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## load library
library(ggplot2)
library(Rtsne)
library(gridExtra)
library(ggrepel)

## set seed for reproducibility
set.seed(0)

## set 1x1 plot space
par(mfrow=c(1,1))

############################
# Quality Control 
############################

## read in data
data <- read.csv('codex_spleen_subset.csv', row.names = 1)
gexp <- data[,4:ncol(data)]
genes <- colnames(gexp)
pos <- data[,1:2]

# normalize
totgexp <- rowSums(gexp)
mat <- gexp/rowSums(gexp) * median(rowSums(gexp))
mat <- log10(mat + 1)


############################
# Kmeans clustering 
############################

# find optimal number of k

library(segmented)
ks <- 1:28
# loop through ks to return wss and bss as matrix
out <- do.call(rbind, lapply(ks, function(k) {
  com <- kmeans(mat, centers = k)
  c(within = com$tot.withinss, between = com$betweenss)
}))


fit <- segmented(lm(out[,1] ~ ks))

breakpoint <- fit$psi[1]

plot(ks, out[,1], type='b', main='WSS')
plot(ks,out[,2],type='b',main='BSS')
abline(fit, col='red')

abline(v=breakpoint, col='blue')


# optimal number of k=10
num_center = 8
com <- kmeans(mat, centers=num_center)



############################
# Dimensionality Reduction 
############################

## PCA analysis
pcs <- prcomp(mat)

## tSNE analysis
emb <- Rtsne(mat)

# plot tSNE analysis results
df <- data.frame(pos, emb$Y, pcs$x, celltype = as.factor(com$cluster))
head(df)
p1 <- ggplot(df, aes(x = X1, y = X2, col=celltype)) +
  geom_point(size = 0.01) +
  scale_color_manual(values=c("#1b9e77", "#d95f02", "#7570b3", "#e7298a", "#66a61e", "#e6ab02", "#a6761d", "#666666", "#1f78b4", "#b2df8a", "#33a02c", "#fb9a99", "#e31a1c", "#fdbf6f", "#ff7f00")) +
  theme_classic()
p2 <- ggplot(df, aes(x = PC1, y = PC2, col=celltype)) +
  geom_point(size = 0.01) +
  scale_color_manual(values=c("#1b9e77", "#d95f02", "#7570b3", "#e7298a", "#66a61e", "#e6ab02", "#a6761d", "#666666", "#1f78b4", "#b2df8a", "#33a02c", "#fb9a99", "#e31a1c", "#fdbf6f", "#ff7f00")) +
  theme_classic()
grid.arrange(p1,p2)

ggplot(df, aes(x=x, y=y, col=celltype)) +
  geom_point(size=0.01)



############################
# Pick one cluster
############################

## pick a cluster
cluster_number = 6
cluster.of.interest <- names(which(com$cluster == cluster_number))
cluster.other <- names(which(com$cluster != cluster_number))

## loop through my genes and test each one
out <- sapply(genes, function(g) {
  a <- mat[cluster.of.interest, g]
  b <- mat[cluster.other, g]
  pvs <- wilcox.test(a,b,alternative='two.sided')$p.val
  #pvs <- as.data.frame(pvs)
  log2fc <- log2(mean(a)/mean(b))
  #log2fc <- as.data.frame(log2fc)
  c(pvs=pvs,log2fc=log2fc)
})

## volcano plot
# run Bonferroni correction determine a p value cutoff (conservative method with low false-positive rate but high false-negative rate as a trade-off)
cutoff.pvs <- 0.05/ncol(mat) # false-positive rate would be 1-(1-0.05/ncol(mat))^ncol(mat)
cutoff.log2fc <- 1
df_temp <- data.frame(pvs=out[1,], log2fc=out[2,])
## prepare a data frame for plotting
df2 <- df_temp
# add a column of NAs to df2 titled diffexpressed
df2$diffexpressed <- 'NO'
# if log2fc > cutoff.log2fc and pvalue < cutoff.pvs, set as "Up" 
df2$diffexpressed[df2$log2fc > cutoff.log2fc & df2$pvs < cutoff.pvs] <- "Up"
# if log2fc < -cutoff.log2fc and pvalue < cutoff.pvs, set as "Down"
df2$diffexpressed[df2$log2fc < -cutoff.log2fc & df2$pvs < cutoff.pvs] <- "Down"
# add a column of NAs to df2 titled genelabel that will contain names of differentially expressed genes
df2$genelabel <- NA
df2$genelabel[df2$diffexpressed != 'NO'] <- rownames(df2)[df2$diffexpressed != 'NO']

