Splatter Manual Cropped

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Package ‘splatter’
March 16, 2019
Type Package
Title Simple Simulation of Single-cell RNA Sequencing Data
Version 1.6.1
Date 2018-12-06
Author Luke Zappia
Maintainer Luke Zappia 
Description Splatter is a package for the simulation of single-cell RNA
sequencing count data. It provides a simple interface for creating complex
simulations that are reproducible and well-documented. Parameters can be
estimated from real data and functions are provided for comparing real and
simulated datasets.
License GPL-3 + file LICENSE
LazyData TRUE
Depends R (>= 3.4), SingleCellExperiment
Imports akima, BiocGenerics, BiocParallel, checkmate, edgeR,
fitdistrplus, ggplot2, locfit, matrixStats, methods, scales,
scater (>= 1.7.4), stats, SummarizedExperiment, utils, crayon
Suggests BiocStyle, covr, cowplot, knitr, limSolve, lme4, progress,
pscl, testthat, rmarkdown, S4Vectors, scDD, scran, mfa,
phenopath, BASiCS, zinbwave, SparseDC, BiocManager
biocViews SingleCell, RNASeq, Transcriptomics, GeneExpression,
Sequencing, Software, ImmunoOncology
URL https://github.com/Oshlack/splatter
BugReports https://github.com/Oshlack/splatter/issues
RoxygenNote 6.1.0
Encoding UTF-8
VignetteBuilder knitr
git_url https://git.bioconductor.org/packages/splatter
git_branch RELEASE_3_8
git_last_commit b809a81
git_last_commit_date 2018-12-05
Date/Publication 2019-03-15
1

R topics documented:

2

R topics documented:
addFeatureStats . . .
addGeneLengths . .
BASiCSEstimate . .
BASiCSParams . . .
BASiCSSimulate . .
bridge . . . . . . . .
compareSCEs . . . .
diffSCEs . . . . . . .
expandParams . . . .
getLNormFactors . .
getParam . . . . . .
getParams . . . . . .
getPathOrder . . . .
listSims . . . . . . .
logistic . . . . . . . .
lun2Estimate . . . .
Lun2Params . . . . .
lun2Simulate . . . .
lunEstimate . . . . .
LunParams . . . . .
lunSimulate . . . . .
makeCompPanel . .
makeDiffPanel . . .
makeOverallPanel . .
mfaEstimate . . . . .
MFAParams . . . . .
mfaSimulate . . . . .
newParams . . . . .
Params . . . . . . . .
phenoEstimate . . . .
PhenoParams . . . .
phenoSimulate . . .
rbindMatched . . . .
scDDEstimate . . . .
SCDDParams . . . .
scDDSimulate . . . .
setParam . . . . . . .
setParams . . . . . .
setParamsUnchecked
setParamUnchecked .
showDFs . . . . . .
showPP . . . . . . .
showValues . . . . .
simpleEstimate . . .
SimpleParams . . . .
simpleSimulate . . .
sparseDCEstimate . .
SparseDCParams . .
sparseDCSimulate . .
splatEstBCV . . . .

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addFeatureStats

3

splatEstDropout . . . . .
splatEstimate . . . . . .
splatEstLib . . . . . . .
splatEstMean . . . . . .
splatEstOutlier . . . . .
SplatParams . . . . . . .
splatSimBatchCellMeans
splatSimBatchEffects . .
splatSimBCVMeans . .
splatSimCellMeans . . .
splatSimDE . . . . . . .
splatSimDropout . . . .
splatSimGeneMeans . .
splatSimLibSizes . . . .
splatSimTrueCounts . . .
splatSimulate . . . . . .
splatter . . . . . . . . . .
summariseDiff . . . . .
winsorize . . . . . . . .
zinbEstimate . . . . . .
ZINBParams . . . . . .
zinbSimulate . . . . . .

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Index

addFeatureStats

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Add feature statistics

Description
Add additional feature statistics to a SingleCellExperiment object
Usage
addFeatureStats(sce, value = c("counts", "cpm", "tpm", "fpkm"),
log = FALSE, offset = 1, no.zeros = FALSE)
Arguments
sce

SingleCellExperiment to add feature statistics to.

value

the expression value to calculate statistics for. Options are "counts", "cpm",
"tpm" or "fpkm". The values need to exist in the given SingleCellExperiment.

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.

4

addGeneLengths

Details
Currently adds the following statistics: mean, variance, coefficient of variation, median and median
absolute deviation. 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. UpperCamelCase is used to differentiate
these columns from those added by analysis packages.
Value
SingleCellExperiment with additional feature statistics

addGeneLengths

Add gene lengths

Description
Add gene lengths to an SingleCellExperiment object
Usage
addGeneLengths(sce, method = c("generate", "sample"), loc = 7.9,
scale = 0.7, lengths = NULL)
Arguments
sce

SingleCellExperiment to add gene lengths to.

method

Method to use for creating lengths.

loc

Location parameter for the generate method.

scale

Scale parameter for the generate method.

lengths

Vector of lengths for the sample method.

Details
This function adds simulated gene lengths to the rowData slot of a SingleCellExperiment object
that can be used for calculating length normalised expression values such as TPM or FPKM. The
generate method simulates lengths using a (rounded) log-normal distribution, with the default loc
and scale parameters based on human protein-coding genes. Alternatively the sample method can
be used which randomly samples lengths (with replacement) from a supplied vector.
Value
SingleCellExperiment with added gene lengths
Examples
# Default generate method
sce <- simpleSimulate()
sce <- addGeneLengths(sce)
head(rowData(sce))
# Sample method (human coding genes)
## Not run:
library(TxDb.Hsapiens.UCSC.hg19.knownGene)

BASiCSEstimate

5

library(GenomicFeatures)
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
tx.lens <- transcriptLengths(txdb, with.cds_len = TRUE)
tx.lens <- tx.lens[tx.lens$cds_len > 0, ]
gene.lens <- max(splitAsList(tx.lens$tx_len, tx.lens$gene_id))
sce <- addGeneLengths(sce, method = "sample", lengths = gene.lens)
## End(Not run)

BASiCSEstimate

Estimate BASiCS simulation parameters

Description
Estimate simulation parameters for the BASiCS simulation from a real dataset.
Usage
BASiCSEstimate(counts, spike.info = NULL, batch = NULL, n = 20000,
thin = 10, burn = 5000, regression = TRUE,
params = newBASiCSParams(), verbose = TRUE, progress = TRUE, ...)
## S3 method for class 'SingleCellExperiment'
BASiCSEstimate(counts, spike.info = NULL,
batch = NULL, n = 20000, thin = 10, burn = 5000,
regression = TRUE, params = newBASiCSParams(), verbose = TRUE,
progress = TRUE, ...)
## S3 method for class 'matrix'
BASiCSEstimate(counts, spike.info = NULL,
batch = NULL, n = 20000, thin = 10, burn = 5000,
regression = TRUE, params = newBASiCSParams(), verbose = TRUE,
progress = TRUE, ...)
Arguments
counts

either a counts matrix or a SingleCellExperiment object containing count data
to estimate parameters from.

spike.info

data.frame describing spike-ins with two columns: "Name" giving the names of
the spike-in features (must match rownames(counts)) and "Input" giving the
number of input molecules.

batch

vector giving the batch that each cell belongs to.

n

total number of MCMC iterations. Must be >= max(4, thin) and a multiple
of thin.

thin

thining period for the MCMC sampler. Must be >= 2.

burn

burn-in period for the MCMC sampler. Must be in the range 1 <= burn < n
and a multiple of thin.

regression

logical. Whether to use regression to identify over-dispersion. See BASiCS_MCMC
for details.

params

BASiCSParams object to store estimated values in.

6

BASiCSParams
verbose

logical. Whether to print progress messages.

progress

logical. Whether to print additional BASiCS progress messages.

...

Optional parameters passed to BASiCS_MCMC.

Details
This function is just a wrapper around BASiCS_MCMC that takes the output and converts it to a BASiCSParams object. Either a set of spike-ins or batch information (or both) must be supplied. If
only batch information is provided there must be at least two batches. See BASiCS_MCMC for details.
Value
BASiCSParams object containing the estimated parameters.
Examples
## Not run:
# Load example data
library(scater)
data("sc_example_counts")
spike.info <- data.frame(Name = rownames(sc_example_counts)[1:10],
Input = rnorm(10, 500, 200),
stringsAsFactors = FALSE)
params <- BASiCSEstimate(sc_example_counts[1:100, 1:30],
spike.info)
params
## End(Not run)

BASiCSParams

The BASiCSParams class

Description
S4 class that holds parameters for the BASiCS simulation.
Parameters
The BASiCS simulation uses the following parameters:
nGenes The number of genes to simulate.
nCells The number of cells to simulate.
[seed] Seed to use for generating random numbers.
Batch parameters nBatches Number of batches to simulate.
batchCells Number of cells in each batch.
Gene parameters gene.params A data.frame containing gene parameters with two coloumns:
Mean (mean expression for each biological gene) and Delta (cell-to-cell heterogeneity
for each biological gene).
Spike-in parameters nSpikes The number of spike-ins to simulate.
spike.means Input molecules for each spike-in.

