# Principle Component Analysis
# Normalized with limma::voom

source("__ - Preloader.R", verbose=T)


# PCA variables
do.center = TRUE
do.scale = FALSE


# The analysis
master.Table %>% readr::write_csv(
	file.path(results.dir, "patient.table.csv")
)

norm.expr.data <- expression.data %>%
	tibble::column_to_rownames("Gene")
norm.expr.data <- norm.expr.data[rowSums(norm.expr.data) >= 10,] %>%
	limma::voom() %>%
	as.matrix()

# Principle Component analysis
results.dir.pca <- file.path(results.dir, "principle.components")
dir.create(results.dir.pca, recursive=TRUE)

norm.expr.data.pcs <- norm.expr.data %>%
  t() %>%
  stats::prcomp(
  	center = do.center,
  	scale. = do.scale
  )

# Write summary of PCAs to files
pcs.summery <- summary(norm.expr.data.pcs)
pcs.summery$importance %>%
	t() %>%
	as.data.frame() %>%
	tibble::rownames_to_column("PC.name") %>%
	readr::write_csv(
		file.path(results.dir.pca, "importance.csv")
	)

pcs.summery$x %>%
	t() %>%
	as.data.frame() %>%
	tibble::rownames_to_column("ensembl.id") %>%
	readr::write_csv(
		file.path(results.dir.pca, "values.csv")
	)

pcs.summery$rotation %>%
	t() %>%
	as.data.frame() %>%
	tibble::rownames_to_column("sample.id") %>%
	readr::write_csv(
		file.path(results.dir.pca, "rotation.csv")
	)

data.frame(
		rownames = names(pcs.summery$center),
		center = pcs.summery$center,
		scale = pcs.summery$scale
	) %>%
	readr::write_csv(
		file.path(results.dir.pca, "rest.csv")
	)

# Not saved: pcs.summery$sdev,



# Plot PCAs
# https://github.com/kevinblighe/PCAtools
results.dir.pca.plot <- file.path(results.dir.pca, "img")
dir.create(results.dir.pca.plot)

metadata <- master.Table %>%
	dplyr::filter(
		!is.na(GenomeScan_ID)
	) %>%
	tibble::column_to_rownames("GenomeScan_ID") %>%
	select.rows.in.order(
		colnames(norm.expr.data)
	)


p <- pca(norm.expr.data,
	metadata = metadata, 
  	center = do.center,
  	scale = do.scale
)
elbow <- findElbowPoint(p$variance)
metavars <- c('Age','Gender','Smoking_status','COPD_Y_or_N','SEO_COPD_Y_or_N','GOLD_stage')


png(filename = file.path(results.dir.pca.plot,"scree_plot.png"),
    width = 800, height = 800,
    units = "px", pointsize = 12,
    type = "Xlib")
print(screeplot(p,
		axisLabSize = 12,
		titleLabSize = 12,
	    components = getComponents(p, 1:(elbow+5)),
	    vline = c(elbow)
    ) +
    geom_label(
    	aes(
    		x = elbow + 1,
    		y = 50,
    		label = 'Elbow method',
    		vjust = -1,
    		size = 8
    	)
    ))
dev.off()


png(filename = file.path(results.dir.pca.plot, "eigen_corr_plot.png"),
    width = 1200, height = 1200,
    units = "px", pointsize = 12,
    type = "Xlib")
print(eigencorplot(p,
    metavars = metavars
))
dev.off()


dir.create(file.path(results.dir.pca.plot, "pairsplot"))
for (var in metavars) {
	png(
		filename = file.path(results.dir.pca.plot, "pairsplot", paste0(var,".png")),
	    width = 1200, height = 1200,
	    units = "px", pointsize = 12,
	    type = "Xlib"
    )
	print(pairsplot(p,
	    components = getComponents(p, c(1:(elbow+1))),
	    triangle = TRUE,
	    trianglelabSize = 12,
	    hline = 0, vline = 0,
	    pointSize = 0.4,
	    gridlines.major = FALSE,
	    gridlines.minor = FALSE,
	    colby = var,
	    title = paste0('Pairs plot: ',var),
	    plotaxes = TRUE
	))
	dev.off()
}



# Plot PCAs - old failure
pca.combinations <- combinations(
		n = (elbow+1),
		r = 2,
		v = 1:(elbow+1),
		repeats.allowed = FALSE
	)

dir.create(file.path(results.dir.pca.plot, "biplots"))
for (var in metavars) {
	for (i in 1:nrow(pca.combinations)) {
		pca.combi <- pca.combinations[i,]
		pca.title <- paste(paste0("PC", pca.combi), collapse="_")
		png(
			filename = file.path(results.dir.pca.plot, "biplots", paste0(var, "-", pca.title, ".png")),
		    width = 800, height = 800,
		    units = "px", pointsize = 12,
		    type = "Xlib"
	    )
		print(
			autoplot(
				norm.expr.data.pcs,
				data = master.Table %>%
					dplyr::filter(
						!is.na(GenomeScan_ID)
					) %>%
					tibble::column_to_rownames("GenomeScan_ID") %>%
					select.rows.in.order(
						rownames(norm.expr.data.pcs$x)
					),
				x = pca.combi[1],
				y = pca.combi[2],
				colour = var,
				loadings = FALSE,
				loadings.label = FALSE,
				#label = FALSE,
				label.size = 3
		    ) +
		    ggprism::theme_prism() + 
		    #ggprism::scale_colour_prism() + 
		    ggprism::scale_shape_prism() +
		    ggplot2::labs(subtitle = paste0(str_to_title(var), " (", pca.title,")"))
		)
		dev.off()
	}
}