ANIMA: Association network integration for multiscale analysis.

Area of Science:

• Computational systems biology
• Bioinformatics
• Genomics

Background:

• Interpreting large-scale -omics data from clinical samples is challenging.
• Bulk transcriptomics data from complex tissues like blood are common but difficult to analyze.
• Existing methods often focus on molecular signatures or modular analysis.

Purpose of the Study:

• To present ANIMA (Association Network Integration for Multiscale Analysis), a novel network-based data integration method.
• To enable contextual functional interpretation of -omics data by integrating clinical phenotype and microarray data.
• To provide a scalable and interactive platform for analyzing complex biological datasets.

Main Methods:

• ANIMA utilizes a network-based approach with R, Neo4j, and Docker containers.
• The build algorithm integrates differential expression, deconvolution, co-expression, and pathway analyses.
• A Shiny web application provides interactive, multiscale data visualization and querying via Cypher or APIs.

Main Results:

• ANIMA successfully reconstructs multiple features of disease states at various organizational scales.
• The method demonstrates efficacy in analyzing individual experiments and meta-analyses of multiple datasets.
• It allows for the examination of patterns from individual gene transcript abundance to cellular behavior in whole blood.

Conclusions:

• ANIMA offers a powerful, integrated approach for the functional interpretation of complex -omics data.
• The network-based strategy facilitates a multiscale understanding of pathobiology.
• This method advances computational systems biology by bridging big data with clinical relevance.