The invention relates to a method based on Morranapos. Path enrichment analysis method and device for s I

By constructing a spatial weight matrix based on the Moran's index and utilizing Moran's I statistic, the problems of topological structure and multi-omics data integration in pathway enrichment analysis were solved, key regulatory nodes were identified, and the accuracy and relevance of the analysis were improved.

CN122157773APending Publication Date: 2026-06-05TSINGHUA UNIVERSITY
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Patent Information

Application Number
CN202610206410.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-02-12
Publication Date
2026-06-05

AI Technical Summary

Technical Problem

Existing pathway enrichment analysis methods ignore pathway topology, cannot integrate multi-omics data, fail to properly handle missing data, and are difficult to identify key regulatory nodes, resulting in insufficient accuracy and relevance of the analysis results.

Method used

Using a masked Moran index-based approach, a spatial weight matrix is ​​constructed, and global and local Moran's I statistics are utilized in conjunction with Monte Carlo permutation tests to identify the spatial autocorrelation and key regulatory nodes of pathways.

Benefits of technology

It effectively integrates pathway topology and multi-omics data, identifies key regulatory nodes, improves the accuracy and relevance of the analysis, and provides more precise information for subsequent experimental verification.

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Abstract

The invention relates to the technical field of bioinformatics and system biology, in particular to a path enrichment analysis method and device based on Moran's I. The method comprises the following steps: firstly, acquiring biological sample data, and extracting molecular identifiers and expression values corresponding to the molecular identifiers; mapping the molecular identifier to a node of a path network in a preset path database, and constructing a spatial weight matrix based on a topological structure of the path network; performing mask processing on nodes which are not mapped to the expression data, and marking observation nodes through mask vectors; extracting a sub-graph based on the mask vector, and calculating the global spatial autocorrelation of the path by using the global Moran's I statistical magnitude; saliency is evaluated through Monte Carlo permutation test, a p value corresponding to a global Moran's I statistic is calculated, and an enrichment path list is determined. By introducing the Moran's I index in space statistics, the expression change and the topological proximity relation of molecules in the pathway can be comprehensively considered, and the defect that the upstream and downstream regulation and control relation cannot be reflected by a traditional enrichment analysis method is overcome.
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