A growing body of evidence shows that when performing differential analysis it is highly beneficial to go beyond differences in the level of individual genes, and consider differences in their interactions as well. We propose an original statistical approach that identifies the set of variables driving the difference between two conditions under study. Our proposal, set within the framework of Gaussian graphical models, is implemented in the R package SourceSet, that also extends the analysis from a single to multiple pathways and provides several graphical outputs, including Cytoscape visualization to browse the results.

Searching for the Source of Difference: A Graphical Model Approach

Chiogna, Monica;
2020

Abstract

A growing body of evidence shows that when performing differential analysis it is highly beneficial to go beyond differences in the level of individual genes, and consider differences in their interactions as well. We propose an original statistical approach that identifies the set of variables driving the difference between two conditions under study. Our proposal, set within the framework of Gaussian graphical models, is implemented in the R package SourceSet, that also extends the analysis from a single to multiple pathways and provides several graphical outputs, including Cytoscape visualization to browse the results.
Computational Intelligence Methods for Bioinformatics and Biostatistics.
131
138
LECTURE NOTES IN ARTIFICIAL INTELLIGENCE
Djordjilović, Vera; Chiogna, Monica; Romualdi, Chiara; Salviato, Elisa
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/717444
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