Package 'simPIC'

Add feature statistics

Description

Add additional feature statistics to a SingleCellExperiment object

Usage

addFeatureStats(
  sce,
  value = "counts",
  log = FALSE,
  offset = 1,
  no.zeros = FALSE
)

Arguments

sce	SingleCellExperiment to add feature statistics to.
value	the count value to calculate statistics.
log	logical. Whether to take log2 before calculating statistics.
offset	offset to add to avoid taking log of zero.
no.zeros	logical. Whether to remove all zeros from each feature before calculating statistics.

Details

Currently adds the following statistics: mean and variance. Statistics are added to the rowData slot and are named Stat[Log]Value[No0] where Log and No0 are added if those arguments are true.

Value

SingleCellExperiment with additional feature statistics

---

Convert Sparse Matrix to SingleCellExperiment object

Description

This function converts a sparse matrix into a SingleCellExperiment (SCE) object.

Usage

convert_to_SCE(sparse_data)

Arguments

sparse_data

A sparse matrix containing count data, where rows are peaks and columns represent cells.

Value

A SingleCellExperiment (SCE) object with the sparse matrix stored in the "counts" assay.

---

Ensure counts assay is first

Description

Reorders assays in a SingleCellExperiment so that the counts assay appears first. This keeps assay(sce) behavior predictable for downstream code that still assumes the primary assay is counts.

Usage

ensureCountsFirst(sce)

Arguments

sce	SingleCellExperiment object.

Value

SingleCellExperiment with counts assay first if present.

---

Get counts from Single Cell Experiment object

Description

Get counts matrix from a SingleCellExperiment object. If counts is missing a warning is issued and the first assay is returned.

Usage

getCounts(sce)

Arguments

sce	SingleCellExperiment object

Value

counts matrix

---

simPIC: Simulate single-cell ATAC-seq data

Description

simPIC: Simulate single-cell ATAC-seq data

Value

globalvariables

---

newsimPICcount

Description

Create a newsimPICcount object to store parameters.

Usage

newsimPICcount(...)

Arguments

...	Variables to set newsimPICcount object parameters.

Details

This function creates the object variable which is passed in all functions.

Value

new object from class simPICcount.

Examples

object <- newsimPICcount()

---

Custom theme for ggplot2

Description

This function defines a custom theme for ggplot2 to ensure consistent visual appearance across multiple plots.

Usage

plot_theme()

Value

A ggplot2 theme object with predefined settings.

---

Bind rows (matched)

Description

Bind the rows of two data frames, keeping only the columns that are common to both.

Usage

rbindMatched(df1, df2)

Arguments

df1	first data.frame to bind.
df2	second data.frame to bind.

Value

data.frame containing rows from df1 and df2 but only common columns.

---

Select fit

Description

Trying two fitting methods and selecting the best one.

Usage

selectFit(data, distr, verbose = TRUE)

Arguments

data	The data to fit.
distr	Name of the distribution to fit.
verbose	Logical. Whether to print progress messages.

Details

The distribution is fitted to the data using each of the fitdist fitting methods. The fit with the smallest Cramer-von Mises statistic is selected.

Value

The selected fit object

---

Set simPIC parameters

Description

Set input parameters of the simPICcount object.

Usage

setsimPICparameters(object, update = NULL, ...)

Arguments

object	input simPICcount object.
update	new parameters.
...	set new parameters for simPICcount object.

Value

simPICcount object with updated parameters.

Examples

object <- newsimPICcount()
object <- setsimPICparameters(object, nCells = 200, nPeaks = 500)

---

Compare SingleCellExperiment objects

Description

Combine data from several SingleCellExperiment objects and produce some basic plots comparing them.

Usage

simPICcompare(
  sces,
  point.size = 0.2,
  point.alpha = 0.1,
  fits = TRUE,
  colours = NULL
)

Arguments

sces	named list of SingleCellExperiment objects to combine and compare.
point.size	size of points in scatter plots.
point.alpha	opacity of points in scatter plots.
fits	whether to include fits in scatter plots.
colours	vector of colours to use for each dataset.

