## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE)

## ---- eval = FALSE------------------------------------------------------------
#  if (!("devtools" %in% rownames(installed.packages())))
#      install.packages("devtools")
#  
#  library(devtools)
#  devtools::install_github("SydneyBioX/scReClassify")

## ---- eval = FALSE------------------------------------------------------------
#  if (!requireNamespace("BiocManager", quietly = TRUE))
#      install.packages("BiocManager")
#  BiocManager::install("scReClassify")

## -----------------------------------------------------------------------------
suppressPackageStartupMessages({
    library(scReClassify)
    library(DT)
    library(mclust)
    library(dplyr)
    library(SummarizedExperiment)
    library(SingleCellExperiment)
})

data("gse87795_subset_sce")

dat <- gse87795_subset_sce
cellTypes <- gse87795_subset_sce$cellTypes

## -----------------------------------------------------------------------------
# Cell types
table(cellTypes)

# We set the number of clusters
nCs <- length(table(cellTypes))
nCs

# This demo dataset is already pre-processed
dim(dat)

## -----------------------------------------------------------------------------
reducedDim(dat, "matPCs") <- matPCs(dat, assay = "logNorm", 0.7)

## -----------------------------------------------------------------------------
lab <- cellTypes

set.seed(1)
# Function to create noise in the cell type label
noisyCls <- function(dat, rho, cls.truth){
    cls.noisy <- cls.truth
    names(cls.noisy) <- colnames(dat)
    
    for(i in seq_len(length(table(cls.noisy)))) {
        # class label starts from 0
        if (i != length(table(cls.noisy))) {
            cls.noisy[sample(which(cls.truth == names(table(cls.noisy))[i]), 
                            floor(sum(cls.truth == names(table(cls.noisy))[i])*
                            rho))] <- names(table(cls.noisy))[i+1]
        } else {
            cls.noisy[sample(which(cls.truth == names(table(cls.noisy))[i]), 
                            floor(sum(cls.truth == names(table(cls.noisy))[i])*
                            rho))] <- names(table(cls.noisy))[1]
        }
    }
  
    print(sum(cls.truth != cls.noisy))
    return(cls.noisy)
}

cls.noisy01 <- noisyCls(t(reducedDim(dat, "matPCs")), rho=0.1, lab)
cls.noisy02 <- noisyCls(t(reducedDim(dat, "matPCs")), rho=0.2, lab)
cls.noisy03 <- noisyCls(t(reducedDim(dat, "matPCs")), rho=0.3, lab)
cls.noisy04 <- noisyCls(t(reducedDim(dat, "matPCs")), rho=0.4, lab)
cls.noisy05 <- noisyCls(t(reducedDim(dat, "matPCs")), rho=0.5, lab)

## -----------------------------------------------------------------------------
###################################
# SVM
###################################
base <- "svm"
set.seed(1)
result = lapply(seq_len(10), function(j) {
    final <- multiAdaSampling(dat, cls.noisy01, reducedDimName = "matPCs", 
                            classifier=base, percent=1, L=10)$final
    ari01 <- mclust::adjustedRandIndex(lab, final)
    acc01 <- bAccuracy(lab, final)
    
    final <- multiAdaSampling(dat, cls.noisy02, reducedDimName = "matPCs", 
                            classifier=base,  percent=1, L=10)$final
    ari02 <- mclust::adjustedRandIndex(lab, final)
    acc02 <- bAccuracy(lab, final)
    
    final <- multiAdaSampling(dat, cls.noisy03, reducedDimName = "matPCs", 
                            classifier=base, percent=1, L=10)$final
    ari03 <- mclust::adjustedRandIndex(lab, final)
    acc03 <- bAccuracy(lab, final)
    
    final <- multiAdaSampling(dat, cls.noisy04, reducedDimName = "matPCs", 
                            classifier=base, percent=1, L=10)$final
    ari04 <- mclust::adjustedRandIndex(lab, final)
    acc04 <- bAccuracy(lab, final)
    
    final <- multiAdaSampling(dat, cls.noisy05, reducedDimName = "matPCs", 
                            classifier=base, percent=1, L=10)$final
    ari05 <- mclust::adjustedRandIndex(lab, final)
    acc05 <- bAccuracy(lab, final)
    
