Rural Malawi ESBL-Ec Genomics
Angus O’Ferrall
2026-01-29
# Attach packages
knitr::opts_chunk$set(echo=TRUE)
library(knitr)
library(ggplot2)
library(dplyr)##
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library(tibble)
library(forcats)
library(stringr)
library(ggtree)## ggtree v4.0.4 Learn more at https://yulab-smu.top/contribution-tree-data/
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## Please cite:
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## Shuangbin Xu, Lin Li, Xiao Luo, Meijun Chen, Wenli Tang, Li Zhan, Zehan
## Dai, Tommy T. Lam, Yi Guan, Guangchuang Yu. Ggtree: A serialized data
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## S Xu, Z Dai, P Guo, X Fu, S Liu, L Zhou, W Tang, T Feng, M Chen, L
## Zhan, T Wu, E Hu, Y Jiang, X Bo, G Yu. ggtreeExtra: Compact
## visualization of richly annotated phylogenetic data. Molecular Biology
## and Evolution. 2021, 38(9):4039-4042. doi: 10.1093/molbev/msab166library(ape)##
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## Github page: https://github.com/jokergoo/ComplexHeatmap
## Documentation: http://jokergoo.github.io/ComplexHeatmap-reference
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## If you use it in published research, please cite either one:
## - Gu, Z. Complex Heatmap Visualization. iMeta 2022.
## - Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional
## genomic data. Bioinformatics 2016.
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## If you use it in published research, please cite:
## Gu, Z. circlize implements and enhances circular visualization
## in R. Bioinformatics 2014.
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## unionlibrary(ggraph)Participant data
# Import and tidy the participant data
# Import the data
part_data <- read.csv("Data/Participant_data.csv", na.strings = "")
# Set missing data to NA
part_data[part_data == 99 ] <- NA
# Tidy up categorical variables
part_data$sex <- as.factor(part_data$sex)
part_data$follow_up <- as.factor(part_data$follow_up)
part_data$where_defecate <- as.factor(part_data$where_defecate)
part_data <- part_data %>%
mutate(
where_defecate = case_when(
where_defecate %in% c("bush", "other") ~ "other",
TRUE ~ where_defecate
)
)
part_data$drinking_water_source <- as.factor(part_data$drinking_water_source)
# Re-name variables for colonisation in 2023 (baseline) and 2024 (follow-up)
names(part_data)[names(part_data) == "baseline_ESBL_E_coli"] <- "2023"
names(part_data)[names(part_data) == "fu_ESBL_E_coli"] <- "2024"
# Convert and label categories
part_data$district <- factor(part_data$district, levels = c(0, 1), labels = c('Nsanje', 'Mangochi'))
part_data$'2023' <- factor(part_data$'2023', levels = c(0, 1), labels = c('No', 'Yes'))
part_data$'2024' <- factor(part_data$'2024', levels = c(0, 1), labels = c('No', 'Yes'))
part_data$baseline_network_strain <- factor(part_data$baseline_network_strain, levels = c(0, 1), labels = c('No', 'Yes'))
part_data$fu_network_strain <- factor(part_data$fu_network_strain, levels = c(0, 1), labels = c('No', 'Yes'))
# Make variables for colonisation with non-network strains at each time point
part_data$baseline_non_network_strain <- ifelse(
part_data$`2023` == "No",
"No",
ifelse(
part_data$baseline_network_strain == "Yes",
"No",
"Yes"
)
)
part_data$fu_non_network_strain <- ifelse(
part_data$`2024` == "No",
"No",
ifelse(
part_data$fu_network_strain == "Yes",
"No",
"Yes"
)
)# Age
summary(part_data$age_at_baseline)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.00 4.00 6.00 11.92 16.50 47.00# Categorical summaries
categorical_vars <- c(
"sex",
"follow_up",
"where_defecate",
"drinking_water_source"
)
# Generate summary of categorical variables
lapply(categorical_vars, function(v) {
part_data %>%
count(.data[[v]]) %>%
mutate(percent = round(100 * n / sum(n), 1))
})## [[1]]
## sex n percent
## 1 female 122 57.8
## 2 male 89 42.2
##
## [[2]]
## follow_up n percent
## 1 no 58 27.5
## 2 yes 153 72.5
##
## [[3]]
## where_defecate n percent
## 1 missing 1 0.5
## 2 other 28 13.3
## 3 toilet 182 86.3
##
## [[4]]
## drinking_water_source n percent
## 1 borehole 204 96.7
## 2 missing 1 0.5
## 3 unprotected_shallow_well_river_dam_lake 6 2.8Per district
Mangochi
# Filter the data
part_data_mangochi <- part_data %>%
filter(district == "Mangochi")# Age
summary(part_data_mangochi$age_at_baseline)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.0 4.0 6.0 10.2 12.0 47.0# Generate summary of categorical variables
lapply(categorical_vars, function(v) {
part_data_mangochi %>%
count(.data[[v]]) %>%
mutate(percent = round(100 * n / sum(n), 1))
})## [[1]]
## sex n percent
## 1 female 85 52.1
## 2 male 78 47.9
##
## [[2]]
## follow_up n percent
## 1 no 40 24.5
## 2 yes 123 75.5
##
## [[3]]
## where_defecate n percent
## 1 missing 1 0.6
## 2 other 22 13.5
## 3 toilet 140 85.9
##
## [[4]]
## drinking_water_source n percent
## 1 borehole 156 95.7
## 2 missing 1 0.6
## 3 unprotected_shallow_well_river_dam_lake 6 3.7Nsanje
# Filter the data
part_data_nsanje <- part_data %>%
filter(district == "Nsanje")# Age
summary(part_data_nsanje$age_at_baseline)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.00 7.00 14.00 17.77 26.25 46.00# Generate summary of categorical variables
lapply(categorical_vars, function(v) {
part_data_nsanje %>%
count(.data[[v]]) %>%
mutate(percent = round(100 * n / sum(n), 1))
})## [[1]]
## sex n percent
## 1 female 37 77.1
## 2 male 11 22.9
##
## [[2]]
## follow_up n percent
## 1 no 18 37.5
## 2 yes 30 62.5
##
## [[3]]
## where_defecate n percent
## 1 other 6 12.5
## 2 toilet 42 87.5
##
## [[4]]
## drinking_water_source n percent
## 1 borehole 48 100Differences between locations
# Age
wilcox.test(
age_at_baseline ~ district,
data = part_data %>% filter(district %in% c("Mangochi", "Nsanje"))
)##
## Wilcoxon rank sum test with continuity correction
##
## data: age_at_baseline by district
## W = 5746, p-value = 7.286e-07
## alternative hypothesis: true location shift is not equal to 0# Sex
sex_tab <- table(
part_data$sex,
part_data$district
)
fisher.test(sex_tab)##
## Fisher's Exact Test for Count Data
##
## data: sex_tab
## p-value = 0.002559
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 1.413660 7.156798
## sample estimates:
## odds ratio
## 3.071026# Follow-up
fu_tab <- table(
part_data$follow_up,
part_data$district
)
fisher.test(fu_tab)##
## Fisher's Exact Test for Count Data
##
## data: fu_tab
## p-value = 0.09744
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.8679588 3.8373770
## sample estimates:
## odds ratio
## 1.839286# Latrine
latrine_tab <- table(
part_data$where_defecate,
part_data$district
)
fisher.test(latrine_tab)##
## Fisher's Exact Test for Count Data
##
## data: latrine_tab
## p-value = 1
## alternative hypothesis: two.sided# Drinking water
water_tab <- table(
part_data$drinking_water_source,
part_data$district
)
fisher.test(water_tab)##
## Fisher's Exact Test for Count Data
##
## data: water_tab
## p-value = 0.4904
## alternative hypothesis: two.sidedCarriage dynamics
# Mutate data to show dynamics in participants followed-up
fu_part_data <- part_data %>%
filter(follow_up == "yes") %>%
mutate('change' = case_when(
`2023` == "No" & `2024` == "No" ~ "Stayed negative",
`2023` == "Yes" & `2024` == "Yes" ~ "Stayed positive",
`2023` == "No" & `2024` == "Yes" ~ "Became positive",
`2023` == "Yes" & `2024` == "No" ~ "Became negative"
))
# Set the levels for the change variable to the desired order
fu_part_data$change <- factor(fu_part_data$change,
levels = c("Stayed negative", "Became positive", "Became negative", "Stayed positive"))
# Summarise counts of participants for each type of change, and negate values for "negative" changes
summary_data <- fu_part_data %>%
count(change) %>%
mutate(n = ifelse(change %in% c("Stayed negative", "Became negative"), -n, n))
# Add the 'baseline' column based on the conditions
summary_data <- summary_data %>%
mutate(baseline = case_when(
change %in% c('Stayed negative', 'Became positive') ~ 'Negative',
change %in% c('Stayed positive', 'Became negative') ~ 'Positive'
))
# Create the plot
ESBL_dynamics <- ggplot(summary_data, aes(x = change, y = n)) +
geom_bar(stat = "identity", aes(fill = baseline), colour = "black", linewidth = 0.3, width = 0.7) +
scale_y_continuous(labels = abs) +
scale_fill_manual(values = c("Negative" = "cornflowerblue",
"Positive" = "firebrick")) +
labs(fill = "Baseline status") +
labs(x = "ESBL carriage dynamic",
y = "Number of participants") +
theme_minimal() +
theme(
axis.title.x = element_text(margin = margin(t = 10)), # Space above x-axis label
axis.title.y = element_text(margin = margin(r = 10)), # Space to the right of y-axis label
plot.margin = margin(r = 30) # Extra space on the right of the plot for legend
) +
geom_hline(yintercept = 0, linetype = "dashed")
# Save the plot
ggsave("Figures/ESBL_dynamics.png", ESBL_dynamics, height = 5, dpi = 600)## Saving 7 x 5 in image# Print the plot
print(ESBL_dynamics)
Change in prevalence
# Specify columns to look at prevalence in
cols <- c("2023", "2024", "baseline_network_strain", "baseline_non_network_strain", "fu_network_strain", "fu_non_network_strain")
# Calculate prevalences in 2023 and 2024 of colonisation, colonisation with a network strain, and colonisation with a non_network strain
prevalence <- sapply(part_data[cols], function(x) {
mean(x == "Yes", na.rm = TRUE)
})
print(prevalence)## 2023 2024
## 0.3412322 0.5424837
## baseline_network_strain baseline_non_network_strain
## 0.1706161 0.1706161
## fu_network_strain fu_non_network_strain
## 0.2549020 0.2875817# Calculate prevalences in participants followed-up
prevalence <- sapply(fu_part_data[cols], function(x) {
mean(x == "Yes", na.rm = TRUE)
})
print(prevalence)## 2023 2024
## 0.3333333 0.5424837
## baseline_network_strain baseline_non_network_strain
## 0.1568627 0.1764706
## fu_network_strain fu_non_network_strain
## 0.2549020 0.2875817# Check for significant rise in prevalence in colonisation follow-up participants using McNemar's test for paired data
colonisation_tab <- table(fu_part_data$'2023', fu_part_data$'2024')
mcnemar.test(colonisation_tab)##
## McNemar's Chi-squared test with continuity correction
##
## data: colonisation_tab
## McNemar's chi-squared = 11.174, df = 1, p-value = 0.0008293Prevalence differences between locations
# Baseline prevalence
bl_prev_tab <- table(
part_data$"2023",
part_data$district
)
bl_prev_tab##
## Nsanje Mangochi
## No 32 107
## Yes 16 56chisq.test(bl_prev_tab, correct = FALSE)##
## Pearson's Chi-squared test
##
## data: bl_prev_tab
## X-squared = 0.017246, df = 1, p-value = 0.8955# Follow-up prevalence
fu_prev_tab <- table(
fu_part_data$"2024",
fu_part_data$district
)
fu_prev_tab##
## Nsanje Mangochi
## No 12 58
## Yes 18 65chisq.test(fu_prev_tab, correct = FALSE)##
## Pearson's Chi-squared test
##
## data: fu_prev_tab
## X-squared = 0.49739, df = 1, p-value = 0.4806Prevalence summaries with confidence intervals
All participants
