writeWeightedNets: Write an integrated similarity network consisting of selected...
In BaderLab/netDx: Network-based patient classifier

Write an integrated similarity network consisting of selected networks.

writeWeightedNets(
  patientIDs,
  netIDs,
  netDir,
  keepNets,
  filterEdgeWt = 0,
  aggNetFunc = "MAX",
  limitToTop = 50L,
  plotEdgeDensity = FALSE,
  verbose = FALSE
)

`patientIDs`	(data.frame) patient identifiers. Columns include internally-generated identifiers (GM_ID) and user-provided identifiers (ID)
`netIDs`	(data.frame) network metadata. Columns include internal network name (NET_ID), user-provided name (NETWORK). If a third optional column named "isBinary" is provided, and contains binary values (i.e. 1 and 0), that indicates that the network contains only binary weights and an alternate similarity computation (PropBinary) will be used (see description).
`netDir`	(char) path to directory containing interaction networks. Note that these are networks where the node IDs have been recoded by GeneMANIA (e.g. 1,2,3)
`keepNets`	(char or data.frame) networks to include in integrated net If data.frame must be in "NETWORK" column,other columns will be ignored. Mainly included as convenience so pathway scores can passed in table format (NETWORK), and a multiplier constant for edges in that network (WEIGHT)
`filterEdgeWt`	(numeric) keep edges with raw edge weight strictly greater than this value. Note that "raw" refers to this filter being applied before the multiplier is applied.
`aggNetFunc`	(char, one of: [MEAN\|MAX]) Aggregate the network 2) MEAN: average of weighted edges (raw x netDx score) 3) MAX: max of raw edge weight
`limitToTop`	(integer) limit to top strongest connections. Set to Inf to list all connections.
`plotEdgeDensity`	(logical) plot density plot of edge weights, one per input net. Used to troubleshoot problems introduced by specific nets.
`verbose`	(logical) print messages if TRUE

(list) 1) filterEdgeWt (numeric) Value of filterEdgeWt parameter 2) aggNetFunc (char) Value of aggNetFunc parameter 3) limitToTop (integer) Value of limitToTop parameter 4) aggNet (matrix) Value of limitToTop parameter File format is: 1) source patient (SOURCE) 2) target patient (TARGET) 3) network name (NET_NAME) 4) weight similarity for the network (WT_SIM)

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