Details

The returned list has three items: The first dataset in sces is treated as the reference dataset when computing Kolmogorov-Smirnov (KS) summaries.

RowData

Combined row data from the provided SingleCellExperiments.

ColData

Combined column data from the provided SingleCellExperiments.

KS

Data frame summarizing KS statistics for peak means, library sizes, and cell sparsity, comparing each dataset to the first dataset in sces.

Plots

Comparison plots

Means

Boxplot of mean distribution.

Variances

Boxplot of variance distribution.

MeanVar

Scatter plot with fitted lines showing the mean-variance relationship.

LibrarySizes

Boxplot of the library size distribution.

ZerosPeak

Boxplot of the percentage of each peak that is zero.

ZerosCell

Boxplot of the percentage of each cell that is zero.

MeanZeros

Scatter plot with fitted lines showing the mean-zeros relationship.

The plots returned by this function are created using ggplot and are only a sample of the kind of plots you might like to consider. The data used to create these plots is also returned and should be in the correct format to allow you to create further plots using ggplot.

Value

List containing the combined datasets and plots.

Examples

sim1 <- simPICsimulate(
    nPeaks = 1000, nCells = 500,
    pm.distr = "weibull", seed = 7856
)
sim2 <- simPICsimulate(
    nPeaks = 1000, nCells = 500,
    pm.distr = "gamma", seed = 4234
)
comparison <- simPICcompare(list(weibull = sim1, gamma = sim2))
names(comparison)
names(comparison$Plots)

---

The simPICcount class

Description

S4 class that holds parameters for simPIC simulation.

Value

a simPIC class object. The parameters not shown in brackets can be estimated from real data using simPICestimate. For details of the simPIC simulation see simPICsimulate. The default parameters are based on the PBMC10k dataset and can be reproduced using the test data and script provided in inst/scripts.

Parameters

simPIC simulation parameters:

nPeaks

The number of peaks to simulate.

nCells

The number of cells to simulate.

[seed]

Seed to use for generating random numbers.

[default]

Logical value indicating whether to use default parameters (TRUE) or learn parameters from data (FALSE).

Library size parameters

lib.size.meanlog

meanlog (location) parameter for the library size log-normal distribution.

lib.size.sdlog

sdlog (scale) parameter for the library size log-normal distribution.

Peak mean parameters

mean.scale

scale parameter for the mean weibull distribution.

mean.shape

shape parameter for the mean weibull distribution.

Cell sparsity parameters

sparsity

Probability that contributes to the sparsity of the final simulated matrix.

---

Estimate simPIC Biological Coefficient of Variation parameters

Description

Parameters are estimated using the estimateDisp function in the edgeR package.

Usage

simPICEstBCV(counts, object, verbose)

Arguments

counts	counts matrix to estimate parameters from.
object	simPICcount object to store estimated values in.
verbose	Logical. Whether to print progress messages.

Details

The estimateDisp function is used to estimate the common dispersion and prior degrees of freedom. See estimateDisp for details. When estimating parameters on simulated data we found a broadly linear relationship between the true underlying common dispersion and the edgeR estimate, therefore we apply a small correction, disp = -0.3 + 0.15 * edgeR.disp.

Value

simPICcount object with estimated values.

---

Estimate simPIC simulation parameters

Description

Estimate simulation parameters for library size, peak means, and sparsity from a real peak-by-cell input matrix.

Usage

simPICestimate(
  counts,
  object = newsimPICcount(),
  pm.distr = c("lngamma", "gamma", "weibull", "pareto"),
  method = c("single", "groups"),
  verbose = TRUE
)

## S3 method for class 'SingleCellExperiment'
simPICestimate(
  counts,
  object = newsimPICcount(),
  pm.distr = "lngamma",
  method = "single",
  verbose = TRUE
)

## S3 method for class 'dgCMatrix'
simPICestimate(
  counts,
  object = newsimPICcount(),
  pm.distr = "lngamma",
  method = "single",
  verbose = TRUE
)

Arguments

counts	either a sparse peak by cell count matrix, or a SingleCellExperiment object containing count data to estimate parameters.
object	simPICcount object to store estimated parameters and counts.
pm.distr	statistical distribution for estimating peak mean parameters. Available distributions: gamma, weibull, lngamma, pareto. Default is lngamma.
method	Simulation mode. Use "single" to estimate parameters for one cell type or "groups" for distinct cell types.
verbose	logical variable. Prints the simulation progress if TRUE.