# Define the variables of interest
vars <- c("2023", "2024")
# Initialize the summary dataframe
prevalence_summary <- data.frame(
Variable = vars,
Positive_Count = NA,
Total_N = NA,
Prevalence_Percent = NA,
CI_Lower = NA,
CI_Upper = NA
)
# Loop through variables and calculate values
for (i in seq_along(vars)) {
var <- vars[i]
x <- part_data[[var]]
pos <- sum(x %in% c("Yes"), na.rm = TRUE)
total <- sum(!is.na(x))
test <- prop.test(pos, total)
prevalence_summary$Positive_Count[i] <- pos
prevalence_summary$Total_N[i] <- total
prevalence_summary$Prevalence_Percent[i] <- round(100 * pos / total, 1)
prevalence_summary$CI_Lower[i] <- round(100 * test$conf.int[1], 1)
prevalence_summary$CI_Upper[i] <- round(100 * test$conf.int[2], 1)
}
# Print the table
print(prevalence_summary)## Variable Positive_Count Total_N Prevalence_Percent CI_Lower CI_Upper
## 1 2023 72 211 34.1 27.8 41.0
## 2 2024 83 153 54.2 46.0 62.3Baseline prevalence in follow-up participants
# Define the variables of interest
vars <- c("2023")
# Initialize the summary dataframe
prevalence_summary <- data.frame(
Variable = vars,
Positive_Count = NA,
Total_N = NA,
Prevalence_Percent = NA,
CI_Lower = NA,
CI_Upper = NA
)
# Loop through variables and calculate values
for (i in seq_along(vars)) {
var <- vars[i]
x <- fu_part_data[[var]]
pos <- sum(x %in% c("Yes"), na.rm = TRUE)
total <- sum(!is.na(x))
test <- prop.test(pos, total)
prevalence_summary$Positive_Count[i] <- pos
prevalence_summary$Total_N[i] <- total
prevalence_summary$Prevalence_Percent[i] <- round(100 * pos / total, 1)
prevalence_summary$CI_Lower[i] <- round(100 * test$conf.int[1], 1)
prevalence_summary$CI_Upper[i] <- round(100 * test$conf.int[2], 1)
}
# Print the table
print(prevalence_summary)## Variable Positive_Count Total_N Prevalence_Percent CI_Lower CI_Upper
## 1 2023 51 153 33.3 26.1 41.5Mangochi District
# Define the variables of interest
vars <- c("2023", "2024")
# Initialize the summary dataframe
prevalence_summary <- data.frame(
Variable = vars,
Positive_Count = NA,
Total_N = NA,
Prevalence_Percent = NA,
CI_Lower = NA,
CI_Upper = NA
)
# Loop through variables and calculate values
for (i in seq_along(vars)) {
var <- vars[i]
x <- part_data_mangochi[[var]]
pos <- sum(x %in% c("Yes"), na.rm = TRUE)
total <- sum(!is.na(x))
test <- prop.test(pos, total)
prevalence_summary$Positive_Count[i] <- pos
prevalence_summary$Total_N[i] <- total
prevalence_summary$Prevalence_Percent[i] <- round(100 * pos / total, 1)
prevalence_summary$CI_Lower[i] <- round(100 * test$conf.int[1], 1)
prevalence_summary$CI_Upper[i] <- round(100 * test$conf.int[2], 1)
}
# Print the table
print(prevalence_summary)## Variable Positive_Count Total_N Prevalence_Percent CI_Lower CI_Upper
## 1 2023 56 163 34.4 27.2 42.2
## 2 2024 65 123 52.8 43.7 61.8Nsanje District
# Define the variables of interest
vars <- c("2023", "2024")
# Initialize the summary dataframe
prevalence_summary <- data.frame(
Variable = vars,
Positive_Count = NA,
Total_N = NA,
Prevalence_Percent = NA,
CI_Lower = NA,
CI_Upper = NA
)
# Loop through variables and calculate values
for (i in seq_along(vars)) {
var <- vars[i]
x <- part_data_nsanje[[var]]
pos <- sum(x %in% c("Yes"), na.rm = TRUE)
total <- sum(!is.na(x))
test <- prop.test(pos, total)
prevalence_summary$Positive_Count[i] <- pos
prevalence_summary$Total_N[i] <- total
prevalence_summary$Prevalence_Percent[i] <- round(100 * pos / total, 1)
prevalence_summary$CI_Lower[i] <- round(100 * test$conf.int[1], 1)
prevalence_summary$CI_Upper[i] <- round(100 * test$conf.int[2], 1)
}
# Print the table
print(prevalence_summary)## Variable Positive_Count Total_N Prevalence_Percent CI_Lower CI_Upper
## 1 2023 16 48 33.3 20.8 48.5
## 2 2024 18 30 60.0 40.8 76.8Isolate data
# Import the isolate data
isolate_data_human <- read.csv("Data/Malawi_ESBL_E_coli_isolates.csv")
# Label and tidy ESBL isolate data from 2023
isolate_data_human$District <- as.factor(isolate_data_human$District)
isolate_data_human$Year <- as.factor(isolate_data_human$Year)
isolate_data_human$ST <- as.factor(isolate_data_human$ST)
isolate_data_human$popPUNK_cluster <- as.factor(isolate_data_human$popPUNK_cluster)
isolate_data_human$Phylogroup <- as.factor(isolate_data_human$Phylogroup)
isolate_data_human$fu_participant <- factor(isolate_data_human$fu_participant, levels = c(0, 1), labels = c('No', 'Yes'))
# Generate lineage column combining ST and popPUNK cluster
isolate_data_human$Lineage <- paste0("ST", isolate_data_human$ST, "_popPUNK", isolate_data_human$popPUNK_cluster)
isolate_data_human$Lineage <- as.factor(isolate_data_human$Lineage)Lineages
# Print a summary of the lineages
summary(isolate_data_human$Lineage)## ST10_popPUNK5 ST10_popPUNK92 ST1015_popPUNK11
## 4 6 10
## ST10955_popPUNK1410 ST1139_popPUNK1409 ST1158_popPUNK453
## 1 2 1
## ST12714_popPUNK1408 ST131_popPUNK25 ST131_popPUNK4
## 2 10 9
## ST13823_popPUNK1407 ST1421_popPUNK62 ST1485_popPUNK135
## 8 1 1
## ST167_popPUNK5 ST1722_popPUNK190 ST189_popPUNK35
## 5 3 2
## ST206_popPUNK41_1328 ST2161_popPUNK133 ST2172_popPUNK271
## 14 1 1
## ST2852_popPUNK18 ST295_popPUNK107 ST315_popPUNK20
## 5 2 1
## ST349_popPUNK73 ST3580_popPUNK18 ST38_popPUNK20
## 1 5 23
## ST44_popPUNK223 ST443_popPUNK61 ST5614_popPUNK251
## 6 1 10
## ST58_popPUNK11 ST5909_popPUNK46 ST617_popPUNK60
## 8 3 2
## ST6359_popPUNK60 ST636_popPUNK140 ST648_popPUNK31
## 3 1 3
## ST73_popPUNK8 ST8025_popPUNK5
## 1 3# STs belonging to more than one popPUNK cluster
ST_multiple_popPUNK <- isolate_data_human %>%
group_by(ST) %>%
summarise(n_popPUNK = n_distinct(popPUNK_cluster)) %>%
filter(n_popPUNK > 1)
ST_multiple_popPUNK## # A tibble: 2 Ă— 2
## ST n_popPUNK
## <fct> <int>
## 1 10 2
## 2 131 2# popPUNK clusters belonging to more than one ST
popPUNK_multiple_ST <- isolate_data_human %>%
group_by(popPUNK_cluster) %>%
summarise(n_ST = n_distinct(ST)) %>%
filter(n_ST > 1)
popPUNK_multiple_ST## # A tibble: 5 Ă— 2
## popPUNK_cluster n_ST
## <fct> <int>
## 1 11 2
## 2 18 2
## 3 20 2
## 4 5 3
## 5 60 2Sequence types (MLST)
# Plot STs of all isolates
ST_plot <- ggplot(isolate_data_human, aes(x = fct_rev(fct_infreq(ST)), fill = Phylogroup)) +
geom_bar(color = "black", linewidth = 0.3) +
coord_flip() +
theme_bw() +
labs(x = "ST", y = "Number of Isolates") +
scale_fill_brewer(palette = "Set2")
# Save the plot
ggsave("Figures/ST_plot.png", ST_plot, width = 4, height = 5, dpi = 600)
# Print the plot
print(ST_plot)
# Add year breakdown to ST plot
# Define the number of isolates per year
isolate_totals <- c(`2023` = 76, `2024` = 83)
# Create a dataset of all possible combinations of ST and Year (2023, 2024)
all_st_year_combinations <- expand.grid(ST = unique(isolate_data_human$ST), Year = c(2023, 2024))
all_st_year_combinations$Year <- as.factor(all_st_year_combinations$Year)
# Summarize ST rates per 100 isolates per year
ST_summary_data <- isolate_data_human %>%
group_by(ST, Year) %>%
summarise(Count = n(), .groups = "drop") %>%
full_join(all_st_year_combinations, by = c("ST", "Year")) %>% # Ensure all ST-Year combinations exist
mutate(Count = replace_na(Count, 0)) %>% # Fill missing values with 0
mutate(rate_per_100 = (Count / isolate_totals[as.character(Year)]) * 100)
# Calculate total counts of each ST to enable plot ordering
ST_summary_data <- ST_summary_data %>%
group_by(ST) %>%
mutate(total_count = sum(Count)) %>%
ungroup()
# Reorder samples in highest to lowest total isolate count order to match the first ST plot
ST_summary_data <- ST_summary_data %>%
mutate(ST = fct_rev(fct_reorder(ST, total_count, .desc = TRUE)))
# Plot ST rates per 100 participants per year
ST_plot_years <- ggplot(ST_summary_data, aes(x = ST, y = rate_per_100, fill = factor(Year))) +
geom_bar(color = "black", linewidth = 0.3, stat = "identity", position = position_dodge(width = 0.8)) +
coord_flip() +
theme_bw() +
labs(x = "ST", y = "Percentage of isolates per year", fill = "Year")
# Save the plot
ggsave("Figures/ST_plot_years.png", ST_plot_years, width = 4, height = 5, dpi = 600)
# Print the plot
print(ST_plot_years)
# Fisher exact test with Monte Carlo simulation
st_year_tab <- xtabs(Count ~ ST + Year, data = ST_summary_data)
set.seed(1)
fisher.test(st_year_tab, simulate.p.value = TRUE, B = 1e4)##
## Fisher's Exact Test for Count Data with simulated p-value (based on
## 10000 replicates)
##
## data: st_year_tab
## p-value = 9.999e-05
## alternative hypothesis: two.sidedARGs
# Import and clean ARG data
# Import data
ARGs <- read.csv("Data/ARGs.csv", check.names = FALSE)
# Remove '.fasta' from all entries in column 1
ARGs[, 1] <- gsub("\\.fasta", "", ARGs[, 1])
# Apply transformations to all columns except 'Isolate' names so ARGs are present (1) or absent (0)
ARGs[, -1] <- apply(ARGs[, -1], 2, function(x) {
ifelse(is.na(as.numeric(x)), 0, 1)
})## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
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## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion# Clean column names
colnames(ARGs) <- gsub("bla", "", colnames(ARGs)) # Remove 'bla'
colnames(ARGs) <- sapply(colnames(ARGs), function(x) {
paste0(toupper(substring(x, 1, 1)), substring(x, 2))
}) # Capitalize first letter
# Create a base name without suffixes for grouping
base_names <- sub("_.*", "", colnames(ARGs))
# Create a list of indices for columns to merge based on cleaned names
cols_to_merge_list <- split(seq_along(colnames(ARGs)), base_names)
# Track columns to delete after summing
cols_to_delete <- c()
# Sum values into the first column for each group and mark columns for deletion
for (col_indices in cols_to_merge_list) {
if (length(col_indices) > 1) {
# Sum values into the first column
summed_column <- rowSums(ARGs[, col_indices], na.rm = TRUE)
# Set to 1 if total is greater than 1
summed_column[summed_column > 1] <- 1
# Replace the first occurrence with the summed column
ARGs[, col_indices[1]] <- summed_column
# Mark all other columns except the first for deletion
cols_to_delete <- c(cols_to_delete, col_indices[-1])
}
}
# Delete the marked columns all at once after the loop
ARGs <- ARGs[, -cols_to_delete]
# Clean column names (remove everything after and including '_')
colnames(ARGs) <- gsub("_.*", "", colnames(ARGs))
# Mutate the data to have Isolate and ARG columns
ARGs_long <- ARGs %>%
pivot_longer(
cols = -Isolate,
names_to = "ARG",
values_to = "Presence"
)
# Filter for only relevant rows (where presence is 1)
ARGs_long <- ARGs_long %>%
dplyr::filter(Presence == 1) %>%
dplyr::select(-Presence)
# Print all unique AMR determinants in data set
(colnames(ARGs)[-1])## [1] "ARR-3" "Aac(3)-IIa" "Aac(3)-IId" "Aac(3)-IVa"
