network.data: The input network data is the result from the ConNetGNN function.
gmt.path: Pathway database in GMT format.
pathway.min: Minimum size (in genes) for pathway to be considered. Default: 10.
pathway.max: Maximum size (in genes) for database gene sets to be considered. Default: 500.
nperm: Number of random permutations. Default: 50. We recommend the setting of 100.
parallel.cores: Number of processors to use when doing the calculations in parallel (default: 2). If parallel.cores=0, then it will use all available core processors unless we set this argument with a smaller number.
rwr.gamma: Restart parameter. Default: 0.7.
normal_dist: Whether to use pnorm to calculate P values. Default: TRUE.Note that if normal_dist is FALSE, we need to increase nperm (we recommend 100).
seed: Random number generator seed.
verbose: Gives information about each step. Default: TRUE.
Returns
A matrix of single-cell pathway activity score.
Details
scPathway
The scPathway function integrates the results of ConNetGNN into a gene-cell association network. The genes included in each pathway are used as a restart set in the gene-cell association network to calculate the strength of its association with each cell through RWR. Perturbation analysis was performed to remove noise effects in the network and to obtain the final single-cell pathway activity score matrix.
Examples
require(parallel)require(utils)# Load the result of the ConNetGNN function.data(ConNetGNN_data)kegg.path<-system.file("extdata","KEGG_human.gmt", package ="scapGNN")# We recommend the use of a compiler.# The compiler package can be used to speed up the operation.# library(compiler)# scPathway<- cmpfun(scPathway)scPathway_data<-scPathway(ConNetGNN_data,gmt.path=kegg.path, pathway.min=25,nperm=2,parallel.cores=1)