Introduction to Splatter
2019-05-02
Contents
Splatter logo
Welcome to Splatter! Splatter is an R package for the simple simulation of single-cell RNA sequencing data. This vignette gives an overview and introduction to Splatter’s functionality.
1 Installation
Splatter can be installed from Bioconductor:
if (!requireNamespace("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install("splatter")To install the most recent development version from Github use:
BiocManager::install("Oshlack/splatter", dependencies = TRUE,
build_vignettes = TRUE)2 Quickstart
Assuming you already have a matrix of count data similar to that you wish to
simulate there are two simple steps to creating a simulated data set with
Splatter. Here is an example using the example dataset in the scater package:
# Load package
library(splatter)
# Load example data
library(scater)
data("sc_example_counts")
# Estimate parameters from example data
params <- splatEstimate(sc_example_counts)
# Simulate data using estimated parameters
sim <- splatSimulate(params)## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Done!These steps will be explained in detail in the following sections but briefly the first step takes a dataset and estimates simulation parameters from it and the second step takes those parameters and simulates a new dataset.
3 The Splat simulation
Before we look at how we estimate parameters let’s first look at how Splatter simulates data and what those parameters are. We use the term ‘Splat’ to refer to the Splatter’s own simulation and differentiate it from the package itself. The core of the Splat model is a gamma-Poisson distribution used to generate a gene by cell matrix of counts. Mean expression levels for each gene are simulated from a gamma distribution and the Biological Coefficient of Variation is used to enforce a mean-variance trend before counts are simulated from a Poisson distribution. Splat also allows you to simulate expression outlier genes (genes with mean expression outside the gamma distribution) and dropout (random knock out of counts based on mean expression). Each cell is given an expected library size (simulated from a log-normal distribution) that makes it easier to match to a given dataset.
Splat can also simulate differential expression between groups of different types of cells or differentiation paths between different cells types where expression changes in a continuous way. These are described further in the simulating counts section.
4 The SplatParams object
All the parameters for the Splat simulation are stored in a SplatParams
object. Let’s create a new one and see what it looks like.
params <- newSplatParams()
params## A Params object of class SplatParams
## Parameters can be (estimable) or [not estimable], 'Default' or 'NOT DEFAULT'
##
## Global:
## (Genes) (Cells) [Seed]
## 10000 100 138572
##
## 28 additional parameters
##
## Batches:
## [Batches] [Batch Cells] [Location] [Scale]
## 1 100 0.1 0.1
##
## Mean:
## (Rate) (Shape)
## 0.3 0.6
##
## Library size:
## (Location) (Scale) (Norm)
## 11 0.2 FALSE
##
## Exprs outliers:
## (Probability) (Location) (Scale)
## 0.05 4 0.5
##
## Groups:
## [Groups] [Group Probs]
## 1 1
##
## Diff expr:
## [Probability] [Down Prob] [Location] [Scale]
## 0.1 0.5 0.1 0.4
##
## BCV:
## (Common Disp) (DoF)
## 0.1 60
##
## Dropout:
## [Type] (Midpoint) (Shape)
## none 0 -1
##
## Paths:
## [From] [Steps] [Skew] [Non-linear]
## 0 100 0.5 0.1
## [Sigma Factor]
## 0.8As well as telling us what type of object we have (“A Params object of class
SplatParams”) and showing us the values of the parameter this output gives us
some extra information. We can see which parameters can be estimated by the
splatEstimate function (those in parentheses), which can’t be estimated
(those in brackets) and which have been changed from their default values (those
in ALL CAPS). For more details about the parameters of the Splat simulation
refer to the Splat parameters vignette.