## [5] "Aac(6')-Ib-cr" "AadA16" "AadA2" "AadA5"
## [9] "Ant(3'')-Ia" "Aph(3'')-Ib" "Aph(4)-Ia" "Aph(6)-Id"
## [13] "CTX-M-14" "CTX-M-14b" "CTX-M-15" "CTX-M-27"
## [17] "CTX-M-55" "CTX-M-65" "DHA-1" "OXA-1"
## [21] "TEM-141" "TEM-1B" "CatA1" "DfrA12"
## [25] "DfrA14" "DfrA17" "DfrA1" "DfrA27"
## [29] "DfrA5" "DfrA7" "DfrA8" "Erm(B)"
## [33] "FloR" "FosA3" "Mph(A)" "QepA1"
## [37] "QepA4" "QnrB1" "QnrB4" "QnrB6"
## [41] "QnrS1" "Sul1" "Sul2" "Tet(A)"
## [45] "Tet(B)" "Tet(D)"# Define the AMR classes and their associated genes
aminoglycoside = "Aac|Aad|Ant|Aph"
beta_lactam_AmpC = "DHA"
beta_lactam_ESBL = "CTX"
beta_lactam_other = "OXA|TEM"
chloramphenicol = "Cat|FloR"
fosfomycin = "Fos"
macrolide = "Mph"
MDR = "Erm"
quinolone = "Qnr|Qep"
rifampicin = "ARR"
sulphonamide = "Sul"
tetracycline = "Tet"
trimethoprim = "Dfr"# Calculate the absolute abundance (counting only where Presence is 1) and label ARG classes
ARG_abundance <- ARGs_long %>%
group_by(ARG) %>%
summarise(Count = n(), .groups = "drop") %>%
mutate(class = case_when(
str_detect(ARG, aminoglycoside) ~ "Aminoglycoside",
str_detect(ARG, beta_lactam_AmpC) ~ "Beta-lactam (AmpC)",
str_detect(ARG, beta_lactam_ESBL) ~ "Beta-lactam (ESBL)",
str_detect(ARG, beta_lactam_other) ~ "Beta-lactam (other)",
str_detect(ARG, chloramphenicol) ~ "Chloramphenicol",
str_detect(ARG, fosfomycin) ~ "Fosfomycin",
str_detect(ARG, macrolide) ~ "Macrolide",
str_detect(ARG, MDR) ~ "MDR",
str_detect(ARG, quinolone) ~ "Quinolone",
str_detect(ARG, rifampicin) ~ "Rifampicin",
str_detect(ARG, sulphonamide) ~ "Sulphonamide",
str_detect(ARG, tetracycline) ~ "Tetracycline",
str_detect(ARG, trimethoprim) ~ "Trimethoprim",
TRUE ~ NA_character_
))
# Reorder ARGs for plotting: first by class, then by count (highest to lowest)
ARG_abundance <- ARG_abundance %>%
arrange(class, desc(Count)) %>%
mutate(ARG = factor(ARG, levels = ARG))
# Reverse the order of classes and ARGs for top-to-bottom plotting
ARG_abundance <- ARG_abundance %>%
mutate(class = factor(class, levels = sort(unique(class)))) %>%
mutate(ARG = factor(ARG, levels = rev(ARG[order(ARG_abundance$class, -ARG_abundance$Count)])))
# Create the bar plot
AMR_plot <- ggplot(ARG_abundance, aes(y = ARG, x = Count, fill = class)) +
geom_bar(color = "black", linewidth = 0.3, stat = "identity") +
labs(y = "AMR Determinant", x = "Number of Isolates") +
theme_bw() +
guides(fill = guide_legend(title = "Class")) +
theme(axis.text.y = element_text(size = 10),
plot.background = element_rect(fill = "transparent", colour = NA),
legend.background = element_rect(fill = "transparent", colour = NA)
) +
scale_fill_manual(values=c("#1b9e77", "#fdbf6f", "#7570b3", "#d95f02",
"#66a61e", "#e6ab02", "#a6761d", "#e7298a",
"#1f78b4", "#b2df8a", "#fb9a99", "#666666",
"#cab2d6", "#ffff99"))
# Save the plot
ggsave("Figures/AMR_plot.png", AMR_plot, height = 7, width = 6, dpi = 600)
# Print the plot
print(AMR_plot)
# Add year breakdown in follow-up participants to ARG plot
# Add year to follow-up ARG data
ARGs <- ARGs %>%
mutate(Year = case_when(
grepl("^c", Isolate) ~ 2023,
grepl("^f", Isolate) ~ 2024,
TRUE ~ NA_real_
))
# Convert to long format
ARGs_fu_long <- ARGs %>%
pivot_longer(cols = -c(Isolate, Year), names_to = "ARG", values_to = "Presence")
# Summarize counts for each gene per year
ARG_summary_data <- ARGs_fu_long %>%
group_by(ARG, Year) %>%
summarize(Count = sum(Presence), .groups = "drop") %>%
mutate(rate_per_100 = (Count / isolate_totals[as.character(Year)]) * 100) %>%
mutate(class = case_when(
str_detect(ARG, aminoglycoside) ~ "Aminoglycoside",
str_detect(ARG, beta_lactam_AmpC) ~ "Beta-lactam (AmpC)",
str_detect(ARG, beta_lactam_ESBL) ~ "Beta-lactam (ESBL)",
str_detect(ARG, beta_lactam_other) ~ "Beta-lactam (other)",
str_detect(ARG, chloramphenicol) ~ "Chloramphenicol",
str_detect(ARG, fosfomycin) ~ "Fosfomycin",
str_detect(ARG, macrolide) ~ "Macrolide",
str_detect(ARG, MDR) ~ "MDR",
str_detect(ARG, quinolone) ~ "Quinolone",
str_detect(ARG, rifampicin) ~ "Rifampicin",
str_detect(ARG, sulphonamide) ~ "Sulphonamide",
str_detect(ARG, tetracycline) ~ "Tetracycline",
str_detect(ARG, trimethoprim) ~ "Trimethoprim",
TRUE ~ NA_character_
))
# Calculate total counts of each ARG to enable plot ordering
ARG_summary_data <- ARG_summary_data %>%
group_by(ARG) %>%
mutate(total_count = sum(Count)) %>%
ungroup() %>%
filter(total_count > 0)
# Reverse the order of classes and ARGs for top-to-bottom plotting
ARG_summary_data <- ARG_summary_data %>%
mutate(class = factor(class, levels = sort(unique(class)))) %>%
group_by(ARG) %>%
slice_max(total_count, n = 1) %>%
ungroup() %>%
arrange(class, desc(total_count)) %>%
mutate(ARG = factor(ARG, levels = rev(unique(ARG))))
# Plot ST rates per 100 participants per year
ARG_plot_years <- ggplot(ARG_summary_data, aes(x = ARG, y = rate_per_100, fill = factor(Year))) +
geom_bar(color = "black", linewidth = 0.3, stat = "identity", position = position_dodge(width = 0.8)) +
coord_flip() +
theme_bw() +
labs(x = "AMR Determinant", y = "Percentage of isolates per year", fill = "Year") +
theme(axis.text.y = element_text(size = 10)) # Adjust y-axis text size for better readability
# Save the plot
ggsave("Figures/ARG_plot_years.png", ARG_plot_years, height = 7, width = 6, dpi = 600)
# Print the plot
print(ARG_plot_years)
# Fisher exact test with Monte Carlo simulation
arg_year_tab <- xtabs(Count ~ ARG + Year, data = ARG_summary_data)
set.seed(1)
fisher.test(arg_year_tab, simulate.p.value = TRUE, B = 1e4)##
## Fisher's Exact Test for Count Data with simulated p-value (based on
## 10000 replicates)
##
## data: arg_year_tab
## p-value = 9.999e-05
## alternative hypothesis: two.sidedPlasmid types
# Import and can plasmid type data
# Import data
plasmids <- read.csv("Data/Plasmid_data.csv", check.names = FALSE)
# Remove '.fasta' from all entries in column 1
plasmids[, 1] <- gsub("\\.fasta", "", plasmids[, 1])
# Apply transformations to all columns except 'Isolate' names plasmids are present (1) or absent (0)
plasmids[, -1] <- apply(plasmids[, -1], 2, function(x) {
ifelse(is.na(as.numeric(x)), 0, 1)
})## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
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## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion# Create a base name without suffixes for grouping
base_names <- sub("_.*", "", colnames(plasmids))
# Create a list of indices for columns to merge based on cleaned names
cols_to_merge_list <- split(seq_along(colnames(plasmids)), base_names)
# Track columns to delete after summing
cols_to_delete <- c()
# Sum values into the first column for each group and mark columns for deletion
for (col_indices in cols_to_merge_list) {
if (length(col_indices) > 1) {
# Sum values into the first column
summed_column <- rowSums(plasmids[, col_indices], na.rm = TRUE)
# Set to 1 if total is greater than 1
summed_column[summed_column > 1] <- 1
# Replace the first occurrence with the summed column
plasmids[, col_indices[1]] <- summed_column
# Mark all other columns except the first for deletion
cols_to_delete <- c(cols_to_delete, col_indices[-1])
}
}
# Delete the marked columns all at once after the loop
plasmids <- plasmids[, -cols_to_delete]
# Clean column names (remove everything after and including '_')
colnames(plasmids) <- gsub("_.*", "", colnames(plasmids))
# Mutate the data to have Isolate and ARG columns
plasmids_long <- plasmids %>%
pivot_longer(
cols = -Isolate,
names_to = "Plasmid",
values_to = "Presence"
)
# filter for only relevant rows (where presence is 1)
plasmids_long <- plasmids_long %>%
dplyr::filter(Presence == 1) %>%
dplyr::select(-Presence)
# print all unique AMR determinants in data set
(colnames(plasmids)[-1])## [1] "IncA/C2" "IncA/C" "IncB/O/K/Z" "IncFIA" "IncFIB"
## [6] "IncFIC" "IncFII" "IncHI1A" "IncHI1B" "IncHI2A"
## [11] "IncHI2" "IncI1" "IncI2" "IncIÎł" "IncX1"
## [16] "IncY"# Calculate the absolute abundance (counting only where Presence is 1) of plasmids
plasmid_abundance <- plasmids_long %>%
group_by(Plasmid) %>%
summarise(Count = n(), .groups = "drop")
# Reorder plasmids based on Count
plasmid_abundance <- plasmid_abundance %>%
mutate(Plasmid = fct_reorder(Plasmid, Count))
# Create the plot
plasmid_plot <- ggplot(plasmid_abundance, aes(y = Plasmid, x = Count)) +
geom_bar(colour = "black", linewidth = 0.3, stat = "identity") +
labs(y = "Plasmid Replicon", x = "Number of Isolates") +
theme_bw() +
theme(axis.text.y = element_text(size = 10))
# Save the plot
ggsave("Figures/Plasmid_plot.png", plasmid_plot, height = 5, width = 4, dpi = 600)
# Print the plot
print(plasmid_plot)
Virulence
# Import data
virulence <- read.csv("Data/virulence.csv", check.names = FALSE)
# Remove '.fasta' from all entries in column 1
virulence[, 1] <- gsub("\\.fasta", "", virulence[, 1])
# Apply transformations to all columns except 'Isolate' names so gene names are present (1) or absent (0)
virulence[, -1] <- apply(virulence[, -1], 2, function(x) {
ifelse(is.na(as.numeric(x)), 0, 1)
})## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
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## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion
## Warning in ifelse(is.na(as.numeric(x)), 0, 1): NAs introduced by coercion# Inspect data
virulence## #FILE NUM_FOUND aap/aspU afaA afaB-I afaC-I afaD afaE-I afaE-V afaF-III
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## afaF-VII agg3A agg3B agg3C agg3D aggB aggC aggD aslA astA cesAB cesD cesD2
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## 159 1 1 1 1 1 1 1Phylogenetic tree with annotation
# Read in the treefile
tree <- read.tree("Data/human_esbl_core_snps.aln.treefile")
# Root the tree at midpoint
tree <- midpoint(tree)
# Extract isolate metadata for tree
phylogroup_data <- isolate_data_human[, c("Isolate", "Phylogroup")]
# Reorder metadata to match tree tip order
phylogroup_data <- phylogroup_data[match(tree$tip.label, phylogroup_data$Isolate), ]
rownames(phylogroup_data) <- phylogroup_data$Isolate
# Extract District data for plotting
district_data <- isolate_data_human[, c("Isolate", "District")]
# Extract Year data for plotting
year_data <- isolate_data_human[, c("Isolate", "Year")]
# Prepare and reorder ESBL gene data for tree_plotting
tree_arg_data <- ARGs
selected_columns <- grep("Isolate|CTX", colnames(tree_arg_data), value = TRUE)
tree_arg_data <- tree_arg_data[, selected_columns]
# Mutate the ESBL gene data to have Isolate and ARG columns
ESBL_genes_long <- tree_arg_data %>%
pivot_longer(
cols = -Isolate,
names_to = "ARG",
values_to = "Presence"
)
# Filter for only relevant rows (where presence is 1)
ESBL_genes_long <- ESBL_genes_long %>%
dplyr::filter(Presence == 1) %>%
dplyr::select(-Presence)
# Generate the basic tree with phylogroup data
p <- ggtree(tree) %<+% phylogroup_data +
geom_tippoint(
aes(fill = Phylogroup),
shape = 21,
color = "black",
stroke = 0.3,
size = 2.0
) +
scale_fill_brewer(palette = "Set2") +
geom_treescale(x = 0.02, y = 100, width = 0.01, fontsize = 3, linesize = 0.5)## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## â„ą Please use `linewidth` instead.