Value

simPICcount object containing all estimated parameters.

Examples

counts <- readRDS(system.file("extdata", "test.rds", package = "simPIC"))
est <- newsimPICcount()
est <- simPICestimate(counts, pm.distr = "lngamma")

---

Estimate simPIC library size parameters

Description

Estimate the library size parameters for simPIC simulation.

Usage

simPICestimateLibSize(counts, object, verbose)

Arguments

counts	count matrix.
object	simPICcount object to store estimated values.
verbose	Logical. Whether to print progress messages.

Details

Parameters for the lognormal distribution are estimated by fitting the library sizes using fitdist. All the fitting methods are tried and the fit with the best Cramer-von Mises statistic is selected.

Value

simPICcount object with estimated library size parameters.

---

Estimate simPIC peak means

Description

Estimate peak mean parameters for simPIC simulation

Usage

simPICestimatePeakMean(norm.counts, object, pm.distr, verbose)

Arguments

norm.counts	Library-size normalized count matrix.
object	simPICcount object to store estimated values.
pm.distr	distribution parameter for peak means.
verbose	Logical. Whether to print progress messages.

Details

Parameters for gamma distribution are estimated by fitting the mean normalized counts using fitdist. All the fitting methods are tried and the fit with the best Cramer-von Mises statistic is selected.

Value

simPICcount object containing all estimated parameters

---

Estimate sparsity

Description

This function estimates cell sparsity from a normalized count matrix and updates the parameters of a simPIC object accordingly.

Usage

simPICestimateSparsity(norm.counts, object, verbose)

Arguments

norm.counts	A normalized count matrix to estimate parameters from.
object	simPICcount object to store estimated parameters.
verbose	Logical. Whether to print progress messages.

Value

simPICcount object with updated sparsity parameter.

---

Get a single simPICcount parameter

Description

Get the value of a single variable from input simPICcount object.

Usage

simPICget(object, name)

Arguments

object	input simPICcount object.
name	name of the parameter.

Value

Value of the input parameter.

Examples

object <- newsimPICcount()
nPeaks <- simPICget(object, "nPeaks")

---

Get parameters

Description

Get multiple parameter values from a simPIC object.

Usage

simPICgetparameters(object, names)

Arguments

object	input object to get values from.
names	vector of names of the parameters to get.

Value

List with the values of the selected parameters.

Examples

object <- newsimPICcount()
simPICgetparameters(object, c("nPeaks", "nCells", "peak.mean.shape"))

---

Run the Packaged Microglia splatPop Example

Description

Run a streamlined population-scale Microglia example using packaged SingleCellExperiment, sample mapping, peak annotation, and VCF files. The workflow follows the splatPop estimation pattern by estimating population means from retained donor-batch units while estimating single-cell behaviour from the donor-batch unit with the most cells.

Usage

simPICMicrogliaExample(
  sce = NULL,
  sample.map = NULL,
  peak.annot = NULL,
  vcf = NULL,
  params = NULL,
  min.cells = 20L,
  pop.cv.bins = 10L,
  eqtl.n = 0.05,
  similarity.scale = 2,
  eqtl.dist = 1e+08,
  sparsify = FALSE,
  pca.ntop = 100L,
  pca.components = 5L,
  plot.n.samples = 4L,
  plot.samples = NULL,
  comparison.batch = NULL,
  comparison.pathology = NULL,
  seed = NULL,
  verbose = TRUE,
  ...
)