BASiCSSimulate

7

Cell parameters cell.params A data.frame containing gene parameters with two coloumns:
Phi (mRNA content factor for each cell, scaled to sum to the number of cells in each
batch) and S (capture efficient for each cell).
Variability parameters theta Technical variability parameter for each batch.
The parameters not shown in brackets can be estimated from real data using BASiCSEstimate. For
details of the BASiCS simulation see BASiCSSimulate.

BASiCSSimulate

BASiCS simulation

Description
Simulate counts using the BASiCS method.
Usage
BASiCSSimulate(params = newBASiCSParams(), verbose = TRUE, ...)
Arguments
params

BASiCSParams object containing simulation parameters.

verbose

logical. Whether to print progress messages

...

any additional parameter settings to override what is provided in params.

Details
This function is just a wrapper around BASiCS_Sim that takes a BASiCSParams, runs the simulation
then converts the output to a SingleCellExperiment object. See BASiCS_Sim for more details of
how the simulation works.
Value
SingleCellExperiment containing simulated counts
References
Vallejos CA, Marioni JC, Richardson S. BASiCS: Bayesian Analysis of Single-Cell Sequencing
data. PLoS Comput. Biol. (2015).
Paper: 10.1371/journal.pcbi.1004333
Code: https://github.com/catavallejos/BASiCS
Examples
sim <- BASiCSSimulate()

8

compareSCEs

bridge

Brownian bridge

Description
Calculate a smoothed Brownian bridge between two points. A Brownian bridge is a random walk
with fixed end points.
Usage
bridge(x = 0, y = 0, N = 5, n = 100, sigma.fac = 0.8)
Arguments
x

starting value.

y

end value.

N

number of steps in random walk.

n

number of points in smoothed bridge.

sigma.fac

multiplier specifying how extreme each step can be.

Value
Vector of length n following a path from x to y.

compareSCEs

Compare SingleCellExperiment objects

Description
Combine the data from several SingleCellExperiment objects and produce some basic plots comparing them.
Usage
compareSCEs(sces, point.size = 0.1, 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.

diffSCEs

9

Details
The returned list has three items:
FeatureData Combined feature data from the provided SingleCellExperiments.
PhenoData Combined pheno data from the provided SingleCellExperiments.
Plots Comparison plots
Means Boxplot of mean distribution.
Variances Boxplot of variance distribution.
MeanVar Scatter plot with fitted lines showing the mean-variance relationship.
LibraySizes Boxplot of the library size distribution.
ZerosGene Boxplot of the percentage of each gene 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 <- splatSimulate(nGenes = 1000, batchCells = 20)
sim2 <- simpleSimulate(nGenes = 1000, nCells = 20)
comparison <- compareSCEs(list(Splat = sim1, Simple = sim2))
names(comparison)
names(comparison$Plots)

diffSCEs

Diff SingleCellExperiment objects

Description
Combine the data from several SingleCellExperiment objects and produce some basic plots comparing them to a reference.
Usage
diffSCEs(sces, ref, point.size = 0.1, point.alpha = 0.1, fits = TRUE,
colours = NULL)
Arguments
sces
ref
point.size
point.alpha
fits
colours

named list of SingleCellExperiment objects to combine and compare.
string giving the name of the SingleCellExperiment to use as the reference
size of points in scatter plots.
opacity of points in scatter plots.
whether to include fits in scatter plots.
vector of colours to use for each dataset.

10

diffSCEs

Details
This function aims to look at the differences between a reference SingleCellExperiment and one or
more others. It requires each SingleCellExperiment to have the same dimensions. Properties are
compared by ranks, for example when comparing the means the values are ordered and the differences between the reference and another dataset plotted. A series of Q-Q plots are also returned.
The returned list has five items:
Reference The SingleCellExperiment used as the reference.
FeatureData Combined feature data from the provided SingleCellExperiments.
PhenoData Combined pheno data from the provided SingleCellExperiments.
Plots Difference plots
Means Boxplot of mean differences.
Variances Boxplot of variance differences.
MeanVar Scatter plot showing the difference from the reference variance across expression
ranks.
LibraySizes Boxplot of the library size differences.
ZerosGene Boxplot of the differences in the percentage of each gene that is zero.
ZerosCell Boxplot of the differences in the percentage of each cell that is zero.
MeanZeros Scatter plot showing the difference from the reference percentage of zeros across
expression ranks.
QQPlots Quantile-Quantile plots
Means Q-Q plot of the means.
Variances Q-Q plot of the variances.
LibrarySizes Q-Q plot of the library sizes.
ZerosGene Q-Q plot of the percentage of zeros per gene.
ZerosCell Q-Q plot of the percentage of zeros per cell.
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 <- splatSimulate(nGenes = 1000, batchCells = 20)
sim2 <- simpleSimulate(nGenes = 1000, nCells = 20)
difference <- diffSCEs(list(Splat = sim1, Simple = sim2), ref = "Simple")
names(difference)
names(difference$Plots)

expandParams

expandParams

11

Expand parameters

Description
Expand the parameters that can be vectors so that they are the same length as the number of groups.
Usage
expandParams(object, ...)
## S4 method for signature 'BASiCSParams'
expandParams(object)
## S4 method for signature 'LunParams'
expandParams(object)
## S4 method for signature 'SplatParams'
expandParams(object)
Arguments
object
...

object to expand.
additional arguments.

Value
Expanded object.
getLNormFactors

Get log-normal factors

Description
Randomly generate multiplication factors from a log-normal distribution.
Usage
getLNormFactors(n.facs, sel.prob, neg.prob, fac.loc, fac.scale)
Arguments
n.facs
sel.prob
neg.prob
fac.loc
fac.scale

Number of factors to generate.
Probability that a factor will be selected to be different from 1.
Probability that a selected factor is less than one.
Location parameter for the log-normal distribution.
Scale factor for the log-normal distribution.

Value
Vector containing generated factors.

12

getParams

getParam

Get a parameter

Description
Accessor function for getting parameter values.
Usage
getParam(object, name)
## S4 method for signature 'Params'
getParam(object, name)
Arguments
object

object to get parameter from.

name

name of the parameter to get.

Value
The extracted parameter value
Examples
params <- newSimpleParams()
getParam(params, "nGenes")

getParams

Get parameters

Description
Get multiple parameter values from a Params object.
Usage
getParams(params, names)
Arguments
params

Params object to get values from.

names

vector of names of the parameters to get.

Value
List with the values of the selected parameters.

getPathOrder

13

Examples
params <- newSimpleParams()
getParams(params, c("nGenes", "nCells", "mean.rate"))

getPathOrder

Get path order

Description
Identify the correct order to process paths so that preceding paths have already been simulated.
Usage
getPathOrder(path.from)
Arguments
path.from

vector giving the path endpoints that each path orginates from.

Value
Vector giving the order to process paths in.

listSims

List simulations

Description
List all the simulations that are currently available in Splatter with a brief description.
Usage
listSims(print = TRUE)
Arguments
print

logical. Whether to print to the console.

Value
Invisibly returns a data.frame containing the information that is displayed.
Examples
listSims()

14

lun2Estimate

logistic

Logistic function

Description
Implementation of the logistic function
Usage
logistic(x, x0, k)
Arguments
x

value to apply the function to.

x0

midpoint parameter. Gives the centre of the function.

k

shape parameter. Gives the slope of the function.

Value
Value of logistic funciton with given parameters

lun2Estimate

Estimate Lun2 simulation parameters

Description
Estimate simulation parameters for the Lun2 simulation from a real dataset.
Usage
lun2Estimate(counts, plates, params = newLun2Params(), min.size = 200,
verbose = TRUE, BPPARAM = SerialParam())
## S3 method for class 'SingleCellExperiment'
lun2Estimate(counts, plates,
params = newLun2Params(), min.size = 200, verbose = TRUE,
BPPARAM = SerialParam())
## S3 method for class 'matrix'
lun2Estimate(counts, plates, params = newLun2Params(),
min.size = 200, verbose = TRUE, BPPARAM = SerialParam())

Lun2Params

15

Arguments
counts

either a counts matrix or a SingleCellExperiment object containing count data
to estimate parameters from.

plates

integer vector giving the plate that each cell originated from.

params

Lun2Params object to store estimated values in.

min.size

minimum size of clusters when identifying group of cells in the data.

verbose

logical. Whether to show progress messages.

BPPARAM

A BiocParallelParam instance giving the parallel back-end to be used. Default
is SerialParam which uses a single core.