## â„ą The deprecated feature was likely used in the ggtree package.
## Please report the issue at <https://github.com/YuLab-SMU/ggtree/issues>.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.# Add District
p1 <- p +
new_scale_fill() +
geom_fruit(
data = district_data,
geom = geom_tile,
mapping = aes(y = Isolate, x = District, fill = District),
color = "black",
offset = 0.03, # The distance between external layers, default is 0.03 times of x range of tree.
pwidth = 0.04 # width of the external layer, default is 0.2 times of x range of tree.
) +
scale_fill_brewer(palette = "Set1")
# Add Year
p2 <- p1 +
new_scale_fill() +
geom_fruit(
data = year_data,
geom = geom_tile,
mapping = aes(y = Isolate, x = Year, fill = Year),
color = "black",
offset = 0.04, # The distance between external layers, default is 0.03 times of x range of tree.
pwidth = 0.04 # width of the external layer, default is 0.2 times of x range of tree.
) +
scale_fill_brewer(palette = "Set3")
# Add ESBL genes
p3 <- p2 +
new_scale_fill() +
geom_fruit(
data = ESBL_genes_long,
geom = geom_tile,
mapping = aes(y = Isolate, x = ARG, fill = ARG),
color = "black",
offset = 0.04, # The distance between external layers, default is 0.03 times of x range of tree.
pwidth = 0.2 # width of the external layer, default is 0.2 times of x range of tree.
) +
scale_fill_brewer(name = "ESBL gene", palette = "Set2")
# Save the plot
ggsave("Figures/Tree.png", p3, width = 12, height = 6.5, dpi = 600)
# Print the plot
print(p3)
Mapping lineages between households
# Import data
# Read in household GPS data
households <- read.csv("data/Households_Jittered.csv")
# Read in lineages
interhousehold_lineages <- read.csv("data/Household_clusters.csv")Samama (Mangochi District) Maps
## Create the base map
# Convert Samama household coordinates to sf object and project
samama_sf <- households %>%
filter(district == "Mangochi") %>%
st_as_sf(coords = c("GPS_E", "GPS_S"), crs = 4326)
samama_sf_proj <- st_transform(samama_sf, 32736)
# Define and project bounding box
rectangle_coords_samama <- matrix(c(
35.2166, -14.4150,
35.2260, -14.4150,
35.2260, -14.4230,
35.2166, -14.4230,
35.2166, -14.4150
), ncol = 2, byrow = TRUE)
rectangle_samama <- st_polygon(list(rectangle_coords_samama)) %>%
st_sfc(crs = 4326) %>%
st_transform(32736)
box_samama <- st_bbox(rectangle_samama)
rectangle_samama_proj <- st_as_sfc(box_samama)
# Plot the base map
samama_base_map <- ggplot() +
geom_sf(data = rectangle_samama_proj, fill = "cornsilk", color = "black") +
geom_sf(data = samama_sf_proj, color = "black", size = 1.8) +
annotation_scale(location = "bl", pad_x = unit(0.5, "in"), pad_y = unit(0.4, "in")) +
annotation_north_arrow(
location = "bl",
pad_x = unit(0.4, "in"), pad_y = unit(0.8, "in"),
style = north_arrow_fancy_orienteering
) +
theme_void()
# Save the base map
ggsave("Figures/Samama_map.png", plot = samama_base_map, width = 5, height = 5, dpi = 600)
# Print the base map
print(samama_base_map)
Samama (Mangochi District) lineages
Here, we plot lineages (where groups of isolates are from the same ST and popPUNK cluster) in Mangochi District. Selected lineages must be represented in at least 3 households in either 2023 or 2024.
# Prepare the data
samama_clusters <- interhousehold_lineages %>%
filter(district == "Mangochi")
samama_clusters <- samama_clusters %>%
left_join(
households %>% select(household, GPS_S, GPS_E),
by = "household"
)
# Convert Samama household coordinates to sf object and project
samama_clusters_sf <- samama_clusters %>%
st_as_sf(coords = c("GPS_E", "GPS_S"), crs = 4326)
samama_clusters_sf_proj <- st_transform(samama_clusters_sf, 32736)
# Plot the clusters
# Set custom colors for year
year_colors <- c(
"2023" = "blue",
"2024" = "red",
"Both years" = "green3"
)
# Define the clusters to include
clusters_to_plot <- c("ST10_popPUNK5", "ST38_popPUNK20", "ST44_popPUNK223", "ST58_popPUNK11", "ST131_popPUNK4", "ST131_popPUNK25", "ST1015_popPUNK11", "ST2852_popPUNK18", "ST3580_popPUNK18", "ST5614_popPUNK251", "ST8025_popPUNK5", "ST13823_popPUNK1407")
# Create individual plots
cluster_plots <- map(clusters_to_plot, function(clust) {
cluster_data <- samama_clusters_sf_proj %>%
filter(cluster == clust)
ggplot() +
geom_sf(data = rectangle_samama_proj, fill = "cornsilk", color = "black") +
geom_sf(data = samama_sf_proj, color = "black", size = 1.5) +
geom_sf(data = cluster_data, aes(color = year), size = 4.0,
show.legend = clust == "ST5614_popPUNK251") + # Show legend only for this cluster
scale_color_manual(
values = year_colors,
breaks = names(year_colors),
name = "Household colonisation year"
) +
labs(title = paste(clust)) +
theme_void() +
theme(
plot.title = element_text(hjust = 0.5, size = 14),
legend.position = "bottom",
legend.text = element_text(size = 14),
legend.title = element_text(size = 14)
) +
guides(color = guide_legend(override.aes = list(size = 4)))
})
# Combine plots into one panel with a single shared legend
samama_panel_plot <- wrap_plots(cluster_plots, ncol = 4, nrow = 3) +
plot_layout(guides = "collect") &
theme(legend.position = "bottom")
# Save the panel
ggsave("Figures/Samama_lineage_panel_map.png", plot = samama_panel_plot, width = 12, height = 12, dpi = 600)
print(samama_panel_plot)
Mthawira (Nsanje District)
## Create the base map
# Convert Mthawira household coordinates to sf object and project
mthawira_sf <- households %>%
filter(district == "Nsanje") %>%
st_as_sf(coords = c("GPS_E", "GPS_S"), crs = 4326)
mthawira_sf_proj <- st_transform(mthawira_sf, 32736)
# Define and project bounding box
rectangle_coords_mthawira <- matrix(c(
35.2850, -16.8440,
35.3020, -16.8440,
35.3020, -16.8580,
35.2850, -16.8580,
35.2850, -16.8440
), ncol = 2, byrow = TRUE)
rectangle_mthawira <- st_polygon(list(rectangle_coords_mthawira)) %>%
st_sfc(crs = 4326) %>%
st_transform(32736)
box_mthawira <- st_bbox(rectangle_mthawira)
rectangle_mthawira_proj <- st_as_sfc(box_mthawira)
# Plot the base map
mthawira_base_map <- ggplot() +
geom_sf(data = rectangle_mthawira_proj, fill = "cornsilk", color = "black") +
geom_sf(data = mthawira_sf_proj, color = "black", size = 1.8) +
annotation_scale(location = "bl", pad_x = unit(0.5, "in"), pad_y = unit(0.4, "in")) +
annotation_north_arrow(
location = "bl",
pad_x = unit(0.4, "in"), pad_y = unit(0.8, "in"),
style = north_arrow_fancy_orienteering
) +
theme_void()
# Save the base map
ggsave("Figures/Mthawira_map.png", plot = mthawira_base_map, width = 5, height = 5, dpi = 600)
# Print the base map
print(mthawira_base_map)
Mthawira (Nsanje District) lineages
Here, we plot lineages (where groups of isolates are from the same ST and popPUNK cluster) in Nsanje District. Selected lineages must be represented in at least 3 households in either 2023 or 2024.
# Prepare the data
mthawira_clusters <- interhousehold_lineages %>%
filter(district == "Nsanje")
mthawira_clusters <- mthawira_clusters %>%
left_join(
households %>% select(household, GPS_S, GPS_E),
by = "household"
)
# Convert Mthawira household coordinates to sf object and project
mthawira_clusters_sf <- mthawira_clusters %>%
st_as_sf(coords = c("GPS_E", "GPS_S"), crs = 4326)
mthawira_clusters_sf_proj <- st_transform(mthawira_clusters_sf, 32736)
# Plot the clusters
# Set custom colors for year
year_colors <- c(
"2023" = "blue",
"2024" = "red",
"Both years" = "green3"
)
# Define the clusters to include
clusters_to_plot <- c("ST10_popPUNK92", "ST206_popPUNK41_1328", "ST1015_popPUNK11")
# Create individual plots
cluster_plots <- map(clusters_to_plot, function(clust) {
cluster_data <- mthawira_clusters_sf_proj %>%
filter(cluster == clust)
ggplot() +
geom_sf(data = rectangle_mthawira_proj, fill = "cornsilk", color = "black") +
geom_sf(data = mthawira_sf_proj, color = "black", size = 1.5) +
geom_sf(data = cluster_data, aes(color = year), size = 4.0,
show.legend = clust == "ST206_popPUNK41_1328") + # Show legend only for this cluster
scale_color_manual(
values = year_colors,
breaks = names(year_colors),
name = "Household colonisation year"
) +
labs(title = paste(clust)) +
theme_void() +
theme(
plot.title = element_text(hjust = 0.5, size = 14),
legend.position = "bottom",
legend.text = element_text(size = 14),
legend.title = element_text(size = 14)
) +
guides(color = guide_legend(override.aes = list(size = 4)))
})
# Combine plots into one panel with a single shared legend
mthawira_panel_plot <- wrap_plots(cluster_plots, ncol = 4, nrow = 1) +
plot_layout(guides = "collect") &
theme(legend.position = "bottom")
# Save the panel
ggsave("Figures/Mthawira_lineage_panel_map.png", plot = mthawira_panel_plot, width = 12, height = 4, dpi = 600)
print(mthawira_panel_plot)
Strain-level analysis
Strain identification
# Read ANI matrix
ani <- read.csv("Data/ANI_matrix.csv", row.names = 1)
# Convert ANI to distance
ani_dist_matrix <- 1 - (as.matrix(ani) / 100)
# Convert to "dist" object
ani_dist_obj <- as.dist(ani_dist_matrix)
# Perform hierarchical clustering (average linkage)
hc <- hclust(ani_dist_obj, method = "average")
