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populations_study_plot.R
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populations_study_plot.R
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# Install and load necessary packages
library(UpSetR)
library(tidyverse)
library(tidyverse)
library(gridExtra)
library(grid)
library(svglite)
# Load the data
data <- read.delim("/Users/yperez/work/pgt-pangenome/blast_canonical-count-tables/1-2mismatches_peptides_match_info/peps_merged_populations.tsv", header = TRUE, sep = "\t")
# Ensure your data has the appropriate column names
# Assuming columns are named 'peptide_sequence', 'gene', and 'population_category'
# Prepare the data for UpSet plot
data_wide <- data %>%
distinct(genes, population_groups) %>%
mutate(presence = 1) %>%
spread(population_groups, presence, fill = 0)
# Prepare the data for peptide sequences
data_peptides <- data %>%
distinct(peptide, population_groups) %>%
mutate(presence = 1) %>%
spread(population_groups, presence, fill = 0)
# Prepare the data for genes
data_genes <- data %>%
distinct(genes, population_groups) %>%
mutate(presence = 1) %>%
spread(population_groups, presence, fill = 0)
# Define a vector of colors for the population groups
population_colors <- c("#48D1CC", "#9370DB", "#1E90FF", "#4682B4", "#7a96ac", "#DAA520")
# Create an UpSet plot for peptide sequences
p1 <- UpSetR::upset(data_peptides, sets = colnames(data_peptides)[-1], main.bar.color = "#1f77b4",sets.bar.color = population_colors,order.by = "freq", empty.intersections = "on")
p2 <- UpSetR::upset(data_genes, sets = colnames(data_genes)[-1], main.bar.color = "#1f77b4", sets.bar.color = population_colors, order.by = "freq",empty.intersections = "on")
# Convert the UpSet plots to ggplot objects
p1_plot <- cowplot::plot_grid(NULL, p1$BoxPlots, p1$Sizes, ncol = 1, align = 'v', rel_heights = c(0.2, 0.5, 0.3))
p2_plot <- cowplot::plot_grid(NULL, p1$Main_bar, p1$Matrix, ncol = 1, align = 'v', rel_heights = c(0.2, 0.5, 0.3))
variants_plot <- cowplot::plot_grid(p1_plot, p2_plot, ncol = 2, rel_widths = c(0.3, 1.7))
ggsave("populations_variants.svg", variants_plot, width = 12, height = 6, units = "in", dpi = 300)
p3_plot <- cowplot::plot_grid(NULL, p2$BoxPlots, p2$Sizes, ncol = 1, align = 'v', rel_heights = c(0.2, 0.5, 0.3))
p4_plot <- cowplot::plot_grid(NULL, p2$Main_bar, p2$Matrix, ncol = 1, align = 'v', rel_heights = c(0.2, 0.5, 0.3))
genes_plot <- cowplot::plot_grid(p3_plot, p4_plot, ncol = 2, rel_widths = c(0.3, 1.7))
ggsave("populations_genes.svg", genes_plot, width = 12, height = 6, units = "in", dpi = 300)
# Arrange the plots side by side
final_plot <- cowplot::plot_grid(p1_plot, p2_plot, p3_plot, p4_plot, ncol = 4, rel_widths = c(0.3, 1.7, 0.3, 1.7))
# Save the final plot as PNG
ggsave("populations_variants_genes.png", final_plot, width = 12, height = 6, units = "in", dpi = 300)
# Save the final plot as SVG
ggsave("populations_variants_genes.svg", final_plot, width = 12, height = 6, units = "in")
# -----------------------
# Install and load necessary library
library(ggplot2)
# Given dictionary with color codes
a <- c('T2T', 'African', 'South-American', 'Asian', 'Caucasian', 'South-Asian')
b <- c(1, 44, 34, 10, 6, 2)
# Convert dictionary to data frame
df <- data.frame(Category = a, Value = b)
# Plot horizontal bar chart with numbers and specified colors
horizontal_bar <- ggplot(df, aes(x = Category, y = Value, fill = "#1f77b4")) +
geom_bar(stat = "identity", width=0.7) +
scale_fill_identity() +
labs( y = "Number of samples") + theme(panel.background = element_blank(), text=element_text(size=20), axis.title.x=element_blank())
# Show the horizontal bar chart
horizontal_bar
ggsave("number_samples_population.svg", horizontal_bar, width = 12, height = 6, units = "in")