    c(
      acc01 = acc01,
      acc02 = acc02,
      acc03 = acc03,
      acc04 = acc04,
      acc05 = acc05,
      ari01 = ari01,
      ari02 = ari02,
      ari03 = ari03,
      ari04 = ari04,
      ari05 = ari05
    )
})

result = do.call(rbind, result)
acc = result[,seq_len(5)]
colnames(acc) = seq(from=0.1,to=0.5,by=0.1)

ari = result[,seq(from= 6, to = 10)]
colnames(ari) = seq(from=0.1,to=0.5,by=0.1)


## -----------------------------------------------------------------------------
plot.new()
par(mfrow = c(1,2))
boxplot(acc, col="lightblue", main="SVM Accuracy", 
        ylim=c(0.45, 1), xlab = "rho", ylab = "Accuracy")
points(x=seq_len(5), y=c(
    bAccuracy(lab, cls.noisy01), 
    bAccuracy(lab, cls.noisy02),
    bAccuracy(lab, cls.noisy03), 
    bAccuracy(lab, cls.noisy04),
    bAccuracy(lab, cls.noisy05)), 
    col="red3", pch=c(2,3,4,5,6), cex=1)
boxplot(ari, col="lightblue", main="SVM ARI", 
        ylim=c(0.25, 1), xlab = "rho", ylab = "ARI")
points(x=seq_len(5), y=c(
    mclust::adjustedRandIndex(lab, cls.noisy01), 
    mclust::adjustedRandIndex(lab, cls.noisy02),
    mclust::adjustedRandIndex(lab, cls.noisy03), 
    mclust::adjustedRandIndex(lab, cls.noisy04),
    mclust::adjustedRandIndex(lab, cls.noisy05)), 
    col="red3", pch=c(2,3,4,5,6), cex=1)

## -----------------------------------------------------------------------------
# PCA procedure
reducedDim(dat, "matPCs") <- matPCs(dat, assay = "logNorm", 0.7)


# run scReClassify
set.seed(1)
cellTypes.reclassify <- multiAdaSampling(dat, cellTypes, 
                                        reducedDimName = "matPCs", 
                                        classifier = "svm", percent = 1, L = 10)

# Verification by marker genes
End <- c("ITGA2B", "ITGB3")

## -----------------------------------------------------------------------------
# check examples
idx <- which(cellTypes.reclassify$final != cellTypes)

cbind(original=cellTypes[idx], reclassify=cellTypes.reclassify$final[idx]) %>%
    DT::datatable()

## -----------------------------------------------------------------------------
mat <- assay(dat, "logNorm")

c1 <- mat[, which(cellTypes=="Endothelial Cell")]
c2 <- mat[, which(cellTypes=="Erythrocyte")]
c3 <- mat[, which(cellTypes=="Hepatoblast")]
c4 <- mat[, which(cellTypes=="Macrophage")]
c5 <- mat[, which(cellTypes=="Megakaryocyte")]
c6 <- mat[, which(cellTypes=="Mesenchymal Cell")]
cs <- rainbow(length(table(cellTypes)))


# (example 1 E13.5_C14)
#####
par(mfrow=c(1,2))
marker <- End[1]
boxplot(c1[marker,], c2[marker,], c3[marker,], 
        c4[marker,], c5[marker,], c6[marker,], 
        col=cs, main=marker, 
        names=c("Others", "Others", "Others", "Orignal", 
                "Reclassified", "Others"), las=2, xlab = "Labels",
        ylab = "log2FPKM")
points(5, mat[marker, which(colnames(mat) %in% "E13.5_C14")], 
        pch=16, col="red", cex=2)

marker <- End[2]
boxplot(c1[marker,], c2[marker,], c3[marker,], 
        c4[marker,], c5[marker,], c6[marker,], 
        col=cs, main=marker, 
        names=c("Others", "Others", "Others", "Orignal", 
                "Reclassified", "Others"), las=2, xlab = "Labels", 
        ylab = "log2FPKM")
points(5, mat[marker, which(colnames(mat) %in% "E13.5_C14")], 
        pch=16, col="red", cex=2)

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