# Cut tree at ANI > 99.99% (distance < 0.0001)
clusters <- cutree(hc, h = 0.0001)
# Combine results
strain_groups <- split(names(clusters), clusters)
# Print strain groups
print(strain_groups)## $`1`
## [1] "cM10a"
##
## $`2`
## [1] "cM12a" "fM23b"
##
## $`3`
## [1] "cM19a" "cM7b"
##
## $`4`
## [1] "cM20a" "cM2a" "cM3a" "cM44b" "cS28a"
##
## $`5`
## [1] "cM34a" "cM42b" "cM8a" "cM9b" "fS158a" "fS159a"
##
## $`6`
## [1] "cM35a"
##
## $`7`
## [1] "cM41a" "cM42a"
##
## $`8`
## [1] "cM59a" "fM21a"
##
## $`9`
## [1] "cM59b" "cM5a"
##
## $`10`
## [1] "cS100a" "cS58a" "cS71a"
##
## $`11`
## [1] "cS103a"
##
## $`12`
## [1] "cS104a" "cS53a" "cS61a" "cS63a" "fS92a"
##
## $`13`
## [1] "cS107a" "cS115a"
##
## $`14`
## [1] "cS108a" "cS109a" "cS129a" "cS164a"
##
## $`15`
## [1] "cS10a" "cS11a"
##
## $`16`
## [1] "cS113a"
##
## $`17`
## [1] "cS116a"
##
## $`18`
## [1] "cS132a" "cS162a" "cS172a" "cS178a" "cS51a" "cS83a"
##
## $`19`
## [1] "cS142a" "fS146a"
##
## $`20`
## [1] "cS153a"
##
## $`21`
## [1] "cS157a"
##
## $`22`
## [1] "cS15a"
##
## $`23`
## [1] "cS165a"
##
## $`24`
## [1] "cS168a" "cS80a" "cS91a"
##
## $`25`
## [1] "cS184a"
##
## $`26`
## [1] "cS27b" "cS30a" "cS33a" "fM35a"
##
## $`27`
## [1] "cS35a"
##
## $`28`
## [1] "cS37a"
##
## $`29`
## [1] "cS39c" "cS65a" "cS68a" "cS76a"
##
## $`30`
## [1] "cS45a"
##
## $`31`
## [1] "cS46a"
##
## $`32`
## [1] "cS4a"
##
## $`33`
## [1] "cS60a" "cS66a"
##
## $`34`
## [1] "cS6a"
##
## $`35`
## [1] "cS71b"
##
## $`36`
## [1] "cS72c"
##
## $`37`
## [1] "cS86a"
##
## $`38`
## [1] "cS86b"
##
## $`39`
## [1] "cS87a" "cS88a"
##
## $`40`
## [1] "cS8b"
##
## $`41`
## [1] "cS97a"
##
## $`42`
## [1] "cS99a"
##
## $`43`
## [1] "fM10a"
##
## $`44`
## [1] "fM16a"
##
## $`45`
## [1] "fM19a" "fM48a" "fM51a"
##
## $`46`
## [1] "fM24a"
##
## $`47`
## [1] "fM27b" "fM60a"
##
## $`48`
## [1] "fM29a"
##
## $`49`
## [1] "fM30a"
##
## $`50`
## [1] "fM31a"
##
## $`51`
## [1] "fM40a" "fM41b"
##
## $`52`
## [1] "fM54a"
##
## $`53`
## [1] "fM59a"
##
## $`54`
## [1] "fS100a" "fS14a"
##
## $`55`
## [1] "fS101a"
##
## $`56`
## [1] "fS102a"
##
## $`57`
## [1] "fS10a" "fS11a" "fS12a" "fS13a" "fS37a" "fS39a" "fS41a" "fS46a" "fS62a"
## [10] "fS63b" "fS64a" "fS74a" "fS93a" "fS97a" "fS99a"
##
## $`58`
## [1] "fS111a"
##
## $`59`
## [1] "fS117a"
##
## $`60`
## [1] "fS121a"
##
## $`61`
## [1] "fS122a"
##
## $`62`
## [1] "fS128a"
##
## $`63`
## [1] "fS133b"
##
## $`64`
## [1] "fS139a" "fS149a" "fS6a"
##
## $`65`
## [1] "fS143a"
##
## $`66`
## [1] "fS144a"
##
## $`67`
## [1] "fS148a"
##
## $`68`
## [1] "fS155a"
##
## $`69`
## [1] "fS157a"
##
## $`70`
## [1] "fS165a" "fS170a" "fS171a" "fS58a"
##
## $`71`
## [1] "fS174a" "fS177a"
##
## $`72`
## [1] "fS190a"
##
## $`73`
## [1] "fS191a" "fS50a" "fS54a" "fS72b" "fS73a" "fS79a" "fS81a"
##
## $`74`
## [1] "fS24a"
##
## $`75`
## [1] "fS25a" "fS80a" "fS95a"
##
## $`76`
## [1] "fS34a"
##
## $`77`
## [1] "fS35a"
##
## $`78`
## [1] "fS42a"
##
## $`79`
## [1] "fS4a"
##
## $`80`
## [1] "fS56a" "fS84a"
##
## $`81`
## [1] "fS70a"
##
## $`82`
## [1] "fS83a"
##
## $`83`
## [1] "fS85a" "fS86a"# Generate a dendrogram of ANI distances
# Open SVG device
svg("Figures/ANI_dendrogram.svg", width = 8, height = 4.5)
# Export dendrogram of ANI hierarchical clustering
plot(hc, main = "ANI Hierarchical Clustering Dendrogram",
xlab = "Isolate", ylab = "Distance (1 - (ANI/100))", cex = 0.5)
# Close device
dev.off()## png
## 2# Print the plot
plot(hc, main = "ANI Hierarchical Clustering Dendrogram",
xlab = "Isolate", ylab = "Distance (1 - (ANI/100))", cex = 0.5)
Identification of medoid isolates for long-read sequencing
# Identify medoid isolate from each strain represented in 3+ households
# Define strain IDs of interest
target_strains <- c(4, 5, 10, 12, 14, 18, 24, 26, 29, 45, 57, 64, 70, 73, 75)
# Create empty results data frame
medoids <- data.frame(strain = integer(),
n_isolates = integer(),
medoid_isolate = character(),
mean_distance = numeric(),
stringsAsFactors = FALSE)
for (strain_id in target_strains) {
# Get isolates belonging to this strain
members <- names(clusters[clusters == strain_id])
if (length(members) < 2) next # skip if only one isolate
# Subset distance matrix for this strain
submat <- ani_dist_matrix[members, members, drop = FALSE]
# Compute average distance to all other members
mean_dists <- rowMeans(submat, na.rm = TRUE)
# Identify isolate with smallest mean distance (the medoid)
medoid <- names(which.min(mean_dists))
# Store result
medoids <- rbind(medoids, data.frame(
strain = strain_id,
n_isolates = length(members),
medoid_isolate = medoid,
mean_distance = mean_dists[medoid]
))
}
# Print medoid isolates per target strain
print(medoids)## strain n_isolates medoid_isolate mean_distance
## cM2a 4 5 cM2a 4.676800e-05
## cM8a 5 6 cM8a 3.386333e-05
## cS100a 10 3 cS100a 2.077667e-05
## cS53a 12 5 cS53a 4.184000e-05
## cS108a 14 4 cS108a 1.072000e-05
## cS51a 18 6 cS51a 3.701500e-05
## cS91a 24 3 cS91a 1.836333e-05
## cS33a 26 4 cS33a 3.172000e-05
## cS76a 29 4 cS76a 2.731000e-05
## fM51a 45 3 fM51a 2.507667e-05
## fS10a 57 15 fS10a 3.073600e-05
## fS149a 64 3 fS149a 4.928667e-05
## fS58a 70 4 fS58a 3.223500e-05
## fS54a 73 7 fS54a 4.114429e-05
## fS25a 75 3 fS25a 4.437667e-05Chromosomal SNP distances within strains (hybrid assembly of above medoid isolate used as reference for each strain )
# Import within-strain genomic distance data
strain_SNPs <- read.csv("Data/Strain_distance.csv", header = TRUE)
# Order strains by maximum pairwise SNPs within each Phylogroup
strain_SNPs$Strain <- factor(
strain_SNPs$Strain,
levels = strain_SNPs %>%
arrange(Phylogroup, desc(Max_pairwise_SNPs_in_strain), Strain) %>%
pull(Strain) %>%
unique()
)
# Subset cross-district strains
cross_data <- subset(strain_SNPs, Cross_district_strain == "Yes")
# Dummy data for phylogroup legend
phylo_legend <- data.frame(
Phylogroup = unique(strain_SNPs$Phylogroup)
)
# Define the number of isolates in each strain
isolate_counts <- data.frame(
Strain = c("i) ST206", "ii) ST10", "vi) ST10", "xii) ST131", "vii) ST3580",
"xiv) ST38", "xiii) ST131", "viii) ST1015", "iv) ST44", "xi) ST1015",
"xv) ST38", "iii) ST8025", "x) ST2852", "ix) ST58", "v) ST13823"),
Isolates_in_strain = c(5, 6, 3, 5, 4, 6, 3, 4, 4, 3, 15, 3, 4, 7, 3)
)
# Define the number of households each strain was found in
household_counts <- data.frame(
Strain = c("i) ST206", "ii) ST10", "vi) ST10", "xii) ST131", "vii) ST3580",
"xiv) ST38", "xiii) ST131", "viii) ST1015", "iv) ST44", "xi) ST1015",
"xv) ST38", "iii) ST8025", "x) ST2852", "ix) ST58", "v) ST13823"),
Households = c(5, 4, 3, 5, 3, 6, 3, 4, 4, 3, 13, 3, 3, 7, 3)
)
# Define color for multi-district annotation
arrowe_color <- "#E57373"
# Ensure same factor order
isolate_counts$Strain <- factor(isolate_counts$Strain, levels = levels(strain_SNPs$Strain))
# Plot
Strain_SNP_distance_boxplot <- ggplot(strain_SNPs, aes(x = Strain, y = SNP_distance, fill = Phylogroup)) +
geom_boxplot(width = 0.6, linewidth = 0.3, outlier.size = 0.6, show.legend = FALSE) +
ylim(0, 49) + # extended y-limit to make room for circles
geom_point(data = cross_data, aes(x = Strain, y = 42),
shape = 25, fill = arrowe_color, color = arrowe_color, size = 3, show.legend = FALSE) +
geom_point(data = household_counts, aes(x = Strain, y = 46, size = Households),
shape = 21, fill = "steelblue", color = "black", show.legend = TRUE) +
scale_size_continuous(
name = "No. households",
range = c(2, 8),
breaks = c(3, 13)
) +
geom_point(aes(x = NA, y = NA, shape = "Yes"),
fill = arrowe_color, color = arrowe_color, size = 3) +
scale_shape_manual(name = "Cross-district strain", values = c("Yes" = 25)) +
geom_point(data = phylo_legend, aes(x = NA, y = NA, fill = Phylogroup),
shape = 22, size = 5, color = "black", inherit.aes = FALSE) +
scale_fill_brewer(palette = "Set3", name = "Phylogroup") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
labs(x = "Strain", y = "Genomic distance (no. chromosomal SNPs)")
# Save the plot
ggsave("Figures/Strain_SNP_distance_boxplot.png", Strain_SNP_distance_boxplot, height = 4, width = 10, dpi = 600)## Warning in geom_point(aes(x = NA, y = NA, shape = "Yes"), fill = arrowe_color, : All aesthetics have length 1, but the data has 215 rows.
## â„ą Please consider using `annotate()` or provide this layer with data containing
## a single row.## Warning: Removed 215 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 4 rows containing missing values or values outside the scale range
## (`geom_point()`).# Print the plot
print(Strain_SNP_distance_boxplot)## Warning in geom_point(aes(x = NA, y = NA, shape = "Yes"), fill = arrowe_color, : All aesthetics have length 1, but the data has 215 rows.
## â„ą Please consider using `annotate()` or provide this layer with data containing
## a single row.## Warning: Removed 215 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 4 rows containing missing values or values outside the scale range
