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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 <luke.zappia@mcri.edu.au>
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
2Rtopics documented:
Rtopics documented:
addFeatureStats....................................... 3
addGeneLengths ...................................... 4
BASiCSEstimate ...................................... 5
BASiCSParams ....................................... 6
BASiCSSimulate ...................................... 7
bridge ............................................ 8
compareSCEs........................................ 8
diffSCEs........................................... 9
expandParams........................................ 11
getLNormFactors ...................................... 11
getParam .......................................... 12
getParams.......................................... 12
getPathOrder ........................................ 13
listSims ........................................... 13
logistic............................................ 14
lun2Estimate ........................................ 14
Lun2Params......................................... 15
lun2Simulate ........................................ 16
lunEstimate ......................................... 17
LunParams ......................................... 18
lunSimulate ......................................... 19
makeCompPanel ...................................... 20
makeDiffPanel ....................................... 20
makeOverallPanel...................................... 21
mfaEstimate......................................... 22
MFAParams......................................... 23
mfaSimulate......................................... 23
newParams ......................................... 24
Params............................................ 25
phenoEstimate........................................ 25
PhenoParams ........................................ 26
phenoSimulate ....................................... 26
rbindMatched........................................ 27
scDDEstimate........................................ 28
SCDDParams ........................................ 29
scDDSimulate........................................ 30
setParam........................................... 31
setParams .......................................... 32
setParamsUnchecked .................................... 32
setParamUnchecked..................................... 33
showDFs .......................................... 34
showPP ........................................... 34
showValues ......................................... 34
simpleEstimate ....................................... 35
SimpleParams........................................ 36
simpleSimulate ....................................... 36
sparseDCEstimate...................................... 37
SparseDCParams ...................................... 38
sparseDCSimulate...................................... 39
splatEstBCV ........................................ 40
addFeatureStats 3
splatEstDropout....................................... 40
splatEstimate ........................................ 41
splatEstLib ......................................... 42
splatEstMean ........................................ 42
splatEstOutlier ....................................... 43
SplatParams......................................... 43
splatSimBatchCellMeans.................................. 45
splatSimBatchEffects.................................... 45
splatSimBCVMeans .................................... 46
splatSimCellMeans ..................................... 46
splatSimDE ......................................... 47
splatSimDropout ...................................... 47
splatSimGeneMeans .................................... 48
splatSimLibSizes ...................................... 48
splatSimTrueCounts..................................... 49
splatSimulate ........................................ 49
splatter............................................ 51
summariseDiff ....................................... 52
winsorize .......................................... 52
zinbEstimate ........................................ 53
ZINBParams ........................................ 54
zinbSimulate ........................................ 54
Index 56
addFeatureStats 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 statis-
tics.
4addGeneLengths
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.
ntotal 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.
6BASiCSParams
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 BA-
SiCSParams 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 Adata.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 Adata.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()
8compareSCEs
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
xstarting value.
yend value.
Nnumber of steps in random walk.
nnumber 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 com-
paring 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 com-
paring them to a reference.
Usage
diffSCEs(sces, ref, point.size = 0.1, point.alpha = 0.1, fits = TRUE,
colours = NULL)
Arguments
sces named list of SingleCellExperiment objects to combine and compare.
ref string giving the name of the SingleCellExperiment to use as the reference
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.
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 differ-
ences 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 11
expandParams 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 Number of factors to generate.
sel.prob Probability that a factor will be selected to be different from 1.
neg.prob Probability that a selected factor is less than one.
fac.loc Location parameter for the log-normal distribution.
fac.scale 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
xvalue to apply the function to.
x0 midpoint parameter. Gives the centre of the function.
kshape 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 ABiocParallelParam 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 Adata.frame containing gene parameters with two coloumns:
Mean (mean expression for each gene) and Disp (dispersion for each gene).
16 lun2Simulate
zi.params Adata.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 "Over-
coming 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 sim-
ulation 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 dis-
tribution.
Differential expression parameters [de.nGenes] The number of genes that are differentially ex-
pressed 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 = 0.5))
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 23
MFAParams 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 implementa-
tion 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 simula-
tion 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 environmen-
tal 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 ABiocParallelParam 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 ABiocParallelParam 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 31
setParam 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 list of data.frames to show.
not.default 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 object to show.
pp 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 list of values to show.
not.default logical vector giving which have changed from the default.
simpleEstimate 35
simpleEstimate 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, dif-
ferential 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 39
sparseDCSimulate 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 simula-
tion 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 environmen-
tal 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 free-
dom. 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 43
splatEstOutlier 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 distri-
bution. Can be a vector.
44 SplatParams
[batch.facScale] Scale (sdlog) parameter for the batch effect factor log-normal distribu-
tion. 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 param-
eter if a normal distribution is used.
lib.norm Logical. Whether to use a normal distribution for library sizes instead of a log-
normal.
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 dis-
tribution.
out.facScale Scale (sdlog) parameter for the expression outlier factor log-normal distribu-
tion.
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 dis-
tribution. 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 differ-
entiation 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 47
splatSimDE 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 pa-
rameters.
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
xNumeric vector to winsorize
qPercentile to set from each end
Value
Winsorized numeric vector
zinbEstimate 53
zinbEstimate 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 ABiocParallelParam 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 estima-
tion 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 environmen-
tal 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
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
colData,50
compareSCEs,8,20,21
create_synthetic,23
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
fitdist,35,42,43
getLNormFactors,11,45,47
getParam,12
getParam,Params-method (getParam),12
getParams,12
getPathOrder,13
ggplot,9,10
lambda1_calculator,37
lambda2_calculator,37
listSims,13
logistic,14
lun2Estimate,14,16
Lun2Params,15,15
Lun2Params-class (Lun2Params),15
lun2Simulate,16,16
lunEstimate,17,18
LunParams,17,18,19
LunParams-class (LunParams),18
lunSimulate,18,19
makeCompPanel,20
makeDiffPanel,20
makeOverallPanel,21
mfaEstimate,22,23
MFAParams,22,23,23
MFAParams-class (MFAParams),23
mfaSimulate,23,23
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
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
rbindMatched,27
rowData,4,50
scDD,28
scDDEstimate,28,29
SCDDParams,29,30
SCDDParams-class (SCDDParams),29
scDDSimulate,29,30
SerialParam,15,28,30,53
56
INDEX 57
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
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