4.1 Getting and setting
If we want to look at a particular parameter, for example the number of genes to
simulate, we can extract it using the getParam function:
getParam(params, "nGenes")## [1] 10000Alternatively, to give a parameter a new value we can use the setParam
function:
params <- setParam(params, "nGenes", 5000)
getParam(params, "nGenes")## [1] 5000If we want to extract multiple parameters (as a list) or set multiple parameters
we can use the getParams or setParams functions:
# Set multiple parameters at once (using a list)
params <- setParams(params, update = list(nGenes = 8000, mean.rate = 0.5))
# Extract multiple parameters as a list
getParams(params, c("nGenes", "mean.rate", "mean.shape"))## $nGenes
## [1] 8000
##
## $mean.rate
## [1] 0.5
##
## $mean.shape
## [1] 0.6# Set multiple parameters at once (using additional arguments)
params <- setParams(params, mean.shape = 0.5, de.prob = 0.2)
params## A Params object of class SplatParams
## Parameters can be (estimable) or [not estimable], 'Default' or 'NOT DEFAULT'
##
## Global:
## (GENES) (Cells) [Seed]
## 8000 100 138572
##
## 28 additional parameters
##
## Batches:
## [Batches] [Batch Cells] [Location] [Scale]
## 1 100 0.1 0.1
##
## Mean:
## (RATE) (SHAPE)
## 0.5 0.5
##
## Library size:
## (Location) (Scale) (Norm)
## 11 0.2 FALSE
##
## Exprs outliers:
## (Probability) (Location) (Scale)
## 0.05 4 0.5
##
## Groups:
## [Groups] [Group Probs]
## 1 1
##
## Diff expr:
## [PROBABILITY] [Down Prob] [Location] [Scale]
## 0.2 0.5 0.1 0.4
##
## BCV:
## (Common Disp) (DoF)
## 0.1 60
##
## Dropout:
## [Type] (Midpoint) (Shape)
## none 0 -1
##
## Paths:
## [From] [Steps] [Skew] [Non-linear]
## 0 100 0.5 0.1
## [Sigma Factor]
## 0.8The parameters with have changed are now shown in ALL CAPS to indicate that they been changed form the default.
We can also set parameters directly when we call newSplatParams:
params <- newSplatParams(lib.loc = 12, lib.scale = 0.6)
getParams(params, c("lib.loc", "lib.scale"))## $lib.loc
## [1] 12
##
## $lib.scale
## [1] 0.65 Estimating parameters
Splat allows you to estimate many of it’s parameters from a data set containing
counts using the splatEstimate function.
# Check that sc_example counts is an integer matrix
class(sc_example_counts)## [1] "matrix"typeof(sc_example_counts)## [1] "integer"# Check the dimensions, each row is a gene, each column is a cell
dim(sc_example_counts)## [1] 2000 40# Show the first few entries
sc_example_counts[1:5, 1:5]## Cell_001 Cell_002 Cell_003 Cell_004 Cell_005
## Gene_0001 0 123 2 0 0
## Gene_0002 575 65 3 1561 2311
## Gene_0003 0 0 0 0 1213
## Gene_0004 0 1 0 0 0
## Gene_0005 0 0 11 0 0params <- splatEstimate(sc_example_counts)Here we estimated parameters from a counts matrix but splatEstimate can also
take a SingleCellExperiment object. The estimation process has the following
steps:
- Mean parameters are estimated by fitting a gamma distribution to the mean expression levels.
- Library size parameters are estimated by fitting a log-normal distribution to the library sizes.
- Expression outlier parameters are estimated by determining the number of outliers and fitting a log-normal distribution to their difference from the median.
- BCV parameters are estimated using the
estimateDispfunction from theedgeRpackage. - Dropout parameters are estimated by checking if dropout is present and fitting a logistic function to the relationship between mean expression and proportion of zeros.
For more details of the estimation procedures see ?splatEstimate.
6 Simulating counts
Once we have a set of parameters we are happy with we can use splatSimulate
to simulate counts. If we want to make small adjustments to the parameters we
can provide them as additional arguments, alternatively if we don’t supply any
parameters the defaults will be used:
sim <- splatSimulate(params, nGenes = 1000)## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Done!sim## class: SingleCellExperiment
## dim: 1000 40
## metadata(1): Params
## assays(6): BatchCellMeans BaseCellMeans ... TrueCounts counts
## rownames(1000): Gene1 Gene2 ... Gene999 Gene1000
## rowData names(4): Gene BaseGeneMean OutlierFactor GeneMean
## colnames(40): Cell1 Cell2 ... Cell39 Cell40
## colData names(3): Cell Batch ExpLibSize
## reducedDimNames(0):
## spikeNames(0):Looking at the output of splatSimulate we can see that sim is
SingleCellExperiment object with 1000 features (genes) and
40 samples (cells). The main part of this object is a features
by samples matrix containing the simulated counts (accessed using counts),
although it can also hold other expression measures such as FPKM or TPM.
Additionally a SingleCellExperiment contains phenotype information about
each cell (accessed using colData) and feature information about each gene
(accessed using rowData). Splatter uses these slots, as well as assays, to
store information about the intermediate values of the simulation.