Details
See Lun2Params for more details on the parameters.
Value
LunParams object containing the estimated parameters.
Examples
## Not run:
# Load example data
library(scater)
data("sc_example_counts")
data("sc_example_cell_info")
plates <- factor(sc_example_cell_info$Mutation_Status)
params <- lun2Estimate(sc_example_counts, plates, min.size = 20)
params
## End(Not run)

Lun2Params

The Lun2Params class

Description
S4 class that holds parameters for the Lun2 simulation.
Parameters
The Lun2 simulation uses the following parameters:
nGenes The number of genes to simulate.
nCells The number of cells to simulate.
[seed] Seed to use for generating random numbers.
Gene parameters gene.params A data.frame containing gene parameters with two coloumns:
Mean (mean expression for each gene) and Disp (dispersion for each gene).

16

lun2Simulate
zi.params A data.frame containing zero-inflated gene parameters with three coloumns:
Mean (mean expression for each gene), Disp (dispersion for each, gene), and Prop (zero
proportion for each gene).
[nPlates] The number of plates to simulate.
Plate parameters plate.ingroup Character vecotor giving the plates considered to be part of the
"ingroup".
plate.mod Plate effect modifier factor. The plate effect variance is divided by this value.
plate.var Plate effect variance.
Cell parameters cell.plates Factor giving the plate that each cell comes from.
cell.libSizes Library size for each cell.
cell.libMod Modifier factor for library sizes. The library sizes are multiplied by this value.
Differential expression parameters de.nGenes Number of differentially expressed genes.
de.fc Fold change for differentially expressed genes.
The parameters not shown in brackets can be estimated from real data using lun2Estimate. For
details of the Lun2 simulation see lun2Simulate.

lun2Simulate

Lun2 simulation

Description
Simulate single-cell RNA-seq count data using the method described in Lun and Marioni "Overcoming confounding plate effects in differential expression analyses of single-cell RNA-seq data".
Usage
lun2Simulate(params = newLun2Params(), zinb = FALSE, verbose = TRUE,
...)
Arguments
params

Lun2Params object containing simulation parameters.

zinb

logical. Whether to use a zero-inflated model.

verbose

logical. Whether to print progress messages

...

any additional parameter settings to override what is provided in params.

Details
The Lun2 simulation uses a negative-binomial distribution where the means and dispersions have
been sampled from a real dataset (using lun2Estimate). The other core feature of the Lun2 simulation is the addition of plate effects. Differential expression can be added between two groups
of plates (an "ingroup" and all other plates). Library size factors are also applied and optionally a
zero-inflated negative-binomial can be used.
If the number of genes to simulate differs from the number of provied gene parameters or the
number of cells to simulate differs from the number of library sizes the relevant paramters will be
sampled with a warning. This allows any number of genes or cells to be simulated regardless of the
number in the dataset used in the estimation step but has the downside that some genes or cells may
be simulated multiple times.

lunEstimate

17

Value
SingleCellExperiment containing simulated counts.
References
Lun ATL, Marioni JC. Overcoming confounding plate effects in differential expression analyses of
single-cell RNA-seq data. Biostatistics (2017).
Paper: dx.doi.org/10.1093/biostatistics/kxw055
Code: https://github.com/MarioniLab/PlateEffects2016
Examples
sim <- lun2Simulate()

lunEstimate

Estimate Lun simulation parameters

Description
Estimate simulation parameters for the Lun simulation from a real dataset.
Usage
lunEstimate(counts, params = newLunParams())
## S3 method for class 'SingleCellExperiment'
lunEstimate(counts,
params = newLunParams())
## S3 method for class 'matrix'
lunEstimate(counts, params = newLunParams())
Arguments
counts

either a counts matrix or an SingleCellExperiment object containing count data
to estimate parameters from.

params

LunParams object to store estimated values in.

Details
The nGenes and nCells parameters are taken from the size of the input data. No other parameters
are estimated. See LunParams for more details on the parameters.
Value
LunParams object containing the estimated parameters.

18

LunParams

Examples
# Load example data
library(scater)
data("sc_example_counts")
params <- lunEstimate(sc_example_counts)
params

LunParams

The LunParams class

Description
S4 class that holds parameters for the Lun simulation.

Parameters
The Lun simulation uses the following parameters:
nGenes The number of genes to simulate.
nCells The number of cells to simulate.
[nGroups] The number of groups to simulate.
[groupCells] Vector giving the number of cells in each simulation group/path.
[seed] Seed to use for generating random numbers.
Mean parameters [mean.shape] Shape parameter for the mean gamma distribution.
[mean.rate] Rate parameter for the mean gamma distribution.
Counts parameters [count.disp] The dispersion parameter for the counts negative binomial distribution.
Differential expression parameters [de.nGenes] The number of genes that are differentially expressed in each group
[de.upProp] The proportion of differentially expressed genes that are up-regulated in each
group
[de.upFC] The fold change for up-regulated genes
[de.downFC] The fold change for down-regulated genes
The parameters not shown in brackets can be estimated from real data using lunEstimate. For
details of the Lun simulation see lunSimulate.

lunSimulate

19

lunSimulate

Lun simulation

Description
Simulate single-cell RNA-seq count data using the method described in Lun, Bach and Marioni
"Pooling across cells to normalize single-cell RNA sequencing data with many zero counts".
Usage
lunSimulate(params = newLunParams(), verbose = TRUE, ...)
Arguments
params

LunParams object containing Lun simulation parameters.

verbose

logical. Whether to print progress messages.

...

any additional parameter settings to override what is provided in params.

Details
The Lun simulation generates gene mean expression levels from a gamma distribution with shape = mean.shape
and rate = mean.rate. Counts are then simulated from a negative binomial distribution with
mu = means and size = 1 / bcv.common. In addition each cell is given a size factor (2 ^ rnorm(nCells, mean = 0, sd
and differential expression can be simulated with fixed fold changes.
See LunParams for details of the parameters.
Value
SingleCellExperiment object containing the simulated counts and intermediate values.
References
Lun ATL, Bach K, Marioni JC. Pooling across cells to normalize single-cell RNA sequencing data
with many zero counts. Genome Biology (2016).
Paper: dx.doi.org/10.1186/s13059-016-0947-7
Code: https://github.com/MarioniLab/Deconvolution2016
Examples
sim <- lunSimulate()

20

makeDiffPanel

makeCompPanel

Make comparison panel

Description
Combine the plots from compareSCEs into a single panel.
Usage
makeCompPanel(comp, title = "Comparison", labels = c("Means",
"Variance", "Mean-variance relationship", "Library size",
"Zeros per gene", "Zeros per cell", "Mean-zeros relationship"))
Arguments
comp

list returned by compareSCEs.

title

title for the panel.

labels

vector of labels for each of the seven plots.

Value
Combined panel plot
Examples
## Not run:
sim1 <- splatSimulate(nGenes = 1000, batchCells = 20)
sim2 <- simpleSimulate(nGenes = 1000, nCells = 20)
comparison <- compareSCEs(list(Splat = sim1, Simple = sim2))
panel <- makeCompPanel(comparison)
## End(Not run)

makeDiffPanel

Make difference panel

Description
Combine the plots from diffSCEs into a single panel.
Usage
makeDiffPanel(diff, title = "Difference comparison",
labels = c("Means", "Variance", "Library size", "Zeros per cell",
"Zeros per gene", "Mean-variance relationship",
"Mean-zeros relationship"))

makeOverallPanel

21

Arguments
diff

list returned by diffSCEs.

title

title for the panel.

labels

vector of labels for each of the seven sections.

Value
Combined panel plot
Examples
## Not run:
sim1 <- splatSimulate(nGenes = 1000, batchCells = 20)
sim2 <- simpleSimulate(nGenes = 1000, nCells = 20)
difference <- diffSCEs(list(Splat = sim1, Simple = sim2), ref = "Simple")
panel <- makeDiffPanel(difference)
## End(Not run)

makeOverallPanel

Make overall panel

Description
Combine the plots from compSCEs and diffSCEs into a single panel.
Usage
makeOverallPanel(comp, diff, title = "Overall comparison",
row.labels = c("Means", "Variance", "Mean-variance relationship",
"Library size", "Zeros per cell", "Zeros per gene",
"Mean-zeros relationship"))
Arguments
comp

list returned by compareSCEs.

diff

list returned by diffSCEs.

title

title for the panel.

row.labels

vector of labels for each of the seven rows.

Value
Combined panel plot

22

mfaEstimate

Examples
## Not run:
sim1 <- splatSimulate(nGenes = 1000, batchCells = 20)
sim2 <- simpleSimulate(nGenes = 1000, nCells = 20)
comparison <- compSCEs(list(Splat = sim1, Simple = sim2))
difference <- diffSCEs(list(Splat = sim1, Simple = sim2), ref = "Simple")
panel <- makeOverallPanel(comparison, difference)
## End(Not run)

mfaEstimate

Estimate mfa simulation parameters

Description
Estimate simulation parameters for the mfa simulation from a real dataset.
Usage
mfaEstimate(counts, params = newMFAParams())
## S3 method for class 'SingleCellExperiment'
mfaEstimate(counts,
params = newMFAParams())
## S3 method for class 'matrix'
mfaEstimate(counts, params = newMFAParams())
Arguments
counts

either a counts matrix or a SingleCellExperiment object containing count data
to estimate parameters from.

params

MFAParams object to store estimated values in.

