Compare reaction fluxes with gene expression values to identify metabolic pathways where expression and flux data agree or conflict.
This method evaluates the agreement/disagreement of a flux distribution against overall gene expression and organizes the results based on KEGG metabolic pathways that represent the entire metabolic model. For metabolic modeling related questions, please refer to the metabolic modeling FAQ.
The following inputs are required: a flux balance analysis (FBA), a solution, and a gene expression data set. Please note that for optimum outcome the FBA solution needs to be generated against either the same or a similar media formulation as the expression data was originally generated.
Upon successful completion, the App creates a pathway analysis data object of type FBAPathwayAnalysis and creates a visual summary via horizontal bar graphs. These bar graphs are generated by decomposing model reactions into KEGG pathways. Bars represent each the combinations of presence or absence of reactions or flux:
- Active gap-filled reactions
- GAR active flux but no expression
- GAR no flux but active expression
- GAR no flux or expression
- GAR active flux and expression
Note that GAR stands for Gene Associated Reactions.
Active expression is defined by log2(fold_change) > expression fold-change threshold (input parameter).
The bar graph visualization is generated using the Plotly library. Icon widgets in the top right allow the user to change the graph scale, download the image as a .png file, and access other functionalities offered through Plotly.
Team members who developed & deployed algorithm in KBase: Chris Henry, Janaka Edirisinghe, and Neal Conrad. For questions, please contact us.
- Arkin AP, Cottingham RW, Henry CS, Harris NL, Stevens RL, Maslov S, et al. KBase: The United States Department of Energy Systems Biology Knowledgebase. Nature Biotechnology. 2018;36: 566. doi: 10.1038/nbt.4163 , https://www.nature.com/articles/nbt.4163
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