# Access the counts
counts(sim)[1:5, 1:5]## Cell1 Cell2 Cell3 Cell4 Cell5
## Gene1 11 0 0 0 4814
## Gene2 0 0 0 0 0
## Gene3 1848 210 6 1 1096
## Gene4 0 0 0 0 0
## Gene5 0 0 27 29 80# Information about genes
head(rowData(sim))## DataFrame with 6 rows and 4 columns
## Gene BaseGeneMean OutlierFactor GeneMean
## <factor> <numeric> <numeric> <numeric>
## Gene1 Gene1 162.27961350832 1 162.27961350832
## Gene2 Gene2 0.00394586265413722 1 0.00394586265413722
## Gene3 Gene3 27.0488594070323 1 27.0488594070323
## Gene4 Gene4 0.00893408360914568 1 0.00893408360914568
## Gene5 Gene5 43.218226825995 1 43.218226825995
## Gene6 Gene6 0.436957397662496 1 0.436957397662496# Information about cells
head(colData(sim))## DataFrame with 6 rows and 3 columns
## Cell Batch ExpLibSize
## <factor> <character> <numeric>
## Cell1 Cell1 Batch1 633156.33789341
## Cell2 Cell2 Batch1 543355.825769202
## Cell3 Cell3 Batch1 361636.014794991
## Cell4 Cell4 Batch1 273146.845309571
## Cell5 Cell5 Batch1 762131.522140643
## Cell6 Cell6 Batch1 1111265.22540117# Gene by cell matrices
names(assays(sim))## [1] "BatchCellMeans" "BaseCellMeans" "BCV" "CellMeans"
## [5] "TrueCounts" "counts"# Example of cell means matrix
assays(sim)$CellMeans[1:5, 1:5]## Cell1 Cell2 Cell3 Cell4 Cell5
## Gene1 1.656039e+01 9.782325e-05 7.783061e-19 1.550924e-15 4.785735e+03
## Gene2 4.884689e-23 1.469951e-58 3.569772e-10 1.902942e-58 6.634616e-32
## Gene3 1.850411e+03 2.161880e+02 6.841197e+00 1.690751e+00 1.095851e+03
## Gene4 2.167214e-43 1.058928e-27 3.464428e-109 1.379729e-56 1.953030e-03
## Gene5 5.842502e-03 1.104395e-03 2.863923e+01 3.535881e+01 7.466149e+01An additional (big) advantage of outputting a SingleCellExperiment is that we
get immediate access to other analysis packages, such as the plotting functions
in scater. For example we can make a PCA plot:
# Use scater to calculate logcounts
sim <- normalize(sim)## Warning in .local(object, ...): using library sizes as size factors# Plot PCA
plotPCA(sim)
(NOTE: Your values and plots may look different as the simulation is random and produces different results each time it is run.)
For more details about the SingleCellExperiment object refer to the [vignette]
SCE-vignette. For information about what you can do with scater refer to the
scater documentation and vignette.
The splatSimulate function outputs the following additional information about
the simulation:
- Cell information (
colData)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.
- Gene information (
rowData)Gene- Unique gene identifier.BaseGeneMean- The base expression level for that gene.OutlierFactor- Expression outlier factor for that gene (1 is not an outlier).GeneMean- Expression level after applying outlier factors.DEFac[Group]- The differential expression factor for each gene in a particular group (1 is not differentially expressed).GeneMean[Group]- Expression level of a gene in a particular group after applying differential expression factors.
- Gene by cell information (
assays)BaseCellMeans- The expression of genes in each cell adjusted for expected library size.BCV- The Biological Coefficient of Variation for each gene in each cell.CellMeans- The expression level of genes in each cell adjusted for BCV.TrueCounts- The simulated counts before dropout.Dropout- Logical matrix showing which counts have been dropped in which cells.
Values that have been added by Splatter are named using UpperCamelCase to
separate them from the underscore_naming used by scater and other packages.
For more information on the simulation see ?splatSimulate.
6.1 Simulating groups
So far we have only simulated a single population of cells but often we are
interested in investigating a mixed population of cells and looking to see what
cell types are present or what differences there are between them. Splatter is
able to simulate these situations by changing the method argument Here we are
going to simulate two groups, by specifying the group.prob parameter and
setting the method parameter to "groups":
(NOTE: We have also set the verbose argument to FALSE to stop Splatter
printing progress messages.)
sim.groups <- splatSimulate(group.prob = c(0.5, 0.5), method = "groups",
verbose = FALSE)
sim.groups <- normalize(sim.groups)## Warning in .local(object, ...): using library sizes as size factorsplotPCA(sim.groups, colour_by = "Group")