missed
Solution Approach 1:
The patent merges information from multiple gene transcripts by calculating composite correlation indexes that evaluate coordinated expression patterns across groups of genes. This combining approach preserves subtle changes that would be invisible in individual gene analysis, while the integration across multiple transcripts provides a more robust signal that maintains analytical simplicity
3Quantity of substance
If whole transcriptome analysis is performed to capture comprehensive molecular changes, then more complete biological information is obtained, but the complexity of analyzing and interpreting the data increases significantly
Solution Approach 1:
The patent segments the complex whole transcriptome data into analyzable units by calculating composite correlation indexes for groups of transcripts. This segmentation approach breaks down the overwhelming complexity of analyzing all genes simultaneously into manageable correlation calculations, while still capturing comprehensive molecular changes through the aggregate analysis of coordinated gene groups
Data Source
AI summary
Systems, methods and diagnostic tools based a paradigm on how the diversity in expression profiles of primary specimens could be leveraged for target discovery via evaluating transcriptomes that lose coordination between the disease carrying and control groups and assessing the biological functions that are acquired in the former group.