## get the most significant genes (upregulated) from the chosen cluster
significant_genes <- df2$gene[df2$diffexpressed == 'Up']
#sort by p-value
significant_genes_sorted <- significant_genes[order(out[1,][significant_genes])]
#choose first one from list
most_significant_gene <- significant_genes_sorted[1]

volcano_plt <- ggplot(df2, aes(x=log2fc,y=-log10(pvs), col=diffexpressed, label=genelabel)) +
  geom_point() +
  scale_color_manual(values = c('blue', 'black', 'red'),
                     name = 'Expression',
                     breaks = c('Down', 'NO', 'Up'),
                     labels = c('Down', 'N.S.', 'Up')) +
  ggtitle(paste('Cluster ',cluster_number,' vs. Other Clusters')) +
  xlab('log2 fold change') +
  ylab('-log10(p value)') +
  geom_label_repel() +
  geom_vline(xintercept = c(-cutoff.log2fc, cutoff.log2fc), col='black',linetype='dashed') +
  geom_hline(yintercept = -log10(cutoff.pvs), col='black',linetype='dashed') +
  theme_classic()

volcano_plt
significant_genes
most_significant_gene



############################
# Multi-Panel Visualization
############################


#### plot for a fi
gure ####
## set custom theme for plots
plot_theme <- theme_classic() +
  theme(
    text = element_text(size = 25),
    legend.key.size = unit(0.75, 'cm')
  )

# set the green color palette
green_palette <- c("#e5f5e0", "#c7e9c0", "#a1d99b", "#74c476", "#41ab5d", "#238b45", "#006d2c", "#00441b")

# plot certain cluster on tSNE
cluster.of.interest = cluster_number
gene.of.interest = most_significant_gene 
df <- data.frame(pos, pcs$x[,1:2], emb$Y, celltype = as.factor(com$cluster), gene=mat[,gene.of.interest])
head(df)
# PCA plots
p1 <- ggplot(df, aes(x = PC1, y = PC2, col = celltype)) +
  geom_point(size = 0.5) +
  ggtitle('PCA (All clusters)') +
  labs(color = 'Cluster') +
  plot_theme
p2 <- ggplot(df, aes(x = PC1, y = PC2, col = celltype == cluster.of.interest)) +
  geom_point(size = 0.5) +
  scale_color_manual(values = c(green_palette[5], 'black'),
                     name = paste('Cluster',cluster.of.interest),
                     labels = c('FALSE','TRUE')) +
  ggtitle(paste0('PCA (Cluster ',cluster.of.interest,')')) +
  plot_theme
p3 <- ggplot(df, aes(x = PC1, y = PC2, col = gene)) +
  geom_point(size = 0.5) +
  scale_color_gradient(low=green_palette[5], high='black', name = gene.of.interest) +
  ggtitle(paste0('PCA (',gene.of.interest,')')) +
  plot_theme

# tSNE plots
p4 <- ggplot(df, aes(x = X1, y = X2, col = celltype)) +
  geom_point(size = 0.5) +
  ggtitle('tSNE (All clusters)') +
  labs(color = 'Cluster') +
  plot_theme
p5 <- ggplot(df, aes(x = X1, y = X2, col = celltype == cluster.of.interest)) +
  geom_point(size = 0.5) +
  scale_color_manual(values = c(green_palette[5], 'black'),
                     name = paste('Cluster',cluster.of.interest),
                     labels = c('FALSE','TRUE')) +
  ggtitle(paste0('tSNE (Cluster ',cluster.of.interest,')')) +
  plot_theme
p6 <- ggplot(df, aes(x = X1, y = X2, col = gene)) +
  geom_point(size = 0.5) +
  scale_color_gradient(low=green_palette[5], high='black', name = gene.of.interest) +
  ggtitle(paste0('tSNE (',gene.of.interest,')')) +
  plot_theme


lay <- rbind(c(1,2,3),
             c(4,5,6))

plot_theme <- plot_theme + theme(plot.width = unit(5, "cm"), plot.height = unit(5, "cm"))

result <- grid.arrange(p1, p2, p3, p4, p5, p6, layout_matrix = lay)

## save panel plots
ggsave("hw_moneera_panels.png", plot = result, width = 40, height = 15, dpi = 300)