Arguments

sce	Optional SingleCellExperiment or path to an RDS file containing the packaged Microglia SCE. If NULL, the packaged example is used.
sample.map	Optional data.frame or path to a tab-separated sample map linking VCF donor IDs to Microglia donor IDs. If NULL, the packaged example is used.
peak.annot	Optional data.frame or path to the chr14 peak annotation table. If NULL, the packaged example is used.
vcf	Optional CollapsedVCF or path to the chr14 VCF used for the Microglia example. If NULL, the packaged example is used.
params	Optional SplatPopParams. If NULL, a new object is created with pop.cv.bins.
min.cells	Minimum number of cells required per donor-batch unit for inclusion in the aggregated population means. Units must exceed this threshold.
pop.cv.bins	Number of CV bins used when params is created internally.
eqtl.n	Proportion of peaks to simulate with caQTL effects. The packaged example uses a modest default to keep the workflow lightweight while still including genetic effects.
similarity.scale	Similarity scale passed to splatter::setParams() before simulation.
eqtl.dist	Maximum cis-distance used when assigning caQTL effects.
sparsify	Logical. Whether to sparsify the simulated output.
pca.ntop	Number of variable peaks used for PCA plots.
pca.components	Number of principal components to compute for the PCA summaries.
plot.n.samples	Number of libraries to show in the default PCA comparison panels. The top libraries are chosen by real-data cell count.
plot.samples	Optional character vector of specific libraries to show in the PCA comparison panels. When NULL, the top plot.n.samples libraries are used.
comparison.batch	Optional real-data batch/library label used for the Poptrial-style comparison panels. When supplied, the real data are subset to this batch and the simulated data are matched by the same sample IDs.
comparison.pathology	Optional pathology label used together with comparison.batch for Poptrial-style panels, for example "earlyAD".
seed	Optional random seed.
verbose	Logical. Whether to print progress messages.
...	Additional parameters passed to splatter::setParams() prior to simulation.

Value

A list containing:

sce

Filtered real-data Microglia SingleCellExperiment.

bigcounts

The donor-batch subset with the most cells used for count-based estimation.

aggregated

Aggregated donor-batch means as a SingleCellExperiment.

params

Estimated SplatPopParams after Microglia-specific parameter updates.

sim

Simulated SingleCellExperiment.

plot_samples

Libraries used for the default PCA comparison panels.

plots

A named list containing real and simulated PCA plot summaries, side-by-side comparison panels, and bluster-based silhouette and neighborhood-purity panels.

sample_map

The cleaned sample map used for alignment.

peak_annot

The chr14 peak annotation used for simulation.

vcf

The aligned VCF used for simulation.

Examples

if (requireNamespace("splatter", quietly = TRUE) &&
    requireNamespace("VariantAnnotation", quietly = TRUE) &&
    requireNamespace("bluster", quietly = TRUE)) {
out <- simPICMicrogliaExample(verbose = FALSE)
out$plot_samples
names(out$plots$comparison)
}

---

Plot Poptrial-Style PCA, Silhouette, and Purity Comparisons

Description

Create side-by-side real-versus-simulated comparison panels inspired by the bluster-based plotting workflow used in Poptrial.Rmd. The comparison can be restricted either to a chosen set of samples or to a real-data batch, in which case the simulated data are matched by the same sample IDs.

Usage

simPICplotBlusterComparison(
  real.sce,
  simulated.sce,
  sample.col = "Sample",
  batch.col = NULL,
  subset.batch = NULL,
  pathology.col = NULL,
  subset.pathology = NULL,
  plot.samples = NULL,
  plot.n.samples = 5L,
  pca.ntop = 2000L,
  pca.components = 10L,
  point.size = 0.8,
  verbose = TRUE
)

Arguments

real.sce	Real-data SingleCellExperiment.
simulated.sce	Simulated SingleCellExperiment.
sample.col	Column in colData() containing sample IDs.
batch.col	Optional column in the real-data colData() used to define a batch or library subset such as "Library5".
subset.batch	Optional batch/library value used to subset the real data before matching the simulated samples.
pathology.col	Optional pathology column in the real-data colData().
subset.pathology	Optional pathology value used together with subset.batch to restrict the real-data comparison subset.
plot.samples	Optional character vector of samples to compare. Ignored when subset.batch is supplied.
plot.n.samples	Number of top samples to keep when neither subset.batch nor plot.samples is supplied.
pca.ntop	Number of most variable peaks used for PCA.
pca.components	Number of principal components to compute.
point.size	Point size used in PCA panels.
verbose	Logical. Whether to print progress messages.