Details
The nGenes and nCells parameters are taken from the size of the input data. The dropout lambda
parameter is estimate using empirical_lambda. See MFAParams for more details on the parameters.
Value
MFAParams object containing the estimated parameters.
Examples
# Load example data
library(scater)
data("sc_example_counts")
params <- mfaEstimate(sc_example_counts)
params

MFAParams

MFAParams

23

The MFAParams class

Description
S4 class that holds parameters for the mfa simulation.
Parameters
The mfa simulation uses the following parameters:
nGenes The number of genes to simulate.
nCells The number of cells to simulate.
[seed] Seed to use for generating random numbers.
[trans.prop] Proportion of genes that show transient expression. These genes are briefly up or
down-regulated before returning to their initial state
[zero.neg] Logical. Whether to set negative expression values to zero. This will zero-inflate the
data.
[dropout.present] Logical. Whether to simulate dropout.
dropout.lambda Lambda parameter for the exponential dropout function.
The parameters not shown in brackets can be estimated from real data using mfaEstimate. See
create_synthetic for more details about the parameters. For details of the Splatter implementation of the mfa simulation see mfaSimulate.

mfaSimulate

MFA simulation

Description
Simulate a bifurcating pseudotime path using the mfa method.
Usage
mfaSimulate(params = newMFAParams(), verbose = TRUE, ...)
Arguments
params

MFAParams object containing simulation parameters.

verbose

Logical. Whether to print progress messages.

...

any additional parameter settings to override what is provided in params.

Details
This function is just a wrapper around create_synthetic that takes a MFAParams, runs the simulation then converts the output from log-expression to counts and returns a SingleCellExperiment
object. See create_synthetic and the mfa paper for more details about how the simulation works.

24

newParams

Value
SingleCellExperiment containing simulated counts
References
Campbell KR, Yau C. Probabilistic modeling of bifurcations in single-cell gene expression data
using a Bayesian mixture of factor analyzers. Wellcome Open Research (2017).
Paper: 10.12688/wellcomeopenres.11087.1
Code: https://github.com/kieranrcampbell/mfa
Examples
sim <- mfaSimulate()

newParams

New Params

Description
Create a new Params object. Functions exist for each of the different Params subtypes.
Usage
newBASiCSParams(...)
newLun2Params(...)
newLunParams(...)
newMFAParams(...)
newPhenoParams(...)
newSCDDParams(...)
newSimpleParams(...)
newSparseDCParams(...)
newSplatParams(...)
newZINBParams(...)
Arguments
...

additional parameters passed to setParams.

Value
New Params object.

Params

25

Examples
params <- newSimpleParams()
params <- newSimpleParams(nGenes = 200, nCells = 10)

Params

The Params virtual class

Description
Virtual S4 class that all other Params classes inherit from.
Parameters
The Params class defines the following parameters:
nGenes The number of genes to simulate.
nCells The number of cells to simulate.
[seed] Seed to use for generating random numbers.
The parameters not shown in brackets can be estimated from real data.

phenoEstimate

Estimate PhenoPath simulation parameters

Description
Estimate simulation parameters for the PhenoPath simulation from a real dataset.
Usage
phenoEstimate(counts, params = newPhenoParams())
## S3 method for class 'SingleCellExperiment'
phenoEstimate(counts,
params = newPhenoParams())
## S3 method for class 'matrix'
phenoEstimate(counts, params = newPhenoParams())
Arguments
counts

either a counts matrix or an SingleCellExperiment object containing count data
to estimate parameters from.

params

PhenoParams object to store estimated values in.

Details
The nGenes and nCells parameters are taken from the size of the input data. The total number of
genes is evenly divided into the four types. See PhenoParams for more details on the parameters.

26

phenoSimulate

Value
PhenoParams object containing the estimated parameters.
Examples
# Load example data
library(scater)
data("sc_example_counts")
params <- phenoEstimate(sc_example_counts)
params

PhenoParams

The PhenoParams class

Description
S4 class that holds parameters for the PhenoPath simulation.
Parameters
The PhenoPath simulation uses the following parameters:
nGenes The number of genes to simulate.
nCells The number of cells to simulate.
[seed] Seed to use for generating random numbers.
[n.de] Number of genes to simulate from the differential expression regime
[n.pst] Number of genes to simulate from the pseudotime regime
[n.pst.beta] Number of genes to simulate from the pseudotime + beta interactions regime
[n.de.pst.beta] Number of genes to simulate from the differential expression + pseudotime +
interactions regime
The parameters not shown in brackets can be estimated from real data using phenoEstimate. For
details of the PhenoPath simulation see phenoSimulate.

phenoSimulate

PhenoPath simulation

Description
Simulate counts from a pseudotime trajectory using the PhenoPath method.
Usage
phenoSimulate(params = newPhenoParams(), verbose = TRUE, ...)

rbindMatched

27

Arguments
params

PhenoParams object containing simulation parameters.

verbose

logical. Whether to print progress messages

...

any additional parameter settings to override what is provided in params.

Details
This function is just a wrapper around simulate_phenopath that takes a PhenoParams, runs the
simulation then converts the output from log-expression to counts and returns a SingleCellExperiment
object. The original simulated log-expression values are returned in the LogExprs assay. See
simulate_phenopath and the PhenoPath paper for more details about how the simulation works.
Value
SingleCellExperiment containing simulated counts
References
Campbell K, Yau C. Uncovering genomic trajectories with heterogeneous genetic and environmental backgrounds across single-cells and populations. bioRxiv (2017).
Paper: 10.1101/159913
Code: https://github.com/kieranrcampbell/phenopath
Examples
sim <- phenoSimulate()

rbindMatched

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.

28

scDDEstimate

scDDEstimate

Estimate scDD simulation parameters

Description
Estimate simulation parameters for the scDD simulation from a real dataset.
Usage
scDDEstimate(counts, params = newSCDDParams(), verbose = TRUE,
BPPARAM = SerialParam(), ...)
## S3 method for class 'matrix'
scDDEstimate(counts, params = newSCDDParams(),
verbose = TRUE, BPPARAM = SerialParam(), conditions, ...)
## S3 method for class 'SingleCellExperiment'
scDDEstimate(counts,
params = newSCDDParams(), verbose = TRUE, BPPARAM = SerialParam(),
condition = "condition", ...)
## Default S3 method:
scDDEstimate(counts, params = newSCDDParams(),
verbose = TRUE, BPPARAM = SerialParam(), condition, ...)
Arguments
counts

either a counts matrix or a SingleCellExperiment object containing count data
to estimate parameters from.

params

SCDDParams object to store estimated values in.

verbose

logical. Whether to show progress messages.

BPPARAM

A BiocParallelParam instance giving the parallel back-end to be used. Default
is SerialParam which uses a single core.

...

further arguments passed to or from other methods.

conditions

Vector giving the condition that each cell belongs to. Conditions can be 1 or 2.

condition

String giving the column that represents biological group of interest.

Details
This function applies preprocess to the counts then uses scDD to estimate the numbers of each
gene type to simulate. The output is then converted to a SCDDParams object. See preprocess and
scDD for details.
Value
SCDDParams object containing the estimated parameters.

SCDDParams

29

Examples
## Not run:
# Load example data
library(scater)
data("sc_example_counts")
conditions <- sample(1:2, ncol(sc_example_counts), replace = TRUE)
params <- scDDEstimate(sc_example_counts, conditions = conditions)
params
## End(Not run)

SCDDParams

The SCDDParams class

Description
S4 class that holds parameters for the scDD simulation.
Parameters
The SCDD simulation uses the following parameters:
nGenes The number of genes to simulate (not used).
nCells The number of cells to simulate in each condition.
[seed] Seed to use for generating random numbers.
SCdat SingleCellExperiment containing real data.
nDE Number of DE genes to simulate.
nDP Number of DP genes to simulate.
nDM Number of DM genes to simulate.
nDB Number of DB genes to simulate.
nEE Number of EE genes to simulate.
nEP Number of EP genes to simulate.
[sd.range] Interval for fold change standard deviations.
[modeFC] Values for DP, DM and DB mode fold changes.
[varInflation] Variance inflation factors for each condition. If all equal to 1 will be set to NULL
(default).
[condition] String giving the column that represents biological group of interest.
The parameters not shown in brackets can be estimated from real data using scDDEstimate. See
simulateSet for more details about the parameters. For details of the Splatter implementation of
the scDD simulation see scDDSimulate.

30

scDDSimulate

scDDSimulate

scDD simulation

Description
Simulate counts using the scDD method.
Usage
scDDSimulate(params = newSCDDParams(), plots = FALSE,
plot.file = NULL, verbose = TRUE, BPPARAM = SerialParam(), ...)
Arguments
params

SCDDParams object containing simulation parameters.

plots

logical. whether to generate scDD fold change and validation plots.

plot.file

File path to save plots as PDF.

verbose

logical. Whether to print progress messages

BPPARAM

A BiocParallelParam instance giving the parallel back-end to be used. Default
is SerialParam which uses a single core.

