Published:
It’s not impossible that you’ll at some point with to collapse down your multiplex network into a multigraph. RWRtoolkit provides two methods of multiplex network aggregation.
Published:
The RWR_LOE command uses RWR to rank all genes in the multiplex network with respect to a geneset, which provides biological context for the seed genes in the gene set using the multiple lines of evidence (i.e. layers) contained within the multiplex network. The output of RWR_LOE is a matrix with random walk scores, and their associated ranks. Each row additionally contains the number of seeds within the network, the total supplied number of seeds, the network name, the modified name, and the seed gene set name. The connectivity between seed genes and the top n ranking genes can be visualized as a subnetwork in Cytoscape via the RCy3 implementation of CyREST (Gustavsen 2019, Ono 2015) by setting the cyto flag.
Published:
The RWR_LOE command uses RWR to rank all genes in the multiplex network with respect to a geneset, which provides biological context for the seed genes in the gene set using the multiple lines of evidence (i.e. layers) contained within the multiplex network. The output of RWR_LOE is a matrix with random walk scores, and their associated ranks. Each row additionally contains the number of seeds within the network, the total supplied number of seeds, the network name, the modified name, and the seed gene set name. The connectivity between seed genes and the top n ranking genes can be visualized as a subnetwork in Cytoscape via the RCy3 implementation of CyREST (Gustavsen 2019, Ono 2015) by setting the cyto flag. Users can use RWR_LOE output from two separate gene sets on the same multiplex to explore data driven differences between those seed sets of interest.
Published:
This vignette descibes the usage of RWR_CV with respect to differing gene sets and the parsing of RWR_CV’s output.
Published:
This vignette describes the usage of RWR_CV with respect to validating a multiplex network against an independently curated set of highly connected genes.
Published:
With NetStats, you can generate statistics on your networks with a number of different functions, from generating basic network statistics on a paritcular network layer or multiplex to comparing individual networks against other networks or multiplexes. For example, if you have network layer acting as a gold set standard, you can use that network to test against another non-gold set network or multiplex to determine how closely related they are.
Published:
With Shortest Paths the shortest paths function, we generate a list of the shortest paths between two vertices ordered by the number of nodes traversed. Not only may any two vertices be supplied as potential source and target nodes, but entire sets may be supplied as well.
Published:
In order to use any of the features of the RWRtoolkit package, you fill first need a multiplex network. A multiplex network is a topological construction of a series of networks. Instead of collapsing all networks into a single layer, a multiplex will instead maintain each layer’s distinct topology with edges existing between the same node within each layer (i.e. Node A existing in layer 1 will have edges to node A existing in layers 2, 3, and so on).
Published:
The RWR_LOE command uses RWR to rank all genes in the multiplex network with respect to a geneset, which provides biological context for the seed genes in the gene set using the multiple lines of evidence (i.e. layers) contained within the multiplex network. The output of RWR_LOE is a matrix with random walk scores, and their associated ranks. Each row additionally contains the number of seeds within the network, the total supplied number of seeds, the network name, the modified name, and the seed gene set name. The connectivity between seed genes and the top n ranking genes can be visualized as a subnetwork in Cytoscape via the RCy3 implementation of CyREST (Gustavsen 2019, Ono 2015) by setting the cyto flag.
Published:
It’s not impossible that you’ll at some point with to collapse down your multiplex network into a multigraph. RWRtoolkit provides two methods of multiplex network aggregation.
Published:
The RWR_LOE command uses RWR to rank all genes in the multiplex network with respect to a geneset, which provides biological context for the seed genes in the gene set using the multiple lines of evidence (i.e. layers) contained within the multiplex network. The output of RWR_LOE is a matrix with random walk scores, and their associated ranks. Each row additionally contains the number of seeds within the network, the total supplied number of seeds, the network name, the modified name, and the seed gene set name. The connectivity between seed genes and the top n ranking genes can be visualized as a subnetwork in Cytoscape via the RCy3 implementation of CyREST (Gustavsen 2019, Ono 2015) by setting the cyto flag.