## (`geom_point()`).
Strain networks
# Build isolate -> Strain mapping from pairwise table
strain_map <- strain_SNPs %>%
select(Isolate = Isolate1, Strain) %>%
bind_rows(
strain_SNPs %>% select(Isolate = Isolate2, Strain)
) %>%
distinct()
# Keep only the required columns for edges
edges <- strain_SNPs %>%
select(Isolate1, Isolate2, SNP_distance)
# Categorise SNP distances for edge construction
edges <- edges %>%
mutate(
category = case_when(
SNP_distance <= 10 ~ "≤10",
SNP_distance <= 25 ~ "11–25",
SNP_distance > 25 ~ "26-50"
),
category = factor(category,
levels = c("≤10", "11–25", "26-50"),
ordered = TRUE)
)
# Prepare node metadata and attach Strain
all_isolates <- unique(c(strain_SNPs$Isolate1, strain_SNPs$Isolate2))
nodes <- isolate_data_human %>%
filter(Isolate %in% all_isolates) %>%
select(Isolate, District, Year, Participant.ID) %>%
left_join(strain_map, by = "Isolate")
# Create igraph object
g <- graph_from_data_frame(d = edges, vertices = nodes, directed = FALSE)
# Set seed for reproducible plotting and compute layout coordinate
set.seed(42)
lay <- create_layout(g, layout = "fr")
# Prepare dataframe for label positions just outside each Strain cluster
lay_df <- as.data.frame(lay) %>% filter(!is.na(Strain))
# Compute centroid per strain
centroids <- lay_df %>%
group_by(Strain) %>%
summarise(cx = mean(x), cy = mean(y), .groups = "drop")
# Compute cluster radius (max distance from centroid) per strain
spread_df <- lay_df %>%
left_join(centroids, by = "Strain") %>%
group_by(Strain, cx, cy) %>%
summarise(radius = max(sqrt((x - cx)^2 + (y - cy)^2)), .groups = "drop")
# Join and compute label coordinates (1.8 * radius above cluster)
strain_labels <- spread_df %>%
mutate(
label_x = cx - radius * 0,
label_y = cy + radius * 1.8
)
# Final plot with labels placed above clusters
network_plot <- ggraph(lay) +
geom_edge_link(aes(colour = category), width = 0.7, alpha = 0.7) +
geom_node_point(aes(fill = District, colour = Year),
shape = 21, size = 2.5, stroke = 1.0) +
geom_text(
data = strain_labels,
aes(x = label_x, y = label_y, label = Strain),
inherit.aes = FALSE,
size = 3
) +
scale_edge_color_manual(
values = c("≤10" = "red",
"11–25" = "orange",
"26-50" = "yellow2"),
breaks = c("≤10", "11–25", "26-50")
) +
scale_fill_manual(
values = c("Mangochi" = "dodgerblue3", "Nsanje" = "lightblue")
) +
scale_colour_manual(
values = c("2023" = "black", "2024" = "pink3")
) +
theme_void() +
labs(edge_color = "No. chromosomal SNPs", fill = "District")
# Save the plot
ggsave("Figures/Strain_network_plot.png", network_plot, height = 4, width = 5.5, dpi = 600)
# Print the plot
print(network_plot)
Genomic vs geographic distance within villages
# Import within-strain genomic and geographic distance data (for comparisons within same village at the same sampling point)
strain_distance <- read.csv("Data/Strain_distance_within_village.csv", header = TRUE)
# Order ST levels by Phylogroup
strain_distance$ST <- factor(
strain_distance$ST,
levels = strain_distance %>%
dplyr::arrange(Phylogroup, ST) %>%
dplyr::pull(ST) %>%
unique()
)
# Remove connections between isolates from the same household (n=6)
strain_distance <- strain_distance %>%
dplyr::filter(Household1 != Household2)
# Print summary of SNP distances
summary(strain_distance$SNP_distance)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 1.000 2.000 3.895 3.000 38.000# Add geographic distance groupings
strain_distance <- strain_distance %>%
mutate(dist_group = case_when(
distance_m < 200 ~ "<200",
distance_m >= 200 & distance_m <= 400 ~ "200 - 400",
distance_m > 400 ~ ">400"
))
strain_distance <- strain_distance %>%
mutate(dist_group = factor(dist_group, levels = c("<200", "200 - 400", ">400")))
# Plot geographic distance vs SNP distance (jittering applied to x axis)
SNP_distance_scatter <- ggplot(strain_distance, aes(x = distance_m, y = SNP_distance, fill = Phylogroup)) +
geom_jitter(height = 0, width = 5, seed = 125, alpha = 0.65, size = 1.8, shape = 21, colour = "black", stroke = 0.15) +
scale_fill_brewer(palette = "Set3") +
scale_x_continuous(name = "Distance between households (metres)") +
scale_y_continuous(name = "Genomic distance (no. chromosomal SNPs)", limits = c(0, 50)) +
theme_grey()## Warning in geom_jitter(height = 0, width = 5, seed = 125, alpha = 0.65, :
## Ignoring unknown parameters: `seed`# Save the plot
ggsave("Figures/SNP_distance_scatter.png", SNP_distance_scatter, height = 4, width = 5, dpi = 600)
# Print the plot
print(SNP_distance_scatter)
# Check for statistical difference in SNP distance between geographic distance groups
kruskal.test(SNP_distance ~ dist_group, data = strain_distance)##
## Kruskal-Wallis rank sum test
##
## data: SNP_distance by dist_group
## Kruskal-Wallis chi-squared = 13.117, df = 2, p-value = 0.001418# Perform post-hoc Dunn's Test with Bonferroni correction for p-values
dunnTest(SNP_distance ~ dist_group, data = strain_distance, method = "bonferroni")## Dunn (1964) Kruskal-Wallis multiple comparison## p-values adjusted with the Bonferroni method.## Comparison Z P.unadj P.adj
## 1 <200 - >400 -3.6082383 0.0003082833 0.000924850
## 2 <200 - 200 - 400 -0.8330581 0.4048119620 1.000000000
## 3 >400 - 200 - 400 3.0214407 0.0025157493 0.007547248# Manually create a table from Dunn's test output
pvals <- data.frame(
group1 = c("<200", "<200", "200 - 400"),
group2 = c(">400", "200 - 400", ">400"),
p.adj = c(0.000407142, 0.677055724, 0.009048447),
y.position = c(48, 42, 45)
)
pvals$signif <- cut(
pvals$p.adj,
breaks = c(-Inf, 0.001, 0.01, 0.05, Inf),
labels = c("***", "**", "*", "ns")
)
# Plot a violin plot with an overlayed boxplot
SNP_distance_violin <- ggplot(strain_distance, aes(x = dist_group, y = SNP_distance, fill = dist_group)) +
geom_violin(alpha = 0.6, color = "black", linewidth = 0.2) +
ylim(0, 50) +
scale_fill_brewer(palette = "Set4") +
labs(x = "Distance between households (metres)", y = "Genomic distance (no. chromosomal SNPs)") +
theme_minimal() +
theme(legend.position = "none") +
stat_pvalue_manual(
pvals,
label = "signif", # shows *, **, ***
tip.length = 0.02
)## Warning: Unknown palette: "Set4"# Save the plot
ggsave("Figures/SNP_distance_violin.png", SNP_distance_violin, height = 4, width = 3.5, dpi = 600)
# Print the plot
print(SNP_distance_violin)
Interhousehold strain maps
# Prepare the data (make household variable for isolates from strain strains found in 3+ households)
nodes$household <- sub("\\.[0-9]+$", "", nodes$Participant.ID)Samama (Mangochi District)
# Prepare the data
samama_strains <- nodes %>%
filter(District == "Mangochi")
samama_strains <- samama_strains %>%
left_join(
households %>% select(household, GPS_S, GPS_E),
by = "household"
)
# Convert Samama household coordinates to sf object and project
samama_strains_sf <- samama_strains %>%
st_as_sf(coords = c("GPS_E", "GPS_S"), crs = 4326)
samama_strains_sf_proj <- st_transform(samama_strains_sf, 32736)
# Set custom colors for year
year_colors <- c(
"2023" = "blue",
"2024" = "red",
"Both years" = "green3"
)
# Define the clusters to include
clusters_to_plot <- c("i) ST206", "ii) ST10", "iii) ST8025", "iv) ST44", "v) ST13823",
"vi) ST10", "vii) ST3580", "viii) ST1015", "ix) ST58", "x) ST2852",
"xii) ST131", "xiii) ST131", "xiv) ST38", "xv) ST38")
# Create individual plots
strain_cluster_plots <- map(clusters_to_plot, function(clust) {
cluster_data <- samama_strains_sf_proj %>%
filter(Strain == clust)
ggplot() +
geom_sf(data = rectangle_samama_proj, fill = "cornsilk", color = "black") +
geom_sf(data = samama_sf_proj, color = "black", size = 1.5) +
geom_sf(data = cluster_data, aes(color = Year), size = 4.0,
show.legend = clust == "xii) ST131") + # Show legend only for this cluster
scale_color_manual(
values = year_colors,
breaks = names(year_colors),
name = "Household colonisation year"
) +
labs(title = paste(clust)) +
theme_void() +
theme(
plot.title = element_text(hjust = 0.5, size = 18),
legend.position = "bottom",
legend.text = element_text(size = 22),
legend.title = element_text(size = 22)
) +
guides(color = guide_legend(override.aes = list(size = 4)))
})
# Combine plots into one panel with a single shared legend
samama_strain_panel_plot <- wrap_plots(strain_cluster_plots, ncol = 5, nrow = 3) +
plot_layout(guides = "collect") &
theme(legend.position = "bottom")
# Save the panel
ggsave("Figures/Samama_strain_cluster_panel_map.png", plot = samama_strain_panel_plot, width = 15, height = 12, dpi = 600)
print(samama_strain_panel_plot)
Mthawira (Nsanje District)
# Prepare the data
mthawira_strains <- nodes %>%
filter(District == "Nsanje")
mthawira_strains <- mthawira_strains %>%
left_join(
households %>% select(household, GPS_S, GPS_E),
by = "household"
)
# Convert mthawira household coordinates to sf object and project
mthawira_strains_sf <- mthawira_strains %>%
st_as_sf(coords = c("GPS_E", "GPS_S"), crs = 4326)
mthawira_strains_sf_proj <- st_transform(mthawira_strains_sf, 32736)
# Set custom colors for year
year_colors <- c(
"2023" = "blue",
"2024" = "red",
"Both years" = "green3"
)
# Define the clusters to include
clusters_to_plot <- c("i) ST206", "ii) ST10", "viii) ST1015", "xi) ST1015")
# Create individual plots
strain_cluster_plots <- map(clusters_to_plot, function(clust) {
cluster_data <- mthawira_strains_sf_proj %>%
filter(Strain == clust)
ggplot() +
geom_sf(data = rectangle_mthawira_proj, fill = "cornsilk", color = "black") +
geom_sf(data = mthawira_sf_proj, color = "black", size = 1.5) +
geom_sf(data = cluster_data, aes(color = Year), size = 4.0,
show.legend = FALSE) + # Show legend only for this cluster) +
scale_color_manual(
values = year_colors,
breaks = names(year_colors),
name = "Household colonisation year"
) +
labs(title = paste(clust)) +
theme_void() +
theme(
plot.title = element_text(hjust = 0.5, size = 18),
legend.position = "bottom",
legend.text = element_text(size = 22),
legend.title = element_text(size = 22)
) +
guides(color = guide_legend(override.aes = list(size = 4)))
})
# Combine plots into one panel with a single shared legend
mthawira_strain_panel_plot <- wrap_plots(strain_cluster_plots, ncol = 5, nrow = 1) +
plot_layout(guides = "collect") &
theme(legend.position = "bottom")
# Save the panel
ggsave("Figures/Mthawira_strain_cluster_panel_map.png", plot = mthawira_strain_panel_plot, width = 15, height = 4, dpi = 600)
print(mthawira_strain_panel_plot)
ST-specific networks and phylogeny in Malawian context
ST131
# Import ST131 isolate metadata, including isolates from previous studies
ST131_metadata <- read.csv("data/ST131_genomes.csv", header = TRUE, row.names = 1)
# Define the core genome alignment size (columns containing only As, Ts, Cs and Gs)
ST131_cg_size <- 3954219
ST131_recomb_masked_cg_size <- 2834919Recombination masked core genome SNPs
# Load ST131 SNP distance matrix masking recombination sites
ST131_snp_matrix_recomb_masked <- read.csv("data/ST131_malawi_SNP_dists_recombination_masked.csv", header = TRUE, row.names = 1)
# Convert to distance object and cluster
ST131_snp_dist_recomb_masked <- as.dist(ST131_snp_matrix_recomb_masked)
ST131_hc_recomb_masked <- hclust(ST131_snp_dist_recomb_masked, method = "average")
