```
#' List simulations
#'
#' List all the simulations that are currently available in Splatter with a
#' brief description.
#'
#' @param print logical. Whether to print to the console.
#'
#' @return Invisibly returns a data.frame containing the information that is
#' displayed.
#' @examples
#' listSims()
#' @export
listSims <- function(print = TRUE) {
sims <- list(c("Splat", "splat", "10.1186/s13059-017-1305-0",
"Oshlack/splatter",
"The Splat simulation generates means from a gamma
distribution, adjusts them for BCV and generates counts from
a gamma-poisson. Dropout and batch effects can be optionally
added."),
c("Splat Single", "splatSingle", "10.1186/s13059-017-1305-0",
"Oshlack/splatter",
"The Splat simulation with a single population."),
c("Splat Groups", "splatGroups", "10.1186/s13059-017-1305-0",
"Oshlack/splatter",
"The Splat simulation with multiple groups. Each group can
have it's own differential expression probability and
fold change distribution."),
c("Splat Paths", "splatPaths", "10.1186/s13059-017-1305-0",
"Oshlack/splatter",
"The Splat simulation with differentiation paths. Each
path can have it's own length, skew and probability. Genes
can change in non-linear ways."),
c("Simple", "simple", "10.1186/s13059-017-1305-0",
"Oshlack/splatter",
"A simple simulation with gamma means and negative binomial
counts."),
c("Lun", "lun", "10.1186/s13059-016-0947-7",
"MarioniLab/Deconvolution2016",
"Gamma distributed means and negative binomial counts. Cells
are given a size factor and differential expression can be
simulated with fixed fold changes."),
c("Lun 2", "lun2", "10.1093/biostatistics/kxw055",
"MarioniLab/PlateEffects2016",
"Negative binomial counts where the means and dispersions
have been sampled from a real dataset. The core feature of
the Lun 2 simulation is the addition of plate effects.
Differential expression can be added between two groups of
plates and optionally a zero-inflated negative-binomial can
be used."),
c("scDD", "scDD", "10.1186/s13059-016-1077-y",
"kdkorthauer/scDD",
"The scDD simulation samples a given dataset and can
simulate differentially expressed and differentially
distributed genes between two conditions."),
c("BASiCS", "BASiCS", "10.1371/journal.pcbi.1004333",
"catavallejos/BASiCS",
"The BASiCS simulation is based on a bayesian model used to
deconvolve biological and technical variation and
includes spike-ins and batch effects."),
c("mfa", "mfa", "10.12688/wellcomeopenres.11087.1",
"kieranrcampbell/mfa",
"The mfa simulation produces a bifurcating pseudotime
trajectory. This can optionally include genes with transient
changes in expression and added dropout."),
c("PhenoPath", "pheno", "10.1101/159913",
"kieranrcampbell/phenopath",
"The PhenoPath simulation produces a pseudotime trajectory
with different types of genes."),
c("ZINB-WaVE", "zinb", "10.1101/125112",
"drisso/zinbwave",
"The ZINB-WaVE simulation simulates counts from a
sophisticated zero-inflated negative-binomial distribution
including cell and gene-level covariates."),
c("SparseDC", "sparseDC", "10.1093/nar/gkx1113",
"cran/SparseDC",
"The SparseDC simulation simulates a set of clusters
across two conditions, where some clusters may be present in
only one condition."))
sims.table <- data.frame(Name = rep(NA, length(sims)),
Prefix = rep(NA, length(sims)),
DOI = rep(NA, length(sims)),
GitHub = rep(NA, length(sims)),
Description = rep(NA, length(sims)))
for (idx in seq_along(sims)) {
entry <- sims[[idx]]
entry[5] <- gsub("[[:space:]]+", " ", entry[5])
sims.table[idx, ] <- entry
}
if (print) {
cat("Splatter currently contains", length(sims), "simulations", "\n\n")
for (idx in seq_len(nrow(sims.table))) {
sim <- as.character(sims.table[idx, ])
cat(crayon::bold(sim[1]), crayon::yellow(paste0("(", sim[2], ")")),
"\n")
cat(crayon::bold("DOI:"), crayon::cyan(sim[3]), "\t",
crayon::bold("GitHub:"), crayon::cyan(sim[4]), "\n")
cat(sim[5], "\n\n")
}
}
invisible(sims.table)
}
```

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