Extract multiple communication feature types from the same simulated graphs

Generates the requested number of Monte Carlo graph realizations once per patient and extracts every requested feature family from each realization, instead of the alternative of calling countDirect(), countWedges(), etc. separately – which would independently re-simulate a brand new set of random graphs per feature type. Graph generation (model1()'s vertex/edge sampling), not feature extraction, is the expensive part of a Monte Carlo run, and it does not depend on which feature you eventually want – so sharing one set of realizations across every requested type avoids paying that cost once per type.

Usage

countAllTypes(
  Dcell,
  Dconn,
  lig,
  rec,
  cellnames,
  N,
  av,
  itNo,
  types = c("D", "W", "TT", "CT", "GSCC")
)

Arguments

Dcell

Cell-type abundance vector for one patient.

Dconn

Ligand-receptor probability matrix for one patient.

lig

Cell-by-ligand compatibility matrix.

rec

Cell-by-receptor compatibility matrix.

cellnames

Character vector of cell-type names.

N

Number of cells per simulated graph.

av

Target average degree.

itNo

Number of Monte Carlo iterations.

types

Character vector of feature families to extract from each simulated graph: any combination of "D", "W", "TT", "CT", "GSCC".

Value

A named list, one element per requested entry in types, each with the same structure the corresponding single-type count*() function returns (e.g. out$D matches countDirect()'s return value).