R/listSims.R

In Oshlack/splatter: Simple Simulation of Single-cell RNA Sequencing Data

#' 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(
            "Kersplat", "kersplat", "",
            "Oshlack/splatter",
            "The Kersplat simulation extends the Splat model by adding a
                   gene network, more complex cell structure, doublets and
                   empty cells (Experimental).",
            "scuttle, igraph"
        ),
        c(
            "splatPop", "splatPop", "10.1186/s13059-021-02546-1",
            "Oshlack/splatter",
            "The splatPop simulation enables splat simulations to be
                   generated for multiple individuals in a population,
                   accounting for correlation structure by simulating
                   expression quantitative trait loci (eQTL).",
            "VariantAnnotation, preprocessCore"
        ),
        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.",
            "scran, scuttle, lme4, pscl, limSolve"
        ),
        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.",
            "scDD"
        ),
        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.",
            "BASiCS"
        ),
        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.",
            "mfa"
        ),
        c(
            "PhenoPath", "pheno", "10.1038/s41467-018-04696-6",
            "kieranrcampbell/phenopath",
            "The PhenoPath simulation produces a pseudotime trajectory
                   with different types of genes.",
            "phenopath"
        ),
        c(
            "ZINB-WaVE", "zinb", "10.1038/s41467-017-02554-5",
            "drisso/zinbwave",
            "The ZINB-WaVE simulation simulates counts from a
                   sophisticated zero-inflated negative-binomial distribution
                   including cell and gene-level covariates.",
            "zinbwave"
        ),
        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.",
            "SparseDC"
        )
    )

    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)),
        Dependencies = 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]), "\t",
                crayon::bold("Dependencies:"), crayon::cyan(sim[6]), "\n"
            )
            cat(sim[5], "\n\n")
        }
    }

    invisible(sims.table)
}