Functional Genomic Imaging Through Gene Filtering and Cluster Analysis
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Solution Overview
Problem
Existing RNA sequencing technologies face challenges in effectively processing and visually displaying the vast amount of RNA data, particularly in identifying and visualizing gene expression patterns and correlations.
Innovation Solution
A Functional Genomic Imaging (FGI) method that filters genes based on expression levels, generates a correlation matrix for co-expression networks, performs data conversion and dimensionality reduction, and uses cluster analysis to obtain visualized gene expression, incorporating data calibration to address batch effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If RNA sequencing data is processed without filtering, then all gene information is retained, but the data contains excessively enormous information that is difficult to analyze and visualize
Solution Approach 1:
The patent extracts and removes non-coding genes and housekeeping genes from the RNA sequencing data through filtering. This extraction of specific gene types reduces the enormous information volume while retaining the most biologically relevant coding genes, making the data manageable for analysis and visualization without losing critical information.
Solution Approach 2:
The patent applies different filtering criteria to different gene types within the dataset. By selectively removing non-coding genes and housekeeping genes while retaining coding genes, the method creates a locally optimized dataset with appropriate quality for specific analytical purposes, balancing information retention with analysis feasibility.
2Illumination intensity
If dimensionality reduction is performed on the full gene dataset, then visualization is achieved, but the visualization accuracy and cluster detection effectiveness deteriorate due to noise from non-effective genes
Solution Approach 1:
The patent performs gene filtering as a preliminary action before dimensionality reduction and cluster analysis. By pre-processing the data to remove non-coding and housekeeping genes, the method prepares a cleaned dataset that enhances the accuracy of subsequent clustering algorithms and improves visualization clarity without introducing noise from irrelevant genes.
Solution Approach 2:
The patent replaces traditional mechanical filtering approaches with sophisticated computational methods including correlation matrix calculation, distance matrix transformation, and advanced clustering algorithms. This substitution enables more precise identification of co-expressed gene clusters while maintaining visualization effectiveness.
3Ease of manufacture
If batch effects are not calibrated, then data processing is simpler, but the stability and accuracy of data interpretation deteriorate
Solution Approach 1:
The patent introduces ComBat-seq as an intermediary computational tool to calibrate batch effects in the RNA sequencing data. This mediator algorithm adjusts and harmonizes data from different batches or experimental conditions, ensuring stable and accurate data interpretation while maintaining relative processing simplicity through automated batch effect correction.
Data Source
AI summary
The present disclosure discloses a Functional Genomic Imaging method. Genes are filtered based on the expression levels and other information first to improve the ratio of effective genes, and then a correlation matrix of gene co-expression is generated based on the filtered gene data to obtain a co-expression network; data conversion is performed based on the correlation matrix of gene co-expression to obtain a distance matrix; and finally, the co-expression network is subjected to dimensionality reduction and cluster analysis, thus obtaining the visualized gene expression according to the analysis result. The Functional Genomic Imaging method provided by the present disclosure achieves a better visualization effect of the genetic co-expression network and can obtain a co-expressed gene cluster more effectively.