getPSN: get the integrated patient similarity network made of...

In BaderLab/netDx: Network-based patient classifier

Description

get the integrated patient similarity network made of selected features

Usage

getPSN(
  dat,
  groupList,
  makeNets,
  selectedFeatures,
  plotCytoscape = FALSE,
  aggFun = "MEAN",
  prune_pctX = 0.3,
  prune_useTop = TRUE,
  numCores = 1L,
  calcShortestPath = FALSE
)

Arguments

dat	(MultiAssayExperiment) input data
groupList	(list) feature groups, identical to groupList provided for buildPredictor()
makeNets	(function) Function used to create patient similarity networks. Identical to makeNets provided to buildPredictor()
selectedFeatures	(list) selected features for each class (key of list). This object is returned as part of a call to getResults(), after running buildPredictor().
plotCytoscape	(logical) If TRUE, plots network in Cytoscape. Requires Cytoscape software to be installed and running on the computer when the function call is being made.
aggFun	(char) function to aggregate edges from different PSN (e.g. mean)
prune_pctX	(numeric between 0 and 1) fraction of most/least edges to keep when pruning the integrated PSN for visualization. Must be used in conjunction with useTop=TRUE/FALSE e.g. Setting pctX=0.2 and useTop=TRUE will keep 20% top edges
prune_useTop	(logical) when pruning integrated PSN for visualization, determines whether to keep strongest edges (useTop=TRUE) or weakest edges (useTop=FALSE)
numCores	(integer) number of cores for parallel processing
calcShortestPath	(logical) if TRUE, computes weighted shortest path Unless you plan to analyse these separately from looking at the shortest path violin plots or integrated PSN in Cytoscape, probably good to set to FALSE.

Details

An integrated patient similarity network can be built using combined top features for each patient class. Such a network is created by taking the union of selected features for all patient labels, and aggregating pairwise edges for all of them using a user-specified function (aggFun). The network is then pruned prior to visualization, using a user-specified fraction of strongest edges (prune_pctX, prune_useTop). In addition, the user may quantify the distance between patients of the same class, relative to those of other classes, using Dijkstra distance (calcShortestPath flag).

Value

(list) information about the integrated network similarity network 2) patientDistNetwork_pruned (matrix) the network plotted in Cytoscape. Also note that this is a dissimilarity network, so that more similar nodes have smaller edge weights 3) colLegend (data.frame): legend for the patient network plotted in Cytoscape. Columns are node labels (STATUS) and colours (colour) 6) outDir (char) value of outDir parameter

BaderLab/netDx documentation built on Sept. 26, 2021, 9:13 a.m.