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rwrtoolkit  (DOE Systems Biology Knowledgebase)
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DOE Systems Biology Knowledgebase rwrtoolkit
A general workflow for using the <t>RWRtoolkit.</t> (a) Illustration of how a user can generate several network layers from different omics data sources, which become input to the RWRtoolkit workflow. Once the user has networks in the correct format, they can then refer to them via a flist file and use RWR_make_multiplex to turn them into a homogeneous multiplex network (e.g., multiple layers of gene-to-gene relationships). This multiplex is wrapped in an Rdata object that is saved for future use. (b) A demonstration of how the user can now execute a variety of multi-omic analyses, most of which require the Rdata object as input. A set of genes of interest (gene set) from discovery studies such as GWAS or differential expression analysis can be used as input to multiple tools. These tools output a variety of files that show how functionally connected the genes in the gene set are to each other, or to a second gene set of interest, or to all the other genes in the multiplex. Some resulting networks can be automatically visualized in Cytoscape via the RCy3 R package . This figure uses illustrations created with BioRender.com .
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A general workflow for using the RWRtoolkit. (a) Illustration of how a user can generate several network layers from different omics data sources, which become input to the RWRtoolkit workflow. Once the user has networks in the correct format, they can then refer to them via a flist file and use RWR_make_multiplex to turn them into a homogeneous multiplex network (e.g., multiple layers of gene-to-gene relationships). This multiplex is wrapped in an Rdata object that is saved for future use. (b) A demonstration of how the user can now execute a variety of multi-omic analyses, most of which require the Rdata object as input. A set of genes of interest (gene set) from discovery studies such as GWAS or differential expression analysis can be used as input to multiple tools. These tools output a variety of files that show how functionally connected the genes in the gene set are to each other, or to a second gene set of interest, or to all the other genes in the multiplex. Some resulting networks can be automatically visualized in Cytoscape via the RCy3 R package . This figure uses illustrations created with BioRender.com .

Journal: GigaScience

Article Title: RWRtoolkit: multi-omic network analysis using random walks on multiplex networks in any species

doi: 10.1093/gigascience/giaf028

Figure Lengend Snippet: A general workflow for using the RWRtoolkit. (a) Illustration of how a user can generate several network layers from different omics data sources, which become input to the RWRtoolkit workflow. Once the user has networks in the correct format, they can then refer to them via a flist file and use RWR_make_multiplex to turn them into a homogeneous multiplex network (e.g., multiple layers of gene-to-gene relationships). This multiplex is wrapped in an Rdata object that is saved for future use. (b) A demonstration of how the user can now execute a variety of multi-omic analyses, most of which require the Rdata object as input. A set of genes of interest (gene set) from discovery studies such as GWAS or differential expression analysis can be used as input to multiple tools. These tools output a variety of files that show how functionally connected the genes in the gene set are to each other, or to a second gene set of interest, or to all the other genes in the multiplex. Some resulting networks can be automatically visualized in Cytoscape via the RCy3 R package . This figure uses illustrations created with BioRender.com .

Article Snippet: We have additionally integrated RWRtoolkit with a graphical user interface (GUI) into the Department of Energy’s (DOE) Knowledge Base (KBase) system [ ].

Techniques: Multiplex Assay, Quantitative Proteomics

RWRtoolkit multiplex visualizations. (a) Using the RWR_LOE function with the –cyto flag, users can specify the total number of nodes they wish to see in the Cytoscape visualization. The total multiplex is aggregated for visualization purposes with all edge types maintained for visual styling. (b) The RWR_LOE function within the KBase application automatically visualizes the network output, illustrating the layers from which each edge came on the same screen as the RWR_LOE node score output. (c) RWR_ShortestPaths finds the shortest paths between any two sets of nodes and plots the subnetwork into Cytoscape.

Journal: GigaScience

Article Title: RWRtoolkit: multi-omic network analysis using random walks on multiplex networks in any species

doi: 10.1093/gigascience/giaf028

Figure Lengend Snippet: RWRtoolkit multiplex visualizations. (a) Using the RWR_LOE function with the –cyto flag, users can specify the total number of nodes they wish to see in the Cytoscape visualization. The total multiplex is aggregated for visualization purposes with all edge types maintained for visual styling. (b) The RWR_LOE function within the KBase application automatically visualizes the network output, illustrating the layers from which each edge came on the same screen as the RWR_LOE node score output. (c) RWR_ShortestPaths finds the shortest paths between any two sets of nodes and plots the subnetwork into Cytoscape.

Article Snippet: We have additionally integrated RWRtoolkit with a graphical user interface (GUI) into the Department of Energy’s (DOE) Knowledge Base (KBase) system [ ].

Techniques: Multiplex Assay