Published:
The RWR_LOE command uses RWR to rank all genes in the multiplex network with respect to a geneset, which provides biological context for the seed genes in the gene set using the multiple lines of evidence (i.e. layers) contained within the multiplex network. The output of RWR_LOE is a matrix with random walk scores, and their associated ranks. Each row additionally contains the number of seeds within the network, the total supplied number of seeds, the network name, the modified name, and the seed gene set name. The connectivity between seed genes and the top n ranking genes can be visualized as a subnetwork in Cytoscape via the RCy3 implementation of CyREST (Gustavsen 2019, Ono 2015) by setting the cyto flag. Users can use RWR_LOE output from two separate gene sets on the same multiplex to explore data driven differences between those seed sets of interest.
Published:
This vignette descibes the usage of RWR_CV with respect to differing gene sets and the parsing of RWR_CV’s output.
Published:
This vignette describes the usage of RWR_CV with respect to validating a multiplex network against an independently curated set of highly connected genes.
Published:
With NetStats, you can generate statistics on your networks with a number of different functions, from generating basic network statistics on a paritcular network layer or multiplex to comparing individual networks against other networks or multiplexes. For example, if you have network layer acting as a gold set standard, you can use that network to test against another non-gold set network or multiplex to determine how closely related they are.
Published:
With Shortest Paths the shortest paths function, we generate a list of the shortest paths between two vertices ordered by the number of nodes traversed. Not only may any two vertices be supplied as potential source and target nodes, but entire sets may be supplied as well.
Published:
In order to use any of the features of the RWRtoolkit package, you fill first need a multiplex network. A multiplex network is a topological construction of a series of networks. Instead of collapsing all networks into a single layer, a multiplex will instead maintain each layer’s distinct topology with edges existing between the same node within each layer (i.e. Node A existing in layer 1 will have edges to node A existing in layers 2, 3, and so on).
Published:
It’s not impossible that you’ll at some point with to collapse down your multiplex network into a multigraph. RWRtoolkit provides two methods of multiplex network aggregation.
Published:
The RWR_LOE command uses RWR to rank all genes in the multiplex network with respect to a geneset, which provides biological context for the seed genes in the gene set using the multiple lines of evidence (i.e. layers) contained within the multiplex network. The output of RWR_LOE is a matrix with random walk scores, and their associated ranks. Each row additionally contains the number of seeds within the network, the total supplied number of seeds, the network name, the modified name, and the seed gene set name. The connectivity between seed genes and the top n ranking genes can be visualized as a subnetwork in Cytoscape via the RCy3 implementation of CyREST (Gustavsen 2019, Ono 2015) by setting the cyto flag.
Published:
The RWR_LOE command uses RWR to rank all genes in the multiplex network with respect to a geneset, which provides biological context for the seed genes in the gene set using the multiple lines of evidence (i.e. layers) contained within the multiplex network. The output of RWR_LOE is a matrix with random walk scores, and their associated ranks. Each row additionally contains the number of seeds within the network, the total supplied number of seeds, the network name, the modified name, and the seed gene set name. The connectivity between seed genes and the top n ranking genes can be visualized as a subnetwork in Cytoscape via the RCy3 implementation of CyREST (Gustavsen 2019, Ono 2015) by setting the cyto flag. Users can use RWR_LOE output from two separate gene sets on the same multiplex to explore data driven differences between those seed sets of interest.
Published:
This vignette descibes the usage of RWR_CV with respect to differing gene sets and the parsing of RWR_CV’s output.
Published:
This vignette describes the usage of RWR_CV with respect to validating a multiplex network against an independently curated set of highly connected genes.
Published:
With NetStats, you can generate statistics on your networks with a number of different functions, from generating basic network statistics on a paritcular network layer or multiplex to comparing individual networks against other networks or multiplexes. For example, if you have network layer acting as a gold set standard, you can use that network to test against another non-gold set network or multiplex to determine how closely related they are.
Published:
With Shortest Paths the shortest paths function, we generate a list of the shortest paths between two vertices ordered by the number of nodes traversed. Not only may any two vertices be supplied as potential source and target nodes, but entire sets may be supplied as well.
Published:
In order to use any of the features of the RWRtoolkit package, you fill first need a multiplex network. A multiplex network is a topological construction of a series of networks. Instead of collapsing all networks into a single layer, a multiplex will instead maintain each layer’s distinct topology with edges existing between the same node within each layer (i.e. Node A existing in layer 1 will have edges to node A existing in layers 2, 3, and so on).