Value

A list with the subsetted real and simulated SCEs, the selected sample IDs, the bluster metric tables, and three ggplot panels: cell_pca_by_sample, silhouette_width, and neighborhood_purity.

---

Plot Population-Style PCA Summaries for Real Data

Description

Create splatPop-style PCA plots from a real peak-by-cell matrix or SingleCellExperiment. When metadata is not already available, sample IDs can be inferred from cell names, which is useful for peak-by-cell matrices where columns are encoded like sample#barcode.

Usage

simPICplotPopulationPCA(
  counts,
  sample.col = NULL,
  batch.col = NULL,
  sample.pattern = "#.*$",
  sample.replacement = "",
  pca.ntop = 2000,
  pca.components = 10,
  aggregate.by.sample = TRUE,
  point.size = 0.8,
  verbose = TRUE
)

Arguments

counts	A peak-by-cell count matrix, sparse Matrix, or SingleCellExperiment.
sample.col	Optional name of a sample column already present in colData(counts). If NULL, sample IDs are inferred from column names using sample.pattern.
batch.col	Optional name of a batch column already present in colData(counts).
sample.pattern	Regular expression used to strip the barcode suffix from cell names when inferring sample IDs.
sample.replacement	Replacement string used with sample.pattern.
pca.ntop	Number of most variable peaks to use for PCA.
pca.components	Number of principal components to compute.
aggregate.by.sample	Logical. Whether to also aggregate cells by sample and create a donor-level PCA plot.
point.size	Point size passed to scater::plotPCA().
verbose	Logical. Whether to print progress messages.

Value

A list containing:

cell_sce

Cell-level SingleCellExperiment with inferred metadata, log-normalized counts, and PCA.

sample_sce

Sample-aggregated SingleCellExperiment if aggregate.by.sample = TRUE, otherwise NULL.

plots

A named list of ggplot objects.

Examples

if (requireNamespace("scater", quietly = TRUE)) {
    counts <- matrix(rpois(50 * 24, lambda = 3), nrow = 50, ncol = 24)
    colnames(counts) <- paste0(
        rep(paste0("Sample", 1:6), each = 4),
        "#Cell",
        seq_len(ncol(counts))
    )
    plots <- simPICplotPopulationPCA(counts, verbose = FALSE)
    names(plots$plots)
}

---

Simulate cell means

Description

Simulate a peak by cell matrix given the mean accessibility for each peak in each cell. Cells start with the mean accessibility for the group they belong to (when simulating groups). The selected means are adjusted for each cell's expected library size.

Usage

simPICsimSingleCellMeans(object, sim)

simPICsimulateGroupCellMeans(object, sim)

Arguments

object	simPIC object with simulation parameters.
sim	SingleCellExperiment to add cell means to.

Value

SingleCellExperiment with added cell means.

---

simPIC simulation

Description

Simulate a peak-by-cell count matrix using simPIC methods.

Usage

simPICsimulate(
  object = newsimPICcount(),
  pm.distr = "lngamma",
  method = c("single", "groups"),
  verbose = TRUE,
  ...
)

simPICsimulatesingle(object = newsimPICcount(), verbose = TRUE, ...)

simPICsimulatemulti(
  object = newsimPICcount(),
  pm.distr = "lngamma",
  method = c("groups"),
  verbose = TRUE,
  ...
)

Arguments

object	simPICcount object with simulation parameters. See simPICcount for details.
pm.distr	distribution parameter for peak means. Available distributions: gamma, weibull, lngamma, pareto. Default is lngamma.
method	Simulation mode. Use "single" to simulate one cell type or "groups" to simulate distinct cell types.
verbose	Logical. Whether to print progress messages.
...	Any additional parameter settings to override what is provided in simPICcount object.

