## ----echo = FALSE, results = "hide", message = FALSE--------------------------
require(knitr)
opts_chunk$set(error = FALSE, message = FALSE, warning = FALSE, dpi = 30)
knitr::opts_chunk$set(dev = "png")

## ----library, echo = FALSE----------------------------------------------------
library("BASiCS")
library("BiocStyle")
library("SingleCellExperiment")
library("cowplot")
library("ggplot2")
theme_set(theme_bw())

## ----quick-start-MCMC---------------------------------------------------------
set.seed(1)
Data <- makeExampleBASiCS_Data()
Chain <- BASiCS_MCMC(
  Data = Data,
  N = 1000, Thin = 10, Burn = 500,
  PrintProgress = FALSE, Regression = TRUE
)

## ----ExampleDataTest----------------------------------------------------------
set.seed(1)
Counts <- matrix(rpois(50 * 40, 2), ncol = 40)
rownames(Counts) <- c(paste0("Gene", 1:40), paste0("Spike", 1:10))
colnames(Counts) <- paste0("Cell", 1:40)

Tech <- c(rep(FALSE, 40), rep(TRUE, 10))
set.seed(2)
SpikeInput <- rgamma(10, 1, 1)
SpikeInfo <- data.frame(
  "SpikeID" = paste0("Spike", 1:10),
  "SpikeInput" = SpikeInput
)

# No batch structure
DataExample <- newBASiCS_Data(Counts, Tech, SpikeInfo)

# With batch structure
DataExample <- newBASiCS_Data(
  Counts, Tech, SpikeInfo,
  BatchInfo = rep(c(1, 2), each = 20)
)

## ----ExampleDataNoSpikes------------------------------------------------------
set.seed(1)
CountsNoSpikes <- matrix(rpois(50 * 40, 2), ncol = 40)
rownames(CountsNoSpikes) <- paste0("Gene", 1:50)
colnames(CountsNoSpikes) <- paste0("Cell", 1:40)

# With batch structure
DataExampleNoSpikes <- SingleCellExperiment(
  assays = list(counts = CountsNoSpikes),
  colData = data.frame(BatchInfo = rep(c(1, 2), each = 20))
)

## ----LoadSingleData-----------------------------------------------------------
data(ChainSC)

## ----quick-start-HVGdetection, fig.height = 8, fig.width = 20-----------------
HVG <- BASiCS_DetectHVG(ChainSC, VarThreshold = 0.6)
LVG <- BASiCS_DetectLVG(ChainSC, VarThreshold = 0.2)

plot_grid(
  BASiCS_PlotVG(HVG, "Grid"),
  BASiCS_PlotVG(HVG, "VG")
)
plot_grid(
  BASiCS_PlotVG(LVG, "Grid"),
  BASiCS_PlotVG(LVG, "VG")
)

## ----quick-start-HVGdetectionTable--------------------------------------------
as.data.frame(HVG)
as.data.frame(LVG)

## ----quick-start-HVGdetectionPlot, fig.width = 8, fig.height = 8, eval = FALSE----
# SummarySC <- Summary(ChainSC)
# plot(SummarySC, Param = "mu", Param2 = "delta", log = "xy")
# HTable <- as.data.frame(HVG)
# LTable <- as.data.frame(LVG)
# with(HTable, points(Mu, Delta))
# with(LTable, points(Mu, Delta))

## ----quick-start-LoadBothData-------------------------------------------------
data(ChainSC)
data(ChainRNA)

## ----quick-start-TestDE, fig.width = 16, fig.height = 8-----------------------
Test <- BASiCS_TestDE(
  Chain1 = ChainSC, Chain2 = ChainRNA,
  GroupLabel1 = "SC", GroupLabel2 = "PaS",
  EpsilonM = log2(1.5), EpsilonD = log2(1.5),
  EFDR_M = 0.10, EFDR_D = 0.10,
  Offset = TRUE, PlotOffset = TRUE, Plot = TRUE
)

## ----testing, message = TRUE--------------------------------------------------
Test

## ----as.data.frame------------------------------------------------------------
head(as.data.frame(Test, Parameter = "Mean"))
head(as.data.frame(Test, Parameter = "Disp"))
## This object doesn't contain residual overdispersion tests as the chains
## were run using the non-regression version of BASiCS
# head(as.data.frame(DE, Parameter = "Disp"))

## ----rowData------------------------------------------------------------------
rowData(Test)
rowData(Test) <- cbind(rowData(Test), Index = 1:nrow(rowData(Test)))
as.data.frame(Test, Parameter = "Mean")

## ----plots, fig.height=16, fig.width=20---------------------------------------
BASiCS_PlotDE(Test)
BASiCS_PlotDE(Test, Plots = c("MA", "Volcano"))
BASiCS_PlotDE(Test, Plots = "MA", Parameters = "Mean")

