getSoftThreshold2: Analysis of scale free topology for soft-threshold. Modified...

In NMikolajewicz/scMiko: scRNAseq analysis functions. Developed using Seurat framework.

Analysis of scale free topology for soft-threshold. Modified from getSoftThreshold.

Description

Analysis of scale free topology for multiple soft thresholding powers. The aim is to help the user pick an appropriate soft-thresholding power for network construction. Inspired by WGCNA::pickSoftThreshold and updated from first version (scMiko::getSoftThreshold)

Usage

getSoftThreshold2(
  s.mat,
  power = c(seq(0.5, 5, by = 0.5), seq(6, 10)),
  network.type = "signed",
  nBreaks = 20,
  removeFirst = T,
  rescale.adjacency = F,
  n.cores = 4,
  r2.target = 0.9
)

Arguments

s.mat	similarity matrix
power	Numeric vector of powers to evaluate. Default is c(seq(0.5,5, by = 0.5), seq(6,10))
nBreaks	Number of bins in connectivity histograms. Default is 20.
removeFirst	Logical specifying whether the first bin should be removed from the connectivity histogram. Default is True.
rescale.adjacency	Logical indicating if s.mat should be rescaled to [0,1]
n.cores	Number of cores to use for parallelization. Default is 4.
r2.target	Retrieves soft power that corresponds to network topology corresponding to r2 >= r2.target. Default is 0.9.
networkType	Allowed values are (unique abbreviations of) "unsigned", "signed", "signed hybrid". See WGCNA::adjacency.

Value

named list containing power estimates, r2 estimates, distribution plots, optimiation plot and results data.frame.

See Also

getConnectivity

Examples

# determine optimal soft threshold
sft <- getSoftThreshold2(s.mat, power =c(seq(0.5,5, by = 0.5), seq(6,10)),
network.type = "signed", rescale.adjacency = F)

# visualize optimization plot
print(sft$optimization.plot)

# visualize node-linkage density plots
cowplot::plot_grid(plotlist = sft$distribution.plot, ncol = 5)

NMikolajewicz/scMiko documentation built on June 28, 2023, 1:41 p.m.