...

any additional parameter settings to override what is provided in params.

Details
This function is just a wrapper around simulateSet that takes a SCDDParams, runs the simulation
then converts the output to a SingleCellExperiment object. See simulateSet for more details
about how the simulation works.
Value
SingleCellExperiment containing simulated counts
References
Korthauer KD, Chu L-F, Newton MA, Li Y, Thomson J, Stewart R, et al. A statistical approach for
identifying differential distributions in single-cell RNA-seq experiments. Genome Biology (2016).
Paper: 10.1186/s13059-016-1077-y
Code: https://github.com/kdkorthauer/scDD
Examples
## Not run:
sim <- scDDSimulate()
## End(Not run)

setParam

setParam

31

Set a parameter

Description
Function for setting parameter values.
Usage
setParam(object, name, value)
## S4 method for signature 'BASiCSParams'
setParam(object, name, value)
## S4 method for signature 'Lun2Params'
setParam(object, name, value)
## S4 method for signature 'LunParams'
setParam(object, name, value)
## S4 method for signature 'Params'
setParam(object, name, value)
## S4 method for signature 'PhenoParams'
setParam(object, name, value)
## S4 method for signature 'SCDDParams'
setParam(object, name, value)
## S4 method for signature 'SplatParams'
setParam(object, name, value)
## S4 method for signature 'ZINBParams'
setParam(object, name, value)
Arguments
object

object to set parameter in.

name

name of the parameter to set.

value

value to set the paramter to.

Value
Object with new parameter value.
Examples
params <- newSimpleParams()
setParam(params, "nGenes", 100)

32

setParamsUnchecked

setParams

Set parameters

Description
Set multiple parameters in a Params object.
Usage
setParams(params, update = NULL, ...)
Arguments
params

Params object to set parameters in.

update

list of parameters to set where names(update) are the names of the parameters
to set and the items in the list are values.

...

additional parameters to set. These are combined with any parameters specified
in update.

Details
Each parameter is set by a call to setParam. If the same parameter is specified multiple times it will
be set multiple times. Parameters can be specified using a list via update (useful when collecting
parameter values in some way) or individually (useful when setting them manually), see examples.
Value
Params object with updated values.
Examples
params <- newSimpleParams()
params
# Set individually
params <- setParams(params, nGenes = 1000, nCells = 50)
params
# Set via update list
params <- setParams(params, list(mean.rate = 0.2, mean.shape = 0.8))
params

setParamsUnchecked

Set parameters UNCHECKED

Description
Set multiple parameters in a Params object.
Usage
setParamsUnchecked(params, update = NULL, ...)

setParamUnchecked

33

Arguments
params

Params object to set parameters in.

update

list of parameters to set where names(update) are the names of the parameters
to set and the items in the list are values.

...

additional parameters to set. These are combined with any parameters specified
in update.

Details
Each parameter is set by a call to setParam. If the same parameter is specified multiple times it will
be set multiple times. Parameters can be specified using a list via update (useful when collecting
parameter values in some way) or individually (useful when setting them manually), see examples.
THE FINAL OBJECT IS NOT CHECKED FOR VALIDITY!
Value
Params object with updated values.

setParamUnchecked

Set a parameter UNCHECKED

Description
Function for setting parameter values. THE OUTPUT IS NOT CHECKED FOR VALIDITY!
Usage
setParamUnchecked(object, name, value)
## S4 method for signature 'Params'
setParamUnchecked(object, name, value)
Arguments
object

object to set parameter in.

name

name of the parameter to set.

value

value to set the paramter to.

Value
Object with new parameter value.

34

showValues

showDFs

Show data.frame

Description
Function used for pretty printing data.frame parameters.
Usage
showDFs(dfs, not.default)
Arguments
dfs
not.default

list of data.frames to show.
logical vector giving which have changed from the default.

showPP

Show pretty print

Description
Function used for pretty printing params object.
Usage
showPP(params, pp)
Arguments
params
pp

object to show.
list specifying how the object should be displayed.

Value
Print params object to console
showValues

Show vales

Description
Function used for pretty printing scale or vector parameters.
Usage
showValues(values, not.default)
Arguments
values
not.default

list of values to show.
logical vector giving which have changed from the default.

simpleEstimate

simpleEstimate

35

Estimate simple simulation parameters

Description
Estimate simulation parameters for the simple simulation from a real dataset.

Usage
simpleEstimate(counts, params = newSimpleParams())
## S3 method for class 'SingleCellExperiment'
simpleEstimate(counts,
params = newSimpleParams())
## S3 method for class 'matrix'
simpleEstimate(counts, params = newSimpleParams())
Arguments
counts

either a counts matrix or a SingleCellExperiment object containing count data
to estimate parameters from.

params

SimpleParams object to store estimated values in.

Details
The nGenes and nCells parameters are taken from the size of the input data. The mean parameters
are estimated by fitting a gamma distribution to the library size normalised mean expression level
using fitdist. See SimpleParams for more details on the parameters.
Value
SimpleParams object containing the estimated parameters.

Examples
# Load example data
library(scater)
data("sc_example_counts")
params <- simpleEstimate(sc_example_counts)
params

36

simpleSimulate

SimpleParams

The SimpleParams class

Description
S4 class that holds parameters for the simple simulation.
Parameters
The simple simulation uses the following parameters:
nGenes The number of genes to simulate.
nCells The number of cells to simulate.
[seed] Seed to use for generating random numbers.
mean.shape The shape parameter for the mean gamma distribution.
mean.rate The rate parameter for the mean gamma distribution.
[count.disp] The dispersion parameter for the counts negative binomial distribution.
The parameters not shown in brackets can be estimated from real data using simpleEstimate. For
details of the simple simulation see simpleSimulate.

simpleSimulate

Simple simulation

Description
Simulate counts from a simple negative binomial distribution without simulated library sizes, differential expression etc.
Usage
simpleSimulate(params = newSimpleParams(), verbose = TRUE, ...)
Arguments
params

SimpleParams object containing simulation parameters.

verbose

logical. Whether to print progress messages

...

any additional parameter settings to override what is provided in params.

Details
Gene means are simulated from a gamma distribution with shape = mean.shape and rate = mean.rate.
Counts are then simulated from a negative binomial distribution with mu = means and size = 1 / counts.disp.
See SimpleParams for more details of the parameters.
Value
SingleCellExperiment containing simulated counts

sparseDCEstimate

37

Examples
sim <- simpleSimulate()
# Override default parameters
sim <- simpleSimulate(nGenes = 1000, nCells = 50)

sparseDCEstimate

Estimate SparseDC simulation parameters

Description
Estimate simulation parameters for the SparseDC simulation from a real dataset.
Usage
sparseDCEstimate(counts, conditions, nclusters, norm = TRUE,
params = newSparseDCParams())
## S3 method for class 'SingleCellExperiment'
sparseDCEstimate(counts, conditions,
nclusters, norm = TRUE, params = newSparseDCParams())
## S3 method for class 'matrix'
sparseDCEstimate(counts, conditions, nclusters,
norm = TRUE, params = newSparseDCParams())
Arguments
counts

either a counts matrix or an SingleCellExperiment object containing count data
to estimate parameters from.

conditions

numeric vector giving the condition each cell belongs to.

nclusters

number of cluster present in the dataset.

norm

logical, whether to libray size normalise counts before estimation. Set this to
FALSE if counts is already normalised.

params

PhenoParams object to store estimated values in.

Details
The nGenes and nCells parameters are taken from the size of the input data. The counts are
preprocessed using pre_proc_data and then parameters are estimated using sparsedc_cluster
using lambda values calculated using lambda1_calculator and lambda2_calculator.
See SparseDCParams for more details on the parameters.
Value
SparseParams object containing the estimated parameters.

38

SparseDCParams

Examples
# Load example data
library(scater)
data("sc_example_counts")
set.seed(1)
conditions <- sample(1:2, ncol(sc_example_counts), replace = TRUE)
params <- sparseDCEstimate(sc_example_counts[1:500, ], conditions,
nclusters = 3)
params

SparseDCParams

The SparseDCParams class

Description
S4 class that holds parameters for the SparseDC simulation.
Parameters
The SparseDC simulation uses the following parameters:
nGenes The number of genes to simulate in each condition.
nCells The number of cells to simulate.
[seed] Seed to use for generating random numbers.
markers.n Number of marker genes to simulate for each cluster.
markers.shared Number of marker genes for each cluster shared between conditions. Must be
less than or equal to markers.n.
[markers.same] Logical. Whether each cluster should have the same set of marker genes.
clusts.c1 Numeric vector of clusters present in condition 1. The number of times a cluster is
repeated controls the proportion of cells from that cluster.
clusts.c2 Numeric vector of clusters present in condition 2. The number of times a cluster is
repeated controls the proportion of cells from that cluster.
[mean.lower] Lower bound for cluster gene means.
[mean.upper] Upper bound for cluster gene means.
The parameters not shown in brackets can be estimated from real data using sparseDCEstimate.
For details of the SparseDC simulation see sparseDCSimulate.

sparseDCSimulate

sparseDCSimulate

39

SparseDC simulation

Description
Simulate counts from cluster in two conditions using the SparseDC method.

