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plot_PPI_gene_rank.R
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plot_PPI_gene_rank.R
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source('utils.R')
gtex_col = read.table('../../data/gtex_colors.txt', sep='\t', header = T, stringsAsFactors = F)
gtex_col = gtex_col[order(gtex_col$tissue_id), ]
gtex_col$tissue_color_hex = paste0("#", gtex_col$tissue_color_hex)
get_tissue_above_lung_ace2 <- function(){
ace2 = read.table(paste0(datadir, 'corrected_gene_exp/ACE2.csv'), sep=',', header = T, stringsAsFactors = F)
TMPRSS2 = read.table(paste0(datadir, 'corrected_gene_exp/TMPRSS2.csv'), sep=',', header = T, stringsAsFactors = F)
ace2_med = ace2 %>% group_by(SMTSD) %>% summarize(Median = 10^median(corrected_geneEXP) - 1)
lung_ace2 = ace2_med %>% filter(SMTSD == 'Lung') %>% pull(Median)
qualify_tissues = ace2_med[ace2_med$Median >= lung_ace2, ] %>% pull(SMTSD)
qualify_tissues = sapply(qualify_tissues,
function(x)gsub(" ", "_", gsub('\\)', '', gsub(' \\(', '_', gsub(' - ', '_', x)))))
return(qualify_tissues)
}
PPI_gene_ranks = read.table('results/PPI_results/Rank_median_in_tissues_corrected_for_COVs.csv', sep = ',',
header = T, stringsAsFactors = F)
PPI_gene_ranks = PPI_gene_ranks[order(rownames(PPI_gene_ranks)), ]
PPI_gene_ranks$tissue = gtex_col$tissue_id
tissues_pass_lung = get_tissue_above_lung_ace2()
df_for_plot = reshape2::melt(PPI_gene_ranks)
#df_for_plot = df_for_plot %>% filter(tissue %in% tissues_pass_lung)
medians = df_for_plot %>% group_by(tissue) %>% summarize(Median = median(value))
df_for_plot$tissue = factor(df_for_plot$tissue,
levels = medians$tissue[order(medians$Median)])
rownames(gtex_col) = gtex_col$tissue_id
colors = gtex_col[medians$tissue[order(medians$Median)], "tissue_color_hex"]
g = ggplot(aes(x = tissue, y = value), data = df_for_plot) +
geom_boxplot(aes(fill = tissue)) +
scale_fill_manual(values = colors) +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust=1,vjust=0.2),
legend.position = 'none') +
scale_y_reverse() +
ylab("Rank of expression across tissues") +
xlab("")
png('results/PPI_results/PPI_ranks_corrected_for_cov.png', res = 120, width = 850, height = 800)
print(g)
dev.off()
############### Heatmap
library(ComplexHeatmap)
library(circlize)
library(readr)
assign_colors_to_baits <- function(){
library(colortools)
unique_baits = c(paste0("nsp", seq(1,5)), "nsp5_C145A",paste0("nsp", seq(6,15)),
"E", "M", "N","Spike",
"orf3a", "orf3b", "orf6", "orf7a", "orf7b", "orf8",
"orf9b", "orf9c", "orf10")
unique_baits = paste('SARS-CoV2', unique_baits)
unique_baits = unique_baits[unique_baits%in%PPI_dat$Bait ]
unique_baits_cols = c()
i = 1
for(b in unique_baits[1:15]){
unique_baits_cols[b] = sequential("blue", percentage = 6, plot=F)[i+2]
i = i + 1
}
i = 1
for(b in unique_baits[16:19]){
unique_baits_cols[b] = sequential("red", percentage = 10, plot=F)[5+i]
i = i + 1
}
i = 1
for(b in unique_baits[20:27]){
unique_baits_cols[b] = sequential("green", percentage=10, plot=F)[2+i]
i = i+1
}
return(unique_baits_cols)
}
## read in PPI interactions
datadir_PPI = './PPI_data/'
PPI_dat = read_csv(paste0(datadir_PPI, 'PPI_TableS2.csv'))
unique_baits_cols = assign_colors_to_baits()
## read in gene expression ranks
PPI_gene_ranks = read.table('results/PPI_results/Rank_median_in_tissues_corrected_for_COVs.csv', sep = ',',
header = T, stringsAsFactors = F)
tissues_pass_lung = get_tissue_above_lung_ace2()
PPI_gene_ranks = PPI_gene_ranks[order(rownames(PPI_gene_ranks)), ]
rownames(PPI_gene_ranks) = gtex_col$tissue_id
PPI_gene_ranks = PPI_gene_ranks[tissues_pass_lung,]
## merge the two dataframes
baits_withGene_ranks = intersect(as.character(PPI_dat %>% pull(PreyGene)), colnames(PPI_gene_ranks))
PPI_dat = PPI_dat %>% filter(PreyGene %in% baits_withGene_ranks)
PPI_gene_ranks = PPI_gene_ranks[,as.character(PPI_dat %>% pull(PreyGene))]
PPI_gene_ranks_baits = as.character(PPI_dat %>% pull(Bait))
ht_opt(
legend_title_gp = gpar(fontsize = 8, fontface = "bold"),
legend_labels_gp = gpar(fontsize = 8),
heatmap_column_names_gp = gpar(fontsize = 8),
heatmap_column_title_gp = gpar(fontsize = 10),
heatmap_row_title_gp = gpar(fontsize = 8)
)
bait_annotation = HeatmapAnnotation(bait = PPI_gene_ranks_baits,