## ----quick-start-TestDE-2, fig.width = 16, fig.height = 8---------------------
Test <- BASiCS_TestDE(
  Chain1 = ChainSC, Chain2 = ChainRNA,
  GroupLabel1 = "SC", GroupLabel2 = "PaS",
  EpsilonM = 0, EpsilonD = log2(1.5),
  EFDR_M = 0.10, EFDR_D = 0.10,
  Offset = TRUE, PlotOffset = FALSE, Plot = FALSE
)

## ----fig.height = 8, fig.width = 8--------------------------------------------
DataNoSpikes <- newBASiCS_Data(
  Counts, Tech, SpikeInfo = NULL,
  BatchInfo = rep(c(1, 2), each = 20)
)

# Alternatively
DataNoSpikes <- SingleCellExperiment(
  assays = list(counts = Counts),
  colData = data.frame(BatchInfo = rep(c(1, 2), each = 20))
)

ChainNoSpikes <- BASiCS_MCMC(
  Data = DataNoSpikes, N = 1000,
  Thin = 10, Burn = 500,
  WithSpikes = FALSE,  Regression = TRUE,
  PrintProgress = FALSE
)

## ----fig.height = 8, fig.width = 8--------------------------------------------
DataRegression <- newBASiCS_Data(
  Counts, Tech, SpikeInfo,
  BatchInfo = rep(c(1, 2), each = 20)
)
ChainRegression <- BASiCS_MCMC(
  Data = DataRegression, N = 1000,
  Thin = 10, Burn = 500,
  Regression = TRUE,
  PrintProgress = FALSE
)

## ----fig.height = 12, fig.width = 12------------------------------------------
data("ChainRNAReg")
BASiCS_ShowFit(ChainRNAReg)

## ----fig.height = 8, fig.width = 16-------------------------------------------
data("ChainSCReg")
Test <- BASiCS_TestDE(
  Chain1 = ChainSCReg, Chain2 = ChainRNAReg,
  GroupLabel1 = "SC", GroupLabel2 = "PaS",
  EpsilonM = log2(1.5), EpsilonD = log2(1.5),
  EpsilonR = log2(1.5) / log2(exp(1)),
  EFDR_M = 0.10, EFDR_D = 0.10,
  Offset = TRUE, PlotOffset = FALSE, Plot = FALSE
)

## ----testde-resdisp, fig.width=24, fig.height=16------------------------------
head(as.data.frame(Test, Parameter = "ResDisp"))
BASiCS_PlotDE(Test, Parameters = "ResDisp")

## ----MCMCNotRun---------------------------------------------------------------
Data <- makeExampleBASiCS_Data()
Chain <- BASiCS_MCMC(
  Data,
  N = 1000, Thin = 10, Burn = 500,
  Regression = TRUE, PrintProgress = FALSE,
  StoreChains = TRUE, StoreDir = tempdir(), RunName = "Example"
)

## ----LoadChainNotRun----------------------------------------------------------
Chain <- BASiCS_LoadChain("Example", StoreDir = tempdir())

## ----Traceplots, fig.height = 8, fig.width = 16-------------------------------
plot(Chain, Param = "mu", Gene = 1, log = "y")

## ----AccessChains-------------------------------------------------------------
displayChainBASiCS(Chain, Param = "mu")[1:2, 1:2]

## ----Summary------------------------------------------------------------------
ChainSummary <- Summary(Chain)

## ----SummaryExample-----------------------------------------------------------
head(displaySummaryBASiCS(ChainSummary, Param = "mu"), n = 2)

## ----OtherHPD, fig.width = 16, fig.height = 8---------------------------------
par(mfrow = c(1, 2))
plot(ChainSummary, Param = "mu", main = "All genes", log = "y")
plot(ChainSummary, Param = "delta",
     Genes = c(2, 5, 10, 50), main = "5 customized genes"
)

## ----Normalisation, fig.width = 16, fig.height = 8----------------------------
par(mfrow = c(1, 2))
plot(ChainSummary, Param = "phi")
plot(ChainSummary, Param = "s", Cells = 1:5)

## ----DispVsExp, fig.width = 16, fig.height = 8--------------------------------
par(mfrow = c(1, 2))
plot(ChainSummary,
  Param = "mu", Param2 = "delta",
  log = "x", SmoothPlot = FALSE
)
plot(ChainSummary,
  Param = "mu", Param2 = "delta",
  log = "x", SmoothPlot = TRUE
)

## ----DenoisedCounts-----------------------------------------------------------
DenoisedCounts <- BASiCS_DenoisedCounts(Data = Data, Chain = Chain)
DenoisedCounts[1:2, 1:2]

## ----DenoisedProp-------------------------------------------------------------
DenoisedRates <- BASiCS_DenoisedRates(
  Data = Data, Chain = Chain,
  Propensities = FALSE
)
DenoisedRates[1:2, 1:2]

## ----DenoisedRates------------------------------------------------------------
DenoisedProp <- BASiCS_DenoisedRates(
  Data = Data, Chain = Chain,
  Propensities = TRUE
)
DenoisedProp[1:2, 1:2]

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