Details

simPIC provides the option to manually adjust each of the simPICcount object parameters by calling setsimPICparameters.

The simulation involves the following steps:

1. Set up simulation parameters

2. Set up SingleCellExperiment object

3. Simulate library sizes

4. Simulate sparsity

5. Simulate peak means

6. Create final synthetic counts

The final output is a SingleCellExperiment object that contains the simulated count matrix. The parameters are stored in the colData (for cell-specific information), rowData (for peak-specific information), or assays (for peak-by-cell matrices).

Value

SingleCellExperiment object containing the simulated counts.

Examples

# default simulation
sim <- simPICsimulate(pm.distr = "lngamma")

---

Simulate BCV means

Description

Simulate means for each peak in each cell that are adjusted to follow a mean-variance trend using Biological Coefficient of Variation taken from and inverse gamma distribution.

Usage

simPICsimulateBCVMeans(object, sim)

Arguments

object	simPICcount object with simulation parameters.
sim	SingleCellExperiment to add BCV means to.

Value

SingleCellExperiment with simulated BCV means.

---

Simulate simPIC library sizes

Description

Generate library sizes for cells in simPIC simulation based on the estimated values of mus and sigmas.

Usage

simPICsimulateLibSize(object, sim, verbose)

Arguments

object	simPICcount object with simulation parameters.
sim	SingleCellExperiment object containing simulation parameters.
verbose	Logical. Whether to print progress messages.

Value

SingleCellExperiment object with simulated library sizes.

---

Simulate group differential accessibility

Description

Simulate differential accessibility. Differential accessibility factors for each group are produced using getLNormFactors and these are added along with updated means for each group. For paths care is taken to make sure they are simulated in the correct order.

Usage

simPICsimulatemultiDA(object, sim)

Arguments

object	simPICcount object with simulation parameters.
sim	SingleCellExperiment to add differential accessibility to.

Value

SingleCellExperiment with simulated differential accessibility.

---

Simulate simPIC peak means

Description

Generate peak means for cells in simPIC simulation based on the estimated values of shape and rate parameters.

Usage

simPICsimulatePeakMean(object, sim, pm.distr, verbose)

Arguments

object	simPICcount object with simulation parameters.
sim	SingleCellExperiment object containing simulation parameters.
pm.distr	distribution parameter for peak means. Available distributions: gamma, weibull, lngamma, pareto. Default is lngamma.
verbose	logical. Whether to print progress messages.

Value

SingleCellExperiment object with simulated peak means.

---

Simulate true counts

Description

Counts are simulated from a Poisson distribution where each peak has a mean, expected library size and proportion of accessible chromatin.

Usage

simPICsimulateTrueCounts(object, sim)

Arguments

object	simPICcount object with simulation parameters.
sim	SingleCellExperiment object containing simulation parameters.

Value

SingleCellExperiment object with simulated true counts.

---

Simulate true counts groups

Description

Counts are simulated from a Poisson distribution where each peak has a mean, expected library size and proportion of accessible chromatin.

Usage

simPICsimulateTrueCountsGroups(object, sim)

Arguments

object	simPICcount object with simulation parameters.
sim	SingleCellExperiment object containing simulation parameters.

Value

SingleCellExperiment object with simulated true counts.

---

Estimate Peak-Based splatPop Parameters

Description

Estimate splatPop population parameters from peak-by-cell counts. This is a peak-based wrapper around splatter::splatPopEstimate() that can optionally aggregate donor-level peak means from a SingleCellExperiment object and then apply the simPIC BCV correction.

