## ----knitr-options, echo = FALSE, message = FALSE, warning = FALSE---------
# To render an HTML version that works nicely with github and web pages, do:
# rmarkdown::render("vignettes/splatter.Rmd", "all")
knitr::opts_chunk$set(fig.align = 'center', fig.width = 6, fig.height = 5,
                      dev = 'png')

## ----install, eval = FALSE-------------------------------------------------
#  source("https://bioconductor.org/biocLite.R")
#  biocLite("splatter")

## ----install-github, eval = FALSE------------------------------------------
#  biocLite("Oshlack/splatter", dependencies = TRUE,
#           build_vignettes = TRUE)

## ----quickstart------------------------------------------------------------
# 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)

## ----SplatParams-----------------------------------------------------------
params <- newSplatParams()
params

## ----getParam--------------------------------------------------------------
getParam(params, "nGenes")

## ----setParam--------------------------------------------------------------
params <- setParam(params, "nGenes", 5000)
getParam(params, "nGenes")

## ----getParams-setParams---------------------------------------------------
# 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"))
# Set multiple parameters at once (using additional arguments)
params <- setParams(params, mean.shape = 0.5, de.prob = 0.2)
params

## ----newSplatParams-set----------------------------------------------------
params <- newSplatParams(lib.loc = 12, lib.scale = 0.6)
getParams(params, c("lib.loc", "lib.scale"))

## ----splatEstimate---------------------------------------------------------
# Check that sc_example counts is an integer matrix
class(sc_example_counts)
typeof(sc_example_counts)
# Check the dimensions, each row is a gene, each column is a cell
dim(sc_example_counts)
# Show the first few entries
sc_example_counts[1:5, 1:5]

params <- splatEstimate(sc_example_counts)

## ----splatSimulate---------------------------------------------------------
sim <- splatSimulate(params, nGenes = 1000)
sim

## ----SCE-------------------------------------------------------------------
# Access the counts
counts(sim)[1:5, 1:5]
# Information about genes
head(rowData(sim))
# Information about cells
head(colData(sim))
# Gene by cell matrices
names(assays(sim))
# Example of cell means matrix
assays(sim)$CellMeans[1:5, 1:5]

## ----pca-------------------------------------------------------------------
# Use scater to calculate logcounts
sim <- normalise(sim)
# Plot PCA
plotPCA(sim)

## ----groups----------------------------------------------------------------
sim.groups <- splatSimulate(group.prob = c(0.5, 0.5), method = "groups",
                            verbose = FALSE)
sim.groups <- normalise(sim.groups)
plotPCA(sim.groups, colour_by = "Group")

## ----paths-----------------------------------------------------------------
sim.paths <- splatSimulate(method = "paths", verbose = FALSE)
sim.paths <- normalise(sim.paths)
plotPCA(sim.paths, colour_by = "Step")

## ----batches---------------------------------------------------------------
sim.batches <- splatSimulate(batchCells = c(50, 50), verbose = FALSE)
sim.batches <- normalise(sim.batches)
plotPCA(sim.batches, colour_by = "Batch")

## ----batch-groups----------------------------------------------------------
sim.groups <- splatSimulate(batchCells = c(50, 50), group.prob = c(0.5, 0.5),
                            method = "groups", verbose = FALSE)
sim.groups <- normalise(sim.groups)
plotPCA(sim.groups, shape_by = "Batch", colour_by = "Group")

## ----listSims--------------------------------------------------------------
listSims()

## ----listSims-table--------------------------------------------------------
knitr::kable(listSims(print = FALSE))

## ----lengths---------------------------------------------------------------
sim <- simpleSimulate(verbose = FALSE)
sim <- addGeneLengths(sim)
head(rowData(sim))

## ----TPM-------------------------------------------------------------------
tpm(sim) <- calculateTPM(sim, rowData(sim)$Length)
tpm(sim)[1:5, 1:5]

## ----comparison------------------------------------------------------------
sim1 <- splatSimulate(nGenes = 1000, batchCells = 20, verbose = FALSE)
sim2 <- simpleSimulate(nGenes = 1000, nCells = 20, verbose = FALSE)
comparison <- compareSCEs(list(Splat = sim1, Simple = sim2))

names(comparison)
names(comparison$Plots)

## ----comparison-means------------------------------------------------------
comparison$Plots$Means

## ----comparison-libsize-features-------------------------------------------
library("ggplot2")
ggplot(comparison$PhenoData,
       aes(x = total_counts, y = total_features, colour = Dataset)) +
    geom_point()

## ----difference------------------------------------------------------------
difference <- diffSCEs(list(Splat = sim1, Simple = sim2), ref = "Simple")
difference$Plots$Means

## ----difference-qq---------------------------------------------------------
difference$QQPlots$Means

## ----save-panels, eval = FALSE---------------------------------------------
#  # This code is just an example and is not run
#  panel <- makeCompPanel(comparison)
#  cowplot::save_plot("comp_panel.png", panel, nrow = 4, ncol = 3)
#  
#  panel <- makeDiffPanel(difference)
#  cowplot::save_plot("diff_panel.png", panel, nrow = 3, ncol = 5)
#  
#  panel <- makeOverallPanel(comparison, difference)
#  cowplot::save_plot("overall_panel.png", panel, ncol = 4, nrow = 7)

## ----citation--------------------------------------------------------------
citation("splatter")

## ----sessionInfo-----------------------------------------------------------
sessionInfo()