col = list(bait = unique_baits_cols),
annotation_name_side = "left",
annotation_legend_param = list(
bait = list(
direction = 'horizontal',
title = 'Bait',
at = names(unique_baits_cols),
labels = names(unique_baits_cols)
)
))
col_fun = colorRamp2(c(1, 49), c('#53bdf8','gray10'))
ht_cluster_genes = Heatmap(as.matrix(PPI_gene_ranks),
show_row_names =T,
show_row_dend = T,
show_column_dend = F,
show_column_names = F,
cluster_rows = T,
cluster_columns = T,
col = col_fun,
row_dend_side = "left",
row_km = 6, ## split by kmeans cluster
row_km_repeats = 100,
cluster_row_slices = F,
bottom_annotation = bait_annotation,
column_title = "SARS-CoV-2 genome components",
column_title_side = "bottom",
heatmap_legend_param = list(
title = "Gene expression Rank",
at = c(1, 49),
labels = c("1", "49"),
direction = "horizontal",
legend_width = unit(10, "cm"))
)
setEPS()
postscript("results/PPI_results/PPI_ranks_corrected_for_cov_heatmap_clusterGenes.eps", height = 5, width = 16)
draw(ht_cluster_genes, heatmap_legend_side = 'bottom')
dev.off()
### annotate drugable genes
druggable_genes = read.table('PPI_data/druggable_genes.txt',
sep='\t', header = T,stringsAsFactors = F)
druggable_genes = druggable_genes$Gene_Name
### interesting gene
druggable_genes = c("AGPS", "AP3B1", "ERC1", "FBN1",
"LOX", "MOV10", "PABPC4", "QSOX2", "SLC25A21")
show_idx = c()
for(g in druggable_genes){
show_idx = c(show_idx, which(colnames(PPI_gene_ranks) == g))
}
anno = columnAnnotation(genes = anno_mark(at = show_idx,
labels = druggable_genes,
labels_gp = gpar(fontface = 'italic')))
ht_cluster_genes = Heatmap(as.matrix(PPI_gene_ranks),
show_row_names =T,
show_row_dend = T,
show_column_dend = F,
show_column_names = F,
cluster_rows = T,
cluster_columns = T,
col = col_fun,
row_dend_side = "left",
row_km = 6, ## split by kmeans cluster
row_km_repeats = 100,
cluster_row_slices = F,
bottom_annotation = bait_annotation,
top_annotation = anno,
column_title = "SARS-CoV-2 genome components",
column_title_side = "bottom",
heatmap_legend_param = list(
title = "Gene expression Rank",
at = c(1, 49),
labels = c("1", "49"),
direction = "horizontal",
legend_width = unit(10, "cm"))
)
setEPS()
postscript("results/PPI_results/PPI_ranks_corrected_for_cov_heatmap_clusterGenes_v3.eps", height = 5, width = 16)
draw(ht_cluster_genes, heatmap_legend_side = 'bottom')
dev.off()
### only the 49 genes
baits_withGene_ranks = intersect(as.character(PPI_dat %>% pull(PreyGene)), druggable_genes)
PPI_dat = PPI_dat %>% filter(PreyGene %in% baits_withGene_ranks)
PPI_gene_ranks = PPI_gene_ranks[,as.character(PPI_dat %>% pull(PreyGene))]
PPI_gene_ranks_baits = as.character(PPI_dat %>% pull(Bait))
ht_opt(
legend_title_gp = gpar(fontsize = 8, fontface = "bold"),
legend_labels_gp = gpar(fontsize = 8),
heatmap_column_names_gp = gpar(fontsize = 8),
heatmap_column_title_gp = gpar(fontsize = 10),
heatmap_row_title_gp = gpar(fontsize = 8)
)
bait_annotation = HeatmapAnnotation(bait = PPI_gene_ranks_baits,
col = list(bait = unique_baits_cols),
annotation_name_side = "left",
annotation_legend_param = list(
bait = list(
direction = 'horizontal',
title = 'Bait',
at = names(unique_baits_cols),
labels = names(unique_baits_cols)
)
))
col_fun = colorRamp2(c(1, 49), c('#53bdf8','gray10'))
show_idx = c()
for(g in druggable_genes){
show_idx = c(show_idx, which(colnames(PPI_gene_ranks) == g))
}
anno = columnAnnotation(genes = anno_mark(at = show_idx,
labels = druggable_genes,
labels_gp = gpar(fontface = 'italic')))
ht_cluster_genes = Heatmap(as.matrix(PPI_gene_ranks),
show_row_names =T,
show_row_dend = T,
show_column_dend = F,
show_column_names = F,
cluster_rows = T,
cluster_columns = T,
col = col_fun,
row_dend_side = "left",
row_km = 6, ## split by kmeans cluster
row_km_repeats = 100,
cluster_row_slices = F,
bottom_annotation = bait_annotation,
top_annotation = anno,
column_title = "SARS-CoV-2 genome components",
column_title_side = "bottom",
heatmap_legend_param = list(
title = "Gene expression Rank",
at = c(1, 49),
labels = c("1", "49"),
direction = "horizontal",
legend_width = unit(10, "cm"))
)
setEPS()
postscript("results/PPI_results/PPI_ranks_corrected_for_cov_heatmap_clusterGenes_v3.eps", height = 5, width = 16)
draw(ht_cluster_genes, heatmap_legend_side = 'bottom')
dev.off()