Usage

splatPopEstimatePeak(
  counts = NULL,
  means = NULL,
  eqtl = NULL,
  params = NULL,
  sample.col = "Sample",
  batch.col = NULL,
  aggregate.by = c("sample", "sample_batch"),
  min.cells = 1,
  pop.cv.bins = 50,
  apply.bcv.correction = TRUE,
  verbose = TRUE
)

Arguments

counts	Either a SingleCellExperiment object or a peak-by-cell count matrix. When means is NULL, counts must be a SingleCellExperiment with sample-level metadata so donor means can be aggregated automatically.
means	Optional dense matrix of aggregated peak means, with peaks in rows and donors/samples in columns.
eqtl	Optional empirical eQTL table to pass through to splatter::splatPopEstimate().
params	Optional SplatPopParams object. If NULL, a new object is created with pop.cv.bins.
sample.col	Name of the sample/donor column in colData(counts) when counts is a SingleCellExperiment.
batch.col	Optional batch column in colData(counts) used when aggregate.by = "sample_batch".
aggregate.by	Whether automatically derived means should be aggregated by sample or by sample-batch combinations.
min.cells	Minimum number of cells required per aggregation unit when deriving means from a SingleCellExperiment.
pop.cv.bins	Number of CV bins to use when params is created internally.
apply.bcv.correction	Logical. If TRUE, apply the simPIC BCV correction rule directly to the estimated SplatPopParams.
verbose	Logical. Whether to print progress messages.

Value

A SplatPopParams object.

Examples

if (requireNamespace("splatter", quietly = TRUE) &&
    requireNamespace("VariantAnnotation", quietly = TRUE)) {
    set.seed(101)
    gene_means <- rgamma(60, shape = 2, rate = 0.4)
    cell_scales <- runif(48, min = 0.7, max = 1.4)
    counts <- vapply(
        cell_scales,
        function(scale) {
            rnbinom(60, mu = gene_means * scale, size = 0.2)
        },
        numeric(60)
    )
    sce <- SingleCellExperiment::SingleCellExperiment(
        assays = list(counts = counts),
        colData = data.frame(
            Sample = rep(paste0("S", 1:12), each = 4),
            Batch = rep(c("B1", "B2"), each = 24)
        )
    )
    params <- splatPopEstimatePeak(
        counts = sce,
        sample.col = "Sample",
        batch.col = "Batch",
        aggregate.by = "sample",
        min.cells = 2,
        pop.cv.bins = 10,
        verbose = FALSE
    )
    params
}

---

Simulate Peak-Based Population Data with splatPop

Description

Simulate peak-based single-cell data using splatter::splatPopSimulate while coercing peak annotations into the GFF-like structure that splatPop expects internally.

Usage

splatPopSimulatePeak(
  params = NULL,
  vcf = NULL,
  method = c("single", "groups", "paths"),
  gff = NULL,
  eqtl = NULL,
  means = NULL,
  key = NULL,
  counts.only = FALSE,
  sparsify = TRUE,
  verbose = TRUE,
  ...
)

Arguments

params	Optional SplatPopParams object. If NULL, a new object is created.
vcf	Optional VariantAnnotation VCF object. If NULL, the splatter mock VCF is used.
method	Simulation mode passed through to splatPopSimulate().
gff	Peak annotation supplied either as NULL, a GFF-like data.frame, or a GRanges. Peak annotations are coerced to a GFF-like table and stored as gene-like features for the underlying splatPop machinery.
eqtl	Optional empirical eQTL table.
means	Optional empirical population means matrix.
key	Optional splatPop key.
counts.only	Logical. Whether to keep only counts in the simulated object.
sparsify	Logical. Whether to sparsify the resulting assays.
verbose	Logical. Whether to print progress messages.
...	Additional parameters passed to splatter::splatPopSimulate.

Value

A SingleCellExperiment object.

Examples

if (requireNamespace("splatter", quietly = TRUE) &&
    requireNamespace("VariantAnnotation", quietly = TRUE)) {
    params <- splatter::newSplatPopParams(nGenes = 20)
    params <- splatter::setParams(params, batchCells = c(20))
    sim <- splatPopSimulatePeak(
        params = params,
        vcf = splatter::mockVCF(),
        gff = splatter::mockGFF()[seq_len(20), ],
        sparsify = FALSE,
        verbose = FALSE
    )
    sim
}

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