SimulateChICseq: Simulate count matrices from a Poisson distribution

In jakeyeung/scChIX: Deconvolving Multiplexed Histone Modifications in Single Cells.

Simulate count matrices from a Poisson distribution

Description

Simulate count matrices from a Poisson distribution

Usage

SimulateChICseq(
  ctype.name = "A",
  jseed = 0,
  shuffle.rows.seed = 0,
  frac.mutual.excl = 0.5,
  jspec = "hg38",
  jncells.per.rep = 250,
  jnbins = 5000,
  jlibmean = 12,
  jlibsd = 1,
  jlibp = 0.5,
  jzp = 1
)

Arguments

ctype.name	Name of "celltype" that these single counts come from. Default "A"
jseed	Random seed for simulating count matrices from Poisson distribution
shuffle.rows.seed	Random seed for shuffling rows. Changing this number and rerunning would generate counts from another "celltype".
frac.mutual.excl	Fraction of bins that will be made mutually exclusive between the second mark. This is done by swapping bins with the highest counts (i.e. largest lambda from Poisson) with the lowest counts.
jspec	Species is mm10 or hg38. Defines position information.
jncells.per.rep	number of cells per replicate. Generates 3 replicates. One for histone mark 1, one for histone mark 2, and one with histone mark 1 + 2
jnbins	Number of bins in the data
jlibmean	Average (mean) library size
jzp	Additional dropouts from Poisson (will still be Poisson after dropouts)
libsd	Standard deviation in library size

Value

ctype1.sim.counts.lst Simulated counts from the three replicates (hist 1, hist 2, hist1+2) and annotations of whether the bin is overlapping or mutually exclusive.

Examples

jspec <- "hg38"
jseed <- 0
jncells.per.rep <- 250
jnbins <- 10000
jlibmean <- 12
jlibsd <- 1
jlibp <- 0.5
jzp <- 1
shuffle.rows.seed <- jseed
sim.params <- list(jspec = jspec,
jseed = jseed,
jncells.per.rep = jncells.per.rep,
jnbins = jnbins,
jlibmean = jlibmean,
jlibsd = jlibsd,
jlibp = jlibp,
jzp = jzp)
ctype.sim.counts.lst <- lapply(ctypes, function(jctype){
ctypes <- c("A", "B", "C")
names(ctypes) <- ctypes

ctype.params <- list(ctype.name = c("A", "B", "C"),
                     jseed = c(0, 123, 999),
                     shuffle.rows.seed = c(0, 123, 999),
                     frac.mutual.excl = c(0.5, 0.5, 0.5))

ctype.params.lst <- lapply(ctypes, function(ctype){
  i <- which(ctype.params$ctype.name == ctype)
  return(list(ctype = ctype,
              jseed = ctype.params$jseed[[i]],
              shuffle.rows.seed = ctype.params$shuffle.rows.seed[[i]],
              frac.mutual.excl = ctype.params$frac.mutual.excl[[i]]))
})
SimulateChICseq(ctype.name = jctype,
                jseed = ctype.params.lst[[jctype]]$jseed,
                shuffle.rows.seed = ctype.params.lst[[jctype]]$shuffle.rows.seed,
                frac.mutual.excl = ctype.params.lst[[jctype]]$frac.mutual.excl)
})

jakeyeung/scChIX documentation built on May 7, 2023, 9:14 a.m.