# Identify SNP thresholds representing strict, moderate and loose transmission networks adjusting for recombination-masking
ST131_strict <- ST131_recomb_masked_cg_size*0.000002
ST131_moderate <- ST131_recomb_masked_cg_size*0.000005
ST131_loose <- ST131_recomb_masked_cg_size*0.00002
print(c(ST131_strict, ST131_moderate, ST131_loose))## [1] 5.669838 14.174595 56.698380# Plot a dendrogram of SNP distances that masks recombination
# Open SVG device
svg("Figures/ST131_dendrogram.svg", width = 8, height = 4.5)
# Convert hclust to dendrogram
ST131_dend_recomb_masked <- as.dendrogram(ST131_hc_recomb_masked)
# Generate the dendrogram
plot(ST131_dend_recomb_masked,
main = "ST131",
xlab = "",
ylab = "SNP distance",
cex = 1, # leaf labels
cex.main = 1, # title
cex.lab = 0.8, # axis labels
cex.axis = 0.8, # tick labels
ylim = c(0, 400))
# Close device
dev.off()## png
## 2# Print the dendrogram
plot(ST131_dend_recomb_masked,
main = "ST131",
xlab = "",
ylab = "SNP distance",
cex = 1, # leaf labels
cex.main = 1, # title
cex.lab = 0.8, # axis labels
cex.axis = 0.8, # tick labels
ylim = c(0, 400))
# Plot an annotated SNP distance heatmap using the above clustering
# Convert SNP matrix to a matrix object for plotting
mat <- as.matrix(ST131_snp_matrix_recomb_masked)
# Typography for annotations and legends
# Legend / annotation TITLE text (bold)
title_gp <- gpar(
fontsize = 10,
fontface = "bold",
fontfamily = "sans"
)
# Legend / annotation LABEL text (normal)
label_gp <- gpar(
fontsize = 9,
fontface = "plain",
fontfamily = "sans"
)
# Prepare the metadata
annotation <- as.data.frame(ST131_metadata)
annotation <- annotation[, c("Donor", "Location", "Study", "Year"), drop = FALSE]
annotation$Year <- as.numeric(annotation$Year)
# Match metadata and matrix orders
annotation <- annotation[colnames(mat), , drop = FALSE]
# Define heatmap colour mappings
snp_color <- colorRamp2(
c(0, 400),
c("white", "red")
)
# Generate the range for continuous year annotation
year_color <- colorRamp2(
range(annotation$Year, na.rm = TRUE),
c("lightblue", "darkblue")
)
ha_col <- HeatmapAnnotation(
df = annotation,
col = list(
Location = c(
"Blantyre (urban)" = "springgreen",
"Mangochi District (rural)" = "springgreen3",
"Nsanje District (rural)" = "green4"
),
Donor = c(
"Hospital inpatient" = "tan4",
"Recent hospital discharge" = "tan3",
"Community" = "tan1"
),
Study = c(
"This study" = "gray80",
"Lewis et al." = "gray50",
"Musicha et al." = "gray20"
),
Year = year_color
),
annotation_name_gp = title_gp,
annotation_legend_param = list(
Location = list(title_gp = title_gp, labels_gp = label_gp),
Donor = list(title_gp = title_gp, labels_gp = label_gp),
Study = list(title_gp = title_gp, labels_gp = label_gp),
Year = list(title_gp = title_gp, labels_gp = label_gp)
),
gp = gpar(col = "grey30", lwd = 0.3)
)
# Generate the annotated heatmap
ht <- Heatmap(
mat,
name = "SNP distance",
col = snp_color,
rect_gp = gpar(col = "grey30", lwd = 0.3),
cluster_rows = ST131_hc_recomb_masked,
cluster_columns = ST131_hc_recomb_masked,
top_annotation = ha_col,
show_row_dend = FALSE,
show_column_dend = FALSE,
show_row_names = FALSE,
show_column_names = FALSE,
row_names_gp = gpar(fontsize = 4.5),
column_names_gp = gpar(fontsize = 4.5),
heatmap_legend_param = list(
title = "SNPs",
title_gp = title_gp,
labels_gp = label_gp,
at = c(0, 100, 200, 300, 400)
)
)
# Save the plot
png(
filename = "Figures/ST131_recombination_masked_SNP_heatmap.png",
width = 8,
height = 6,
units = "in",
res = 600
)
draw(ht, merge_legend = TRUE)
dev.off()## png
## 2# Print the plot
draw(ht, merge_legend = TRUE)
Recombination masked networks
# Convert matrix to long format
long_snp <- ST131_snp_matrix_recomb_masked %>%
rownames_to_column(var = "Isolate1") %>%
pivot_longer(-Isolate1, names_to = "Isolate2", values_to = "SNP_distance")
# Keep only the required columns for edges
edges <- long_snp %>%
select(Isolate1, Isolate2, SNP_distance)
# Categorise SNP distances for edge construction
edges <- edges %>%
mutate(
category = case_when(
SNP_distance <= ST131_strict ~ "<0.0002% (10 SNPs per 5 Mb)",
SNP_distance <= ST131_moderate ~ "<0.0005% (25 SNPs per 5 Mb)",
SNP_distance <= ST131_loose ~ "<0.002% (100 SNPs per 5 Mb)"
),
category = factor(category,
levels = c("<0.0002% (10 SNPs per 5 Mb)",
"<0.0005% (25 SNPs per 5 Mb)",
"<0.002% (100 SNPs per 5 Mb)"),
ordered = TRUE)
) %>%
filter(!is.na(category))
# Add weighting to distribute nodes
edges <- edges %>%
mutate(weight = 1 / (SNP_distance + 1))
# Get node data
nodes <- ST131_metadata %>%
mutate(
# Combine Study and isolation year with space + brackets
`Study (isolation year)` = paste0(Study, " (", Year, ")")
) %>%
rownames_to_column(var = "Isolate")
# Create igraph object
g <- graph_from_data_frame(d = edges, vertices = nodes, directed = FALSE)
# Set seed for reproducible plotting and compute layout coordinate
set.seed(42)
lay <- create_layout(g, layout = "fr")
# Plot networks
network_plot <- ggraph(lay) +
geom_edge_link(aes(colour = category), width = 0.7, alpha = 0.7) +
geom_node_point(aes(fill = `Study (isolation year)`, colour = Location),
shape = 21, size = 2.5, stroke = 1.0) +
scale_edge_color_manual(
values = c("<0.0002% (10 SNPs per 5 Mb)" = "red",
"<0.0005% (25 SNPs per 5 Mb)" = "orange",
"<0.002% (100 SNPs per 5 Mb)" = "yellow2"),
breaks = c("<0.0002% (10 SNPs per 5 Mb)",
"<0.0005% (25 SNPs per 5 Mb)",
"<0.002% (100 SNPs per 5 Mb)"
)
) +
scale_colour_manual(
values = c("Blantyre (urban)" = "red",
"Mangochi District (rural)" = "springgreen",
"Nsanje District (rural)" = "green4"
)
) +
scale_fill_manual(
values = c('Lewis et al. (2017)' = "grey95",
'Lewis et al. (2018)' = "grey30",
'Musicha et al. (2019)' = "pink",
'Musicha et al. (2020)' = "orange2",
'This study (2023)' = "skyblue1",
'This study (2024)' = "blue1"
)
) +
theme_void() +
labs(edge_color = "SNPs as % of core genome alignment", color = "Location", fill = "Study (isolation year)")
# Save the plot
ggsave("Figures/ST131_network_plot.png", network_plot, height = 4, width = 7, dpi = 600)
# Print the plot
print(network_plot)
Networks without masking recombination
# Load ST38 SNP distance matrix including recombination sites
ST131_snp_matrix <- read.csv("data/ST131_malawi_SNP_dists.csv", header = TRUE, row.names = 1)
# Identify SNP thresholds representing strict, moderate and loose transmission networks including recombination sites
ST131_strict_with_recomb <- ST131_cg_size*0.000002
ST131_moderate_with_recomb <- ST131_cg_size*0.000005
ST131_loose_with_recomb <- ST131_cg_size*0.00002
print(c(ST131_strict_with_recomb, ST131_moderate_with_recomb, ST131_loose_with_recomb))## [1] 7.908438 19.771095 79.084380# Convert matrix to long format
long_snp <- ST131_snp_matrix %>%
rownames_to_column(var = "Isolate1") %>%
pivot_longer(-Isolate1, names_to = "Isolate2", values_to = "SNP_distance")
# Keep only the required columns for edges
edges <- long_snp %>%
select(Isolate1, Isolate2, SNP_distance)
# Categorise SNP distances for edge construction
edges <- edges %>%
mutate(
category = case_when(
SNP_distance <= ST131_strict_with_recomb ~ "<0.0002% (10 SNPs per 5 Mb)",
SNP_distance <= ST131_moderate_with_recomb ~ "<0.0005% (25 SNPs per 5 Mb)",
SNP_distance <= ST131_loose_with_recomb ~ "<0.002% (100 SNPs per 5 Mb)"
),
category = factor(category,
levels = c("<0.0002% (10 SNPs per 5 Mb)",
"<0.0005% (25 SNPs per 5 Mb)",
"<0.002% (100 SNPs per 5 Mb)"),
ordered = TRUE)
) %>%
filter(!is.na(category))
# Add weighting to distribute nodes
edges <- edges %>%
mutate(weight = 1 / (SNP_distance + 1))
# Create igraph object
g <- graph_from_data_frame(d = edges, vertices = nodes, directed = FALSE)
# Set seed for reproducible plotting and compute layout coordinate
set.seed(42)
lay <- create_layout(g, layout = "fr")
# Plot networks
network_plot <- ggraph(lay) +
geom_edge_link(aes(colour = category), width = 0.7, alpha = 0.7) +
geom_node_point(aes(fill = `Study (isolation year)`, colour = Location),
shape = 21, size = 2.5, stroke = 1.0) +
scale_edge_color_manual(
values = c("<0.0002% (10 SNPs per 5 Mb)" = "red",
"<0.0005% (25 SNPs per 5 Mb)" = "orange",
"<0.002% (100 SNPs per 5 Mb)" = "yellow2"),
breaks = c("<0.0002% (10 SNPs per 5 Mb)",
"<0.0005% (25 SNPs per 5 Mb)",
"<0.002% (100 SNPs per 5 Mb)"
)
) +
scale_colour_manual(
values = c("Blantyre (urban)" = "red",
"Mangochi District (rural)" = "springgreen",
"Nsanje District (rural)" = "green4"
)
) +
scale_fill_manual(
values = c('Lewis et al. (2017)' = "grey95",
'Lewis et al. (2018)' = "grey30",
'Musicha et al. (2019)' = "pink",
'Musicha et al. (2020)' = "orange2",
'This study (2023)' = "skyblue1",
'This study (2024)' = "blue1"
)
) +
theme_void() +
labs(edge_color = "SNPs as % of core genome alignment", color = "Location", fill = "Study (isolation year)")
# Save the plot
ggsave("Figures/ST131_network_plot_inc_recomb_sites.png", network_plot, height = 4, width = 7, dpi = 600)
# Print the plot
print(network_plot)
Tree
# Read in the treefile
ST131_tree <- read.tree("Data/ST131.aln.treefile")
# Root the tree at midpoint
ST131_tree <- midpoint(ST131_tree)
# Extract isolate metadata for tree
tree_metadata <- nodes[, c("Isolate", "Location", "Study (isolation year)")]
# Reorder metadata to match tree tip order
tree_metadata <- tree_metadata[match(ST131_tree$tip.label, tree_metadata$Isolate), ]
rownames(tree_metadata) <- tree_metadata$Isolate
# Colour palette
study_fill_vals <- c(
'Lewis et al. (2017)' = "grey95",
'Lewis et al. (2018)' = "grey30",
'Musicha et al. (2019)' = "pink",
'Musicha et al. (2020)' = "orange2",
'This study (2023)' = "skyblue1",
'This study (2024)' = "blue1"
)
# Rename Isolate -> label
tree_metadata_for_join <- tree_metadata %>%
rename(label = Isolate)
# Plot tree
p <- ggtree(ST131_tree) %<+% tree_metadata_for_join +
geom_tippoint(
aes(
fill = `Study (isolation year)`,
shape = Location
),
colour = "black",
stroke = 0.3,
size = 2.0
) +
scale_fill_manual(values = study_fill_vals, drop = FALSE) +
scale_shape_manual(
values = c(
"Blantyre (urban)" = 21,
"Mangochi District (rural)" = 22,
"Nsanje District (rural)" = 24
)
) +
theme_tree2() +
theme(legend.position = "right") +
labs(
fill = "Study (isolation year)",
shape = "Location"
) +
geom_treescale(
x = 0.00005, y = 115, width = 0.001,
fontsize = 3, linesize = 0.5
)
# Override shape in key so colours are plotted properly
p <- p +
guides(
fill = guide_legend(
override.aes = list(shape = 21, colour = "black")
)
)
# Clean x-axis
p <- p +
theme(
axis.line.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.x = element_blank()
)
# Add some border space to prevent plot clipping
p <- p + scale_y_continuous(expand = expansion(mult = c(0.03, 0.03)))## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.# Save the plot
ggsave("Figures/ST131_tree.png", p, width = 8, height = 8, dpi = 600)
# Print the plot
print(p)
ST38
# Import ST38 isolate metadata, including isolates from previous studies
ST38_metadata <- read.csv("data/ST38_genomes.csv", header = TRUE, row.names = 1)
# Define the core genome alignment size (columns containing only As, Ts, Cs and Gs)
ST38_cg_size <- 4279355
ST38_recomb_masked_cg_size <- 3472800Recombination masked core genome SNPs
