#' Get Information of SparseDC
#'
#' @param ... ...
#'
#' @return A list contains the information of method and default parameters
#' @import simutils
#' @export
#'
#' @examples
#' SparseDC_method_definition <- SparseDC_method_definition()
#'
SparseDC_method_definition <- function(...){
SparseDC_parameters <- parameter_sets(
param_reference(
id = "counts",
type = "matrix",
default = NULL,
process = "estimation",
force = TRUE,
description = "Either a counts matrix or an SingleCellExperiment object containing count data to estimate parameters from.",
function_name = "sparseDCEstimate"
),
param_vector(
id = "conditions",
default = FALSE,
force = TRUE,
process = "estimation",
description = "Numeric vector giving the condition each cell belongs to.",
function_name = "sparseDCEstimate"
),
param_others(
id = "nclusters",
type = "integer",
force = TRUE,
process = "estimation",
description = "Number of cluster present in the dataset.",
function_name = "sparseDCEstimate"
),
param_Boolean(
id = "norm",
default = TRUE,
process = "estimation",
description = "Logical, whether to library size normalise counts before estimation. Set this to FALSE if counts is already normalised.",
function_name = "sparseDCEstimate"
),
param_others(
id = "params",
type = "SparseDCParams",
default = "newSparseDCParams()",
process = "estimation",
description = "PhenoParams object to store estimated values in.",
function_name = "sparseDCEstimate"
),
param_Boolean(
id = "sparsify",
default = TRUE,
process = "estimation",
description = "Logical. Whether to automatically convert assays to sparse matrices if there will be a size reduction.",
function_name = "sparseDCSimulate"
),
param_Boolean(
id = "verbose",
default = TRUE,
process = "estimation",
description = "Logical. Whether to print progress messages.",
function_name = "sparseDCSimulate"
),
param_integer(
id = "nGenes",
process = "simulation",
default = 10000L,
lower = 1L,
function_name = "sparseDCSimulate"
),
param_integer(
id = "nCells",
process = "simulation",
default = 100L,
lower = 1L,
function_name = "sparseDCSimulate"
),
param_integer(
id = "seed",
process = "simulation",
force = TRUE,
function_name = "sparseDCSimulate"
),
param_integer(
id = "markers.n",
default = 0L,
process = "simulation",
description = "Number of marker genes to simulate for each cluster.",
function_name = "sparseDCSimulate"
),
param_integer(
id = "markers.shared",
default = 0L,
process = "simulation",
description = "Number of marker genes for each cluster shared between conditions. Must be less than or equal to markers.n",
function_name = "sparseDCSimulate"
),
param_Boolean(
id = "markers.same",
default = FALSE,
process = "simulation",
description = "Logical. Whether to print progress messages.",
function_name = "sparseDCSimulate"
),
param_vector(
id = "clusts.c1",
default = 1,
process = "simulation",
description = "Numeric vector of clusters present in condition 1. The number of times a cluster is repeated controls the proportion of cells from that cluster.",
function_name = "sparseDCSimulate"
),
param_vector(
id = "clusts.c2",
default = 1,
process = "simulation",
description = "Numeric vector of clusters present in condition 2. The number of times a cluster is repeated controls the proportion of cells from that cluster.",
function_name = "sparseDCSimulate"
),
param_numeric(
id = "mean.lower",
default = 1,
lower = 0,
description = "Lower bound for cluster gene means.",
process = "simulation",
function_name = "sparseDCSimulate"
),
param_numeric(
id = "mean.upper",
default = 2,
lower = 0,
description = "Upper bound for cluster gene means.",
process = "simulation",
function_name = "sparseDCSimulate"
)
)
SparseDC_method <- method_definition(
method = "SparseDC",
programming = "R",
url = "https://cran.rstudio.com/web/packages/SparseDC/index.html",
authors = authors_definition(
first = "Martin",
last = "Barron",
email = "jun.li@nd.edu",
orcid = NULL
),
manuscript = manuscript_definition(
title = "A sparse differential clustering algorithm for tracing cell type changes via single-cell RNA-sequencing data",
doi = "10.1093/nar/gkx1113",
journal = "Nucleic Acids Research",
date = "2018",
peer_review = TRUE
),
description = "This algorithm clusters samples from two different populations, links the clusters across the conditions and identifies marker genes for these changes. The package was designed for scRNA-Seq data but is also applicable to many other data types, just replace cells with samples and genes with variables.",
vignette = "http://47.254.148.113/software/Simsite/references/methods/22-sparsedc/")
list(SparseDC_method = SparseDC_method,
SparseDC_parameters = SparseDC_parameters)
}
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