Usage
sparseDCSimulate(params = newSparseDCParams(), verbose = TRUE, ...)
Arguments
params

SparseDCParams object containing simulation parameters.

verbose

logical. Whether to print progress messages

...

any additional parameter settings to override what is provided in params.

Details
This function is just a wrapper around sim_data that takes a SparseDCParams, runs the simulation then converts the output from log-expression to counts and returns a SingleCellExperiment
object. The original simulated log-expression values are returned in the LogExprs assay. See
sim_data and the SparseDC paper for more details about how the simulation works.
Value
SingleCellExperiment containing simulated counts

References
Campbell K, Yau C. Uncovering genomic trajectories with heterogeneous genetic and environmental backgrounds across single-cells and populations. bioRxiv (2017).
Barron M, Zhang S, Li J. A sparse differential clustering algorithm for tracing cell type changes via
single-cell RNA-sequencing data. Nucleic Acids Research (2017).
Paper: 10.1093/nar/gkx1113
Examples
sim <- sparseDCSimulate()

40

splatEstDropout

splatEstBCV

Estimate Splat Biological Coefficient of Variation parameters

Description
Parameters are estimated using the estimateDisp function in the edgeR package.
Usage
splatEstBCV(counts, params)
Arguments
counts

counts matrix to estimate parameters from.

params

SplatParams object to store estimated values in.

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 edgR estimate,
therefore we apply a small correction, disp = 0.1 + 0.25 * edgeR.disp.
Value
SplatParams object with estimated values.

splatEstDropout

Estimate Splat dropout parameters

Description
Estimate the midpoint and shape parameters for the logistic function used when simulating dropout.
Usage
splatEstDropout(norm.counts, params)
Arguments
norm.counts

library size normalised counts matrix.

params

SplatParams object to store estimated values in.

Details
Logistic function parameters are estimated by fitting a logistic function to the relationship between
log2 mean gene expression and the proportion of zeros in each gene. See nls for details of fitting.
Note this is done on the experiment level, more granular (eg. group or cell) level dropout is not
estimated.

splatEstimate

41

Value
SplatParams object with estimated values.

splatEstimate

Estimate Splat simulation parameters

Description
Estimate simulation parameters for the Splat simulation from a real dataset. See the individual
estimation functions for more details on how this is done.
Usage
splatEstimate(counts, params = newSplatParams())
## S3 method for class 'SingleCellExperiment'
splatEstimate(counts,
params = newSplatParams())
## S3 method for class 'matrix'
splatEstimate(counts, params = newSplatParams())
Arguments
counts

either a counts matrix or a SingleCellExperiment object containing count data
to estimate parameters from.

params

SplatParams object to store estimated values in.

Value
SplatParams object containing the estimated parameters.
See Also
splatEstMean, splatEstLib, splatEstOutlier, splatEstBCV, splatEstDropout
Examples
# Load example data
library(scater)
data("sc_example_counts")
params <- splatEstimate(sc_example_counts)
params

42

splatEstMean

splatEstLib

Estimate Splat library size parameters

Description
The Shapiro-Wilks test is used to determine if the library sizes are normally distributed. If so a
normal distribution is fitted to the library sizes, if not (most cases) a log-normal distribution is fitted
and the estimated parameters are added to the params object. See fitdist for details on the fitting.
Usage
splatEstLib(counts, params)
Arguments
counts

counts matrix to estimate parameters from.

params

splatParams object to store estimated values in.

Value
splatParams object with estimated values.

splatEstMean

Estimate Splat mean parameters

Description
Estimate rate and shape parameters for the gamma distribution used to simulate gene expression
means.
Usage
splatEstMean(norm.counts, params)
Arguments
norm.counts

library size normalised counts matrix.

params

SplatParams object to store estimated values in.

Details
Parameter for the gamma distribution are estimated by fitting the mean normalised counts using
fitdist. The ’maximum goodness-of-fit estimation’ method is used to minimise the Cramer-von
Mises distance. This can fail in some situations, in which case the ’method of moments estimation’
method is used instead. Prior to fitting the means are winsorized by setting the top and bottom 10
percent of values to the 10th and 90th percentiles.
Value
SplatParams object with estimated values.

splatEstOutlier

splatEstOutlier

43

Estimate Splat expression outlier parameters

Description
Parameters are estimated by comparing means of individual genes to the median mean expression
level.
Usage
splatEstOutlier(norm.counts, params)
Arguments
norm.counts

library size normalised counts matrix.

params

SplatParams object to store estimated values in.

Details
Expression outlier genes are detected using the Median Absolute Deviation (MAD) from median
method. If the log2 mean expression of a gene is greater than two MADs above the median log2
mean expression it is designated as an outlier. The proportion of outlier genes is used to estimate
the outlier probability. Factors for each outlier gene are calculated by dividing mean expression by
the median mean expression. A log-normal distribution is then fitted to these factors in order to
estimate the outlier factor location and scale parameters using fitdist.
Value
SplatParams object with estimated values.

SplatParams

The SplatParams class

Description
S4 class that holds parameters for the Splatter simulation.
Parameters
The Splatter simulation requires the following parameters:
nGenes The number of genes to simulate.
nCells The number of cells to simulate.
[seed] Seed to use for generating random numbers.
Batch parameters [nBatches] The number of batches to simulate.
[batchCells] Vector giving the number of cells in each batch.
[batch.facLoc] Location (meanlog) parameter for the batch effect factor log-normal distribution. Can be a vector.

44

SplatParams
[batch.facScale] Scale (sdlog) parameter for the batch effect factor log-normal distribution. Can be a vector.
Mean parameters mean.shape Shape parameter for the mean gamma distribution.
mean.rate Rate parameter for the mean gamma distribution.
Library size parameters lib.loc Location (meanlog) parameter for the library size log-normal
distribution, or mean parameter if a normal distribution is used.
lib.scale Scale (sdlog) parameter for the library size log-normal distribution, or sd parameter if a normal distribution is used.
lib.norm Logical. Whether to use a normal distribution for library sizes instead of a lognormal.
Expression outlier parameters out.prob Probability that a gene is an expression outlier.
out.facLoc Location (meanlog) parameter for the expression outlier factor log-normal distribution.
out.facScale Scale (sdlog) parameter for the expression outlier factor log-normal distribution.
Group parameters [nGroups] The number of groups or paths to simulate.
[group.prob] Probability that a cell comes from a group.
Differential expression parameters [de.prob] Probability that a gene is differentially expressed
in a group. Can be a vector.
[de.loProb] Probability that a differentially expressed gene is down-regulated. Can be a
vector.
[de.facLoc] Location (meanlog) parameter for the differential expression factor log-normal
distribution. Can be a vector.
[de.facScale] Scale (sdlog) parameter for the differential expression factor log-normal distribution. Can be a vector.
Biological Coefficient of Variation parameters bcv.common Underlying common dispersion across
all genes.
bcv.df Degrees of Freedom for the BCV inverse chi-squared distribution.
Dropout parameters dropout.type The type of dropout to simulate. "none" indicates no dropout,
"experiment" is global dropout using the same parameters for every cell, "batch" uses the
same parameters for every cell in each batch, "group" uses the same parameters for every
cell in each groups and "cell" uses a different set of parameters for each cell.
dropout.mid Midpoint parameter for the dropout logistic function.
dropout.shape Shape parameter for the dropout logistic function.
Differentiation path parameters [path.from] Vector giving the originating point of each path.
This allows path structure such as a cell type which differentiates into an intermediate
cell type that then differentiates into two mature cell types. A path structure of this form
would have a "from" parameter of c(0, 1, 1) (where 0 is the origin). If no vector is given
all paths will start at the origin.
[path.length] Vector giving the number of steps to simulate along each path. If a single
value is given it will be applied to all paths.
[path.skew] Vector giving the skew of each path. Values closer to 1 will give more cells
towards the starting population, values closer to 0 will give more cells towards the final
population. If a single value is given it will be applied to all paths.
[path.nonlinearProb] Probability that a gene follows a non-linear path along the differentiation path. This allows more complex gene patterns such as a gene being equally
expressed at the beginning an end of a path but lowly expressed in the middle.

splatSimBatchCellMeans

45

[path.sigmaFac] Sigma factor for non-linear gene paths. A higher value will result in more
extreme non-linear variations along a path.
The parameters not shown in brackets can be estimated from real data using splatEstimate. For
details of the Splatter simulation see splatSimulate.

splatSimBatchCellMeans
Simulate batch means

Description
Simulate a mean for each gene in each cell incorporating batch effect factors.
Usage
splatSimBatchCellMeans(sim, params)
Arguments
sim

SingleCellExperiment to add batch means to.

params

SplatParams object with simulation parameters.