# Load ST38 SNP distance matrix masking recombination sites
ST38_snp_matrix_recomb_masked <- read.csv("data/ST38_malawi_SNP_dists_recombination_masked.csv", header = TRUE, row.names = 1)
# Convert to distance object and cluster
ST38_snp_dist_recomb_masked <- as.dist(ST38_snp_matrix_recomb_masked)
ST38_hc_recomb_masked <- hclust(ST38_snp_dist_recomb_masked, method = "average")
# Identify SNP thresholds representing strict, moderate and loose transmission networks adjusting for recombination-masking
ST38_strict <- ST38_recomb_masked_cg_size*0.000002
ST38_moderate <- ST38_recomb_masked_cg_size*0.000005
ST38_loose <- ST38_recomb_masked_cg_size*0.00002
print(c(ST38_strict, ST38_moderate, ST38_loose))## [1] 6.9456 17.3640 69.4560# Plot a dendrogram of SNP distances that masks recombination
# Open SVG device
svg("Figures/ST38_dendrogram.svg", width = 8, height = 4.5)
# Convert hclust to dendrogram
ST38_dend_recomb_masked <- as.dendrogram(ST38_hc_recomb_masked)
# Generate the dendrogram
plot(ST38_dend_recomb_masked,
main = "ST38",
xlab = "",
ylab = "SNP distance",
cex = 1, # leaf labels
cex.main = 1, # title
cex.lab = 0.8, # axis labels
cex.axis = 0.8, # tick labels
ylim = c(0, 400))
# Close device
dev.off()## png
## 2# Plot the dendrogram
plot(ST38_dend_recomb_masked,
main = "ST38",
xlab = "",
ylab = "SNP distance",
cex = 1, # leaf labels
cex.main = 1, # title
cex.lab = 0.8, # axis labels
cex.axis = 0.8, # tick labels
ylim = c(0, 400))
# Plot an annotated SNP distance heatmap using the above clustering
# Convert SNP matrix to a matrix object for plotting
mat <- as.matrix(ST38_snp_matrix_recomb_masked)
# Typography for annotations and legends
# Legend / annotation TITLE text (bold)
title_gp <- gpar(
fontsize = 10,
fontface = "bold",
fontfamily = "sans"
)
# Legend / annotation LABEL text (normal)
label_gp <- gpar(
fontsize = 9,
fontface = "plain",
fontfamily = "sans"
)
# Prepare the metadata
annotation <- as.data.frame(ST38_metadata)
annotation <- annotation[, c("Donor", "Location", "Study", "Year"), drop = FALSE]
annotation$Year <- as.numeric(annotation$Year)
# Match metadata and matrix orders
annotation <- annotation[colnames(mat), , drop = FALSE]
# Define heatmap colour mappings
snp_color <- colorRamp2(
c(0, 400),
c("white", "red")
)
# Generate the range for continuous year annotation
year_color <- colorRamp2(
range(annotation$Year, na.rm = TRUE),
c("lightblue", "darkblue")
)
ha_col <- HeatmapAnnotation(
df = annotation,
col = list(
Location = c(
"Blantyre (urban)" = "springgreen",
"Mangochi District (rural)" = "springgreen3",
"Nsanje District (rural)" = "green4"
),
Donor = c(
"Hospital inpatient" = "tan4",
"Recent hospital discharge" = "tan3",
"Community" = "tan1"
),
Study = c(
"This study" = "gray80",
"Lewis et al." = "gray50",
"Musicha et al." = "gray20"
),
Year = year_color
),
annotation_name_gp = title_gp,
annotation_legend_param = list(
Location = list(title_gp = title_gp, labels_gp = label_gp),
Donor = list(title_gp = title_gp, labels_gp = label_gp),
Study = list(title_gp = title_gp, labels_gp = label_gp),
Year = list(title_gp = title_gp, labels_gp = label_gp)
),
gp = gpar(col = "grey30", lwd = 0.3)
)
# Generate the annotated heatmap
ht <- Heatmap(
mat,
name = "SNP distance",
col = snp_color,
rect_gp = gpar(col = "grey30", lwd = 0.3),
cluster_rows = ST38_hc_recomb_masked,
cluster_columns = ST38_hc_recomb_masked,
top_annotation = ha_col,
show_row_dend = FALSE,
show_column_dend = FALSE,
show_row_names = FALSE,
show_column_names = FALSE,
row_names_gp = gpar(fontsize = 4.5),
column_names_gp = gpar(fontsize = 4.5),
heatmap_legend_param = list(
title = "SNPs",
title_gp = title_gp,
labels_gp = label_gp,
at = c(0, 100, 200, 300, 400)
)
)
# Save the plot
png(
filename = "Figures/ST38_recombination_masked_SNP_heatmap.png",
width = 8,
height = 6,
units = "in",
res = 600
)
draw(ht, merge_legend = TRUE)
dev.off()## png
## 2# Print the plot
draw(ht, merge_legend = TRUE)
Recombination masked networks
# Convert matrix to long format
long_snp <- ST38_snp_matrix_recomb_masked %>%
rownames_to_column(var = "Isolate1") %>%
pivot_longer(-Isolate1, names_to = "Isolate2", values_to = "SNP_distance")
# Keep only the required columns for edges
edges <- long_snp %>%
select(Isolate1, Isolate2, SNP_distance)
# Categorise SNP distances for edge construction
edges <- edges %>%
mutate(
category = case_when(
SNP_distance <= ST38_strict ~ "<0.0002% (10 SNPs per 5 Mb)",
SNP_distance <= ST38_moderate ~ "<0.0005% (25 SNPs per 5 Mb)",
SNP_distance <= ST38_loose ~ "<0.002% (100 SNPs per 5 Mb)"
),
category = factor(category,
levels = c("<0.0002% (10 SNPs per 5 Mb)",
"<0.0005% (25 SNPs per 5 Mb)",
"<0.002% (100 SNPs per 5 Mb)"),
ordered = TRUE)
) %>%
filter(!is.na(category))
# Add weighting to distribute nodes
edges <- edges %>%
mutate(weight = 1 / (SNP_distance + 1))
# Get node data
nodes <- ST38_metadata %>%
mutate(
# Combine Study and isolation year with space + brackets
`Study (isolation year)` = paste0(Study, " (", Year, ")")
) %>%
rownames_to_column(var = "Isolate")
# Create igraph object
g <- graph_from_data_frame(d = edges, vertices = nodes, directed = FALSE)
# Set seed for reproducible plotting and compute layout coordinate
set.seed(42)
lay <- create_layout(g, layout = "fr")
# Plot networks
network_plot <- ggraph(lay) +
geom_edge_link(aes(colour = category), width = 0.7, alpha = 0.7) +
geom_node_point(aes(fill = `Study (isolation year)`, colour = Location),
shape = 21, size = 2.5, stroke = 1.0) +
scale_edge_color_manual(
values = c("<0.0002% (10 SNPs per 5 Mb)" = "red",
"<0.0005% (25 SNPs per 5 Mb)" = "orange",
"<0.002% (100 SNPs per 5 Mb)" = "yellow2"),
breaks = c("<0.0002% (10 SNPs per 5 Mb)",
"<0.0005% (25 SNPs per 5 Mb)",
"<0.002% (100 SNPs per 5 Mb)"
)
) +
scale_colour_manual(
values = c("Blantyre (urban)" = "red",
"Mangochi District (rural)" = "springgreen",
"Nsanje District (rural)" = "green4"
)
) +
scale_fill_manual(
values = c('Lewis et al. (2017)' = "grey95",
'Lewis et al. (2018)' = "grey30",
'Musicha et al. (2019)' = "pink",
'Musicha et al. (2020)' = "orange2",
'This study (2023)' = "skyblue1",
'This study (2024)' = "blue1"
)
) +
theme_void() +
labs(edge_color = "SNPs as % of core genome alignment", color = "Location", fill = "Study (isolation year)")
# Save the plot
ggsave("Figures/ST38_network_plot.png", network_plot, height = 4, width = 7, dpi = 600)
# Print the plot
print(network_plot)
Networks without masking recombination
# Load ST38 SNP distance matrix including recombination sites
ST38_snp_matrix <- read.csv("data/ST38_malawi_SNP_dists.csv", header = TRUE, row.names = 1)
# Identify SNP thresholds representing strict, moderate and loose transmission networks including recombination sites
ST38_strict_with_recomb <- ST38_cg_size*0.000002
ST38_moderate_with_recomb <- ST38_cg_size*0.000005
ST38_loose_with_recomb <- ST38_cg_size*0.00002
print(c(ST38_strict_with_recomb, ST38_moderate_with_recomb, ST38_loose_with_recomb))## [1] 8.55871 21.39678 85.58710# Convert matrix to long format
long_snp <- ST38_snp_matrix %>%
rownames_to_column(var = "Isolate1") %>%
pivot_longer(-Isolate1, names_to = "Isolate2", values_to = "SNP_distance")
# Keep only the required columns for edges
edges <- long_snp %>%
select(Isolate1, Isolate2, SNP_distance)
# Categorise SNP distances for edge construction
edges <- edges %>%
mutate(
category = case_when(
SNP_distance <= ST38_strict_with_recomb ~ "<0.0002% (10 SNPs per 5 Mb)",
SNP_distance <= ST38_moderate_with_recomb ~ "<0.0005% (25 SNPs per 5 Mb)",
SNP_distance <= ST38_loose_with_recomb ~ "<0.002% (100 SNPs per 5 Mb)"
),
category = factor(category,
levels = c("<0.0002% (10 SNPs per 5 Mb)",
"<0.0005% (25 SNPs per 5 Mb)",
"<0.002% (100 SNPs per 5 Mb)"),
ordered = TRUE)
) %>%
filter(!is.na(category))
# Add weighting to distribute nodes
edges <- edges %>%
mutate(weight = 1 / (SNP_distance + 1))
# Create igraph object
g <- graph_from_data_frame(d = edges, vertices = nodes, directed = FALSE)
# Set seed for reproducible plotting and compute layout coordinate
set.seed(42)
lay <- create_layout(g, layout = "fr")
# Plot networks
network_plot <- ggraph(lay) +
geom_edge_link(aes(colour = category), width = 0.7, alpha = 0.7) +
geom_node_point(aes(fill = `Study (isolation year)`, colour = Location),
shape = 21, size = 2.5, stroke = 1.0) +
scale_edge_color_manual(
values = c("<0.0002% (10 SNPs per 5 Mb)" = "red",
"<0.0005% (25 SNPs per 5 Mb)" = "orange",
"<0.002% (100 SNPs per 5 Mb)" = "yellow2"),
breaks = c("<0.0002% (10 SNPs per 5 Mb)",
"<0.0005% (25 SNPs per 5 Mb)",
"<0.002% (100 SNPs per 5 Mb)"
)
) +
scale_colour_manual(
values = c("Blantyre (urban)" = "red",
"Mangochi District (rural)" = "springgreen",
"Nsanje District (rural)" = "green4"
)
) +
scale_fill_manual(
values = c('Lewis et al. (2017)' = "grey95",
'Lewis et al. (2018)' = "grey30",
'Musicha et al. (2019)' = "pink",
'Musicha et al. (2020)' = "orange2",
'This study (2023)' = "skyblue1",
'This study (2024)' = "blue1"
)
) +
theme_void() +
labs(edge_color = "SNPs as % of core genome alignment", color = "Location", fill = "Study (isolation year)")
# Save the plot
ggsave("Figures/ST38_network_plot_inc_recomb_sites.png", network_plot, height = 4, width = 7, dpi = 600)
# Print the plot
print(network_plot)
Tree
# Read in the treefile
ST38_tree <- read.tree("Data/ST38.aln.treefile")
# Root the tree at midpoint
ST38_tree <- midpoint(ST38_tree)
# Extract isolate metadata for tree
tree_metadata <- nodes[, c("Isolate", "Location", "Study (isolation year)")]
# Reorder metadata to match tree tip order
tree_metadata <- tree_metadata[match(ST38_tree$tip.label, tree_metadata$Isolate), ]
rownames(tree_metadata) <- tree_metadata$Isolate
# Colour palette
study_fill_vals <- c(
'Lewis et al. (2017)' = "grey95",
'Lewis et al. (2018)' = "grey30",
'Musicha et al. (2019)' = "pink",
'Musicha et al. (2020)' = "orange2",
'This study (2023)' = "skyblue1",
'This study (2024)' = "blue1"
)
# Rename Isolate -> label
tree_metadata_for_join <- tree_metadata %>%
rename(label = Isolate)
# Plot tree
p <- ggtree(ST38_tree) %<+% tree_metadata_for_join +
geom_tippoint(
aes(
fill = `Study (isolation year)`,
shape = Location
),
colour = "black",
stroke = 0.3,
size = 2.0
) +
scale_fill_manual(values = study_fill_vals, drop = FALSE) +
scale_shape_manual(
values = c(
"Blantyre (urban)" = 21,
"Mangochi District (rural)" = 22,
"Nsanje District (rural)" = 24
)
) +
theme_tree2() +
theme(legend.position = "right") +
labs(
fill = "Study (isolation year)",
shape = "Location"
) +
geom_treescale(
x = 0.0005, y = 50, width = 0.001,
fontsize = 3, linesize = 0.5
)
# Override shape in key so colours are plotted properly
p <- p +
guides(
fill = guide_legend(
override.aes = list(shape = 21, colour = "black")
)
)
# Clean x-axis
p <- p +
theme(
axis.line.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.x = element_blank()
)
# Add some border space to prevent plot clipping
p <- p + scale_y_continuous(expand = expansion(mult = c(0.03, 0.03)))## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.# Save the plot
ggsave("Figures/ST38_tree.png", p, width = 8, height = 8, dpi = 600)
# Print the plot
print(p)