tuneEdgeWeights: Tune edge weights

In PFPrzytycki/CellWalkR: An R Package for Integrating and Visualizing Single-Cell and Bulk Data to Resolve Regulatory Elements

Tune edge weights

Description

tuneEdgeWeights generates cell homogeneity scores for possible edge weight settings across all labelEdge matrices. To speed up calculations cells can be subsampled to a specified sampleDepth (default 10000).

Usage

tuneEdgeWeights(
  cellEdges,
  labelEdgesList,
  labelEdgeOpts = 10^seq(-4, 4, 1),
  sampleDepth = 10000,
  steps,
  cellTypes,
  parallel = FALSE,
  numCores = 1,
  trackProgress = FALSE,
  tensorflow = FALSE
)

Arguments

cellEdges	matrix of cell-to-cell similarity (c-by-c)
labelEdgesList	list of cell-to-label similarity matrices each with dimensions c-by-l
labelEdgeOpts	vector of weights to test for label edges
sampleDepth	integer depth to subsample cells to for faster calculation
steps	integer indicating number of steps to take if walk should not be run to convergence
cellTypes	list of names of cell types, if not provided unique cell labels are used
parallel	boolean, execute in parallel
numCores	integer, number of cores to use for parallel execution
trackProgress	boolean, print percent run completed
tensorflow	boolean to indicate whether to compute on GPU

Value

data frame of weights with corresponding cell homogeneity

Examples

data("SampleCellWalkRData")
labelEdgesList <- list(SampleCellWalkRData$labelEdges)
tuneEdgeWeights(SampleCellWalkRData$cellEdges, labelEdgesList, 10^seq(1,7,1))

PFPrzytycki/CellWalkR documentation built on Oct. 26, 2023, 1:50 p.m.