Value
SingleCellExperiment with simulated batch means.

splatSimBatchEffects

Simulate batch effects

Description
Simulate batch effects. Batch effect factors for each batch are produced using getLNormFactors
and these are added along with updated means for each batch.
Usage
splatSimBatchEffects(sim, params)
Arguments
sim

SingleCellExperiment to add batch effects to.

params

SplatParams object with simulation parameters.

Value
SingleCellExperiment with simulated batch effects.

46

splatSimCellMeans

splatSimBCVMeans

Simulate BCV means

Description
Simulate means for each gene in each cell that are adjusted to follow a mean-variance trend using
Biological Coefficient of Variation taken from and inverse gamma distribution.
Usage
splatSimBCVMeans(sim, params)
Arguments
sim

SingleCellExperiment to add BCV means to.

params

SplatParams object with simulation parameters.

Value
SingleCellExperiment with simulated BCV means.

splatSimCellMeans

Simulate cell means

Description
Simulate a gene by cell matrix giving the mean expression for each gene in each cell. Cells start with
the mean expression for the group they belong to (when simulating groups) or cells are assigned
the mean expression from a random position on the appropriate path (when simulating paths). The
selected means are adjusted for each cell’s expected library size.
Usage
splatSimSingleCellMeans(sim, params)
splatSimGroupCellMeans(sim, params)
splatSimPathCellMeans(sim, params)
Arguments
sim

SingleCellExperiment to add cell means to.

params

SplatParams object with simulation parameters.

Value
SingleCellExperiment with added cell means.

splatSimDE

splatSimDE

47

Simulate group differential expression

Description
Simulate differential expression. Differential expression factors for each group are produced using
getLNormFactors and these are added along with updated means for each group. For paths care is
taked to make sure they are simulated in the correct order.
Usage
splatSimGroupDE(sim, params)
splatSimPathDE(sim, params)
Arguments
sim

SingleCellExperiment to add differential expression to.

params

splatParams object with simulation parameters.

Value
SingleCellExperiment with simulated differential expression.

splatSimDropout

Simulate dropout

Description
A logistic function is used to form a relationshop between the expression level of a gene and the
probability of dropout, giving a probability for each gene in each cell. These probabilities are used
in a Bernoulli distribution to decide which counts should be dropped.
Usage
splatSimDropout(sim, params)
Arguments
sim

SingleCellExperiment to add dropout to.

params

SplatParams object with simulation parameters.

Value
SingleCellExperiment with simulated dropout and observed counts.

48

splatSimLibSizes

splatSimGeneMeans

Simulate gene means

Description
Simulate gene means from a gamma distribution. Also simulates outlier expression factors. Genes
with an outlier factor not equal to 1 are replaced with the median mean expression multiplied by the
outlier factor.
Usage
splatSimGeneMeans(sim, params)
Arguments
sim

SingleCellExperiment to add gene means to.

params

SplatParams object with simulation parameters.

Value
SingleCellExperiment with simulated gene means.

splatSimLibSizes

Simulate library sizes

Description
Simulate expected library sizes. Typically a log-normal distribution is used but there is also the
option to use a normal distribution. In this case any negative values are set to half the minimum
non-zero value.
Usage
splatSimLibSizes(sim, params)
Arguments
sim

SingleCellExperiment to add library size to.

params

SplatParams object with simulation parameters.

Value
SingleCellExperiment with simulated library sizes.

splatSimTrueCounts

49

splatSimTrueCounts

Simulate true counts

Description
Simulate a true counts matrix. Counts are simulated from a poisson distribution where Each gene
in each cell has it’s own mean based on the group (or path position), expected library size and BCV.
Usage
splatSimTrueCounts(sim, params)
Arguments
sim

SingleCellExperiment to add true counts to.

params

SplatParams object with simulation parameters.

Value
SingleCellExperiment with simulated true counts.

splatSimulate

Splat simulation

Description
Simulate count data from a fictional single-cell RNA-seq experiment using the Splat method.
Usage
splatSimulate(params = newSplatParams(), method = c("single", "groups",
"paths"), verbose = TRUE, ...)
splatSimulateSingle(params = newSplatParams(), verbose = TRUE, ...)
splatSimulateGroups(params = newSplatParams(), verbose = TRUE, ...)
splatSimulatePaths(params = newSplatParams(), verbose = TRUE, ...)
Arguments
params

SplatParams object containing parameters for the simulation. See SplatParams
for details.

method

which simulation method to use. Options are "single" which produces a single
population, "groups" which produces distinct groups (eg. cell types) or "paths"
which selects cells from continuous trajectories (eg. differentiation processes).

verbose

logical. Whether to print progress messages.

...

any additional parameter settings to override what is provided in params.

50

splatSimulate

Details
Parameters can be set in a variety of ways. If no parameters are provided the default parameters are
used. Any parameters in params can be overridden by supplying additional arguments through a
call to setParams. This design allows the user flexibility in how they supply parameters and allows
small adjustments without creating a new SplatParams object. See examples for a demonstration
of how this can be used.
The simulation involves the following steps:
1. Set up simulation object
2. Simulate library sizes
3. Simulate gene means
4. Simulate groups/paths
5. Simulate BCV adjusted cell means
6. Simulate true counts
7. Simulate dropout
8. Create final dataset
The final output is a SingleCellExperiment object that contains the simulated counts but also the
values for various intermediate steps. These are stored in the colData (for cell specific information),
rowData (for gene specific information) or assays (for gene by cell matrices) slots. This additional
information includes:
phenoData Cell Unique cell identifier.
Group The group or path the cell belongs to.
ExpLibSize The expected library size for that cell.
Step (paths only) how far along the path each cell is.
featureData Gene Unique gene identifier.
BaseGeneMean The base expression level for that gene.
OutlierFactor Expression outlier factor for that gene. Values of 1 indicate the gene is not an
expression outlier.
GeneMean Expression level after applying outlier factors.
BatchFac[Batch ] The batch effects factor for each gene for a particular batch.
DEFac[Group ] The differential expression factor for each gene in a particular group. Values
of 1 indicate the gene is not differentially expressed.
SigmaFac[Path ] Factor applied to genes that have non-linear changes in expression along a
path.
assayData BatchCellMeans The mean expression of genes in each cell after adding batch effects.
BaseCellMeans The mean expression of genes in each cell after any differential expression
and adjusted for expected library size.
BCV The Biological Coefficient of Variation for each gene in each cell.
CellMeans The mean expression level of genes in each cell adjusted for BCV.
TrueCounts The simulated counts before dropout.
Dropout Logical matrix showing which values have been dropped in which cells.
Values that have been added by Splatter are named using UpperCamelCase in order to differentiate
them from the values added by analysis packages which typically use underscore_naming.

splatter

51

Value
SingleCellExperiment object containing the simulated counts and intermediate values.
References
Zappia L, Phipson B, Oshlack A. Splatter: simulation of single-cell RNA sequencing data. Genome
Biology (2017).
Paper: 10.1186/s13059-017-1305-0
Code: https://github.com/Oshlack/splatter
See Also
splatSimLibSizes, splatSimGeneMeans, splatSimBatchEffects, splatSimBatchCellMeans,
splatSimDE, splatSimCellMeans, splatSimBCVMeans, splatSimTrueCounts, splatSimDropout
Examples
# Simulation with default parameters
sim <- splatSimulate()
## Not run:
# Simulation with different number of genes
sim <- splatSimulate(nGenes = 1000)
# Simulation with custom parameters
params <- newSplatParams(nGenes = 100, mean.rate = 0.5)
sim <- splatSimulate(params)
# Simulation with adjusted custom parameters
sim <- splatSimulate(params, mean.rate = 0.6, out.prob = 0.2)
# Simulate groups
sim <- splatSimulate(method = "groups")
# Simulate paths
sim <- splatSimulate(method = "paths")
## End(Not run)

splatter

splatter.

Description
splatter is a package for the well-documented and reproducible simulation of single-cell RNA-seq
count data.
Details
As well as it’s own simulation model splatter provides functions for the estimation of model parameters.
See Also
Zappia L, Phipson B, Oshlack A. Splatter: Simulation Of Single-Cell RNA Sequencing Data.
bioRxiv. 2017; doi:10.1101/133173

52

winsorize

summariseDiff

Summarise diffSCESs

Description
Summarise the results of diffSCEs. Calculates the Median Absolute Deviation (MAD), Mean
Absolute Error (MAE) and Root Mean Squared Error (RMSE) for the various properties and ranks
them.
Usage
summariseDiff(diff)
Arguments
diff

Output from diffSCEs

Value
data.frame with MADs, MAEs, RMSEs, scaled statistics and ranks
Examples
sim1 <- splatSimulate(nGenes = 1000, batchCells = 20)
sim2 <- simpleSimulate(nGenes = 1000, nCells = 20)
difference <- diffSCEs(list(Splat = sim1, Simple = sim2), ref = "Simple")
summary <- summariseDiff(difference)
head(summary)

winsorize

Winsorize vector

Description
Set outliers in a numeric vector to a specified percentile.
Usage
winsorize(x, q)
Arguments
x

Numeric vector to winsorize

q

Percentile to set from each end

Value
Winsorized numeric vector

zinbEstimate

zinbEstimate

53

Estimate ZINB-WaVE simulation parameters

Description
Estimate simulation parameters for the ZINB-WaVE simulation from a real dataset.
Usage
zinbEstimate(counts, design.samples = NULL, design.genes = NULL,
common.disp = TRUE, iter.init = 2, iter.opt = 25,
stop.opt = 1e-04, params = newZINBParams(), verbose = TRUE,
BPPARAM = SerialParam(), ...)
## S3 method for class 'SingleCellExperiment'
zinbEstimate(counts,
design.samples = NULL, design.genes = NULL, common.disp = TRUE,
iter.init = 2, iter.opt = 25, stop.opt = 1e-04,
params = newZINBParams(), verbose = TRUE, BPPARAM = SerialParam(),
...)
## S3 method for class 'matrix'
zinbEstimate(counts, design.samples = NULL,
design.genes = NULL, common.disp = TRUE, iter.init = 2,
iter.opt = 25, stop.opt = 1e-04, params = newZINBParams(),
verbose = TRUE, BPPARAM = SerialParam(), ...)
Arguments
counts

either a counts matrix or a SingleCellExperiment object containing count data
to estimate parameters from.

design.samples design matrix of sample-level covariates.
design.genes

design matrix of gene-level covariates.

common.disp

logical. Whether or not a single dispersion for all features is estimated.

iter.init

number of iterations to use for initalization.

iter.opt

number of iterations to use for optimization.

stop.opt

stopping criterion for optimization.

params

ZINBParams object to store estimated values in.

verbose

logical. Whether to print progress messages.

BPPARAM

A BiocParallelParam instance giving the parallel back-end to be used. Default
is SerialParam which uses a single core.

...

additional arguments passes to zinbFit.

Details
The function is a wrapper around zinbFit that takes the fitted model and inserts it into a ZINBParams
object. See ZINBParams for more details on the parameters and zinbFit for details of the estimation procedure.

54

zinbSimulate

Value
ZINBParams object containing the estimated parameters.
Examples
## Not run:
# Load example data
library(scater)
data("sc_example_counts")
params <- zinbEstimate(sc_example_counts)
params
## End(Not run)

ZINBParams

The ZINBParams class

Description
S4 class that holds parameters for the ZINB-WaVE simulation.
Parameters
The ZINB-WaVE simulation uses the following parameters:
nGenes The number of genes to simulate.
nCells The number of cells to simulate.
[seed] Seed to use for generating random numbers.
model Object describing a ZINB model.
The majority of the parameters for this simulation are stored in a ZinbModel object. Please refer to
the documentation for this class and its constructor(zinbModel) for details about all the parameters.
The parameters not shown in brackets can be estimated from real data using zinbEstimate. For
details of the ZINB-WaVE simulation see zinbSimulate.

zinbSimulate

ZINB-WaVE simulation

Description
Simulate counts using the ZINB-WaVE method.
Usage
zinbSimulate(params = newZINBParams(), verbose = TRUE, ...)

zinbSimulate

55

Arguments
params

ZINBParams object containing simulation parameters.

verbose

logical. Whether to print progress messages

...

any additional parameter settings to override what is provided in params.

Details
This function is just a wrapper around zinbSim that takes a ZINBParams, runs the simulation then
converts the output to a SingleCellExperiment object. See zinbSim and the ZINB-WaVE paper
for more details about how the simulation works.
Value
SingleCellExperiment containing simulated counts
References
Campbell K, Yau C. Uncovering genomic trajectories with heterogeneous genetic and environmental backgrounds across single-cells and populations. bioRxiv (2017).
Risso D, Perraudeau F, Gribkova S, Dudoit S, Vert J-P. ZINB-WaVE: A general and flexible method
for signal extraction from single-cell RNA-seq data bioRxiv (2017).
Paper: 10.1101/125112
Code: https://github.com/drisso/zinbwave
Examples
sim <- zinbSimulate()

Index
addFeatureStats, 3
addGeneLengths, 4
assays, 50

lun2Simulate, 16, 16
lunEstimate, 17, 18
LunParams, 17, 18, 19
LunParams-class (LunParams), 18
lunSimulate, 18, 19

BASiCS_MCMC, 5, 6
BASiCS_Sim, 7
BASiCSEstimate, 5, 7
BASiCSParams, 6, 7
BASiCSParams-class (BASiCSParams), 6
BASiCSSimulate, 7, 7
BiocParallelParam, 15, 28, 30, 53
bridge, 8

makeCompPanel, 20
makeDiffPanel, 20
makeOverallPanel, 21
mfaEstimate, 22, 23
MFAParams, 22, 23, 23
MFAParams-class (MFAParams), 23
mfaSimulate, 23, 23

colData, 50
compareSCEs, 8, 20, 21
create_synthetic, 23

newBASiCSParams (newParams), 24
newLun2Params (newParams), 24
newLunParams (newParams), 24
newMFAParams (newParams), 24
newParams, 24
newPhenoParams (newParams), 24
newSCDDParams (newParams), 24
newSimpleParams (newParams), 24
newSparseDCParams (newParams), 24
newSplatParams (newParams), 24
newZINBParams (newParams), 24
nls, 40

diffSCEs, 9, 21, 52
empirical_lambda, 22
estimateDisp, 40
expandParams, 11
expandParams,BASiCSParams-method
(expandParams), 11
expandParams,LunParams-method
(expandParams), 11
expandParams,SplatParams-method
(expandParams), 11

Params, 25
Params-class (Params), 25
phenoEstimate, 25, 26
PhenoParams, 25, 26, 27
PhenoParams-class (PhenoParams), 26
phenoSimulate, 26, 26
pre_proc_data, 37
preprocess, 28

fitdist, 35, 42, 43
getLNormFactors, 11, 45, 47
getParam, 12
getParam,Params-method (getParam), 12
getParams, 12
getPathOrder, 13
ggplot, 9, 10

rbindMatched, 27
rowData, 4, 50

lambda1_calculator, 37
lambda2_calculator, 37
listSims, 13
logistic, 14
lun2Estimate, 14, 16
Lun2Params, 15, 15
Lun2Params-class (Lun2Params), 15

scDD, 28
scDDEstimate, 28, 29
SCDDParams, 29, 30
SCDDParams-class (SCDDParams), 29
scDDSimulate, 29, 30
SerialParam, 15, 28, 30, 53
56

INDEX
setParam, 31, 32, 33
setParam,BASiCSParams-method
(setParam), 31
setParam,Lun2Params-method (setParam),
31
setParam,LunParams-method (setParam), 31
setParam,Params-method (setParam), 31
setParam,PhenoParams-method (setParam),
31
setParam,SCDDParams-method (setParam),
31
setParam,SplatParams-method (setParam),
31
setParam,ZINBParams-method (setParam),
31
setParams, 24, 32, 50
setParamsUnchecked, 32
setParamUnchecked, 33
setParamUnchecked,Params-method
(setParamUnchecked), 33
showDFs, 34
showPP, 34
showValues, 34
sim_data, 39
simpleEstimate, 35, 36
SimpleParams, 35, 36, 36
SimpleParams-class (SimpleParams), 36
simpleSimulate, 36, 36
simulate_phenopath, 27
simulateSet, 29, 30
SingleCellExperiment, 4, 7, 23, 27, 29, 30,
39, 50, 55
sparsedc_cluster, 37
sparseDCEstimate, 37, 38
SparseDCParams, 37, 38, 39
SparseDCParams-class (SparseDCParams),
38
sparseDCSimulate, 38, 39
splatEstBCV, 40, 41
splatEstDropout, 40, 41
splatEstimate, 41, 45
splatEstLib, 41, 42
splatEstMean, 41, 42
splatEstOutlier, 41, 43
SplatParams, 43, 49
SplatParams-class (SplatParams), 43
splatSimBatchCellMeans, 45, 51
splatSimBatchEffects, 45, 51
splatSimBCVMeans, 46, 51
splatSimCellMeans, 46, 51
splatSimDE, 47, 51
splatSimDropout, 47, 51

57
splatSimGeneMeans, 48, 51
splatSimGroupCellMeans
(splatSimCellMeans), 46
splatSimGroupDE (splatSimDE), 47
splatSimLibSizes, 48, 51
splatSimPathCellMeans
(splatSimCellMeans), 46
splatSimPathDE (splatSimDE), 47
splatSimSingleCellMeans
(splatSimCellMeans), 46
splatSimTrueCounts, 49, 51
splatSimulate, 45, 49
splatSimulateGroups (splatSimulate), 49
splatSimulatePaths (splatSimulate), 49
splatSimulateSingle (splatSimulate), 49
splatter, 51
splatter-package (splatter), 51
summariseDiff, 52
winsorize, 52
zinbEstimate, 53, 54
zinbFit, 53
ZinbModel, 54
zinbModel, 54
ZINBParams, 53, 54, 55
ZINBParams-class (ZINBParams), 54
zinbSim, 55
zinbSimulate, 54, 54



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