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data_fit_all_map.R
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data_fit_all_map.R
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### Code to fit exponential curve to number of SNPs distribution by time
# If you find this code useful, please cite:
# Coll et al. Definition of a genetic relatedness cutoff to exclude recent transmission of meticillin-resistant Staphylococcus aureus: a genomic epidemiology analysis. Lancet Microbe. 2020 Dec;1(8):e328-e335. doi: 10.1016/S2666-5247(20)30149-X. PMID: 33313577; PMCID: PMC7721685. (https://www.thelancet.com/journals/lanmic/article/PIIS2666-5247(20)30149-X/fulltext)
##########################################################################################################
### 1. INPUT FILES ####
##########################################################################################################
working_dir = "";
setwd(working_dir)
dataS1_file = "SupplementaryData1.xlsx"
require(gdata)
require(ggplot2)
require(svglite)
## Which sheet?
if(map == "CC22"){sheet_num = 1}
if(map == "CC22_core"){sheet_num = 2}
dataS1 = read.xls(dataS1_file, sheet = sheet_num, header = T)
dim(dataS1)
# Removing outliers
dataS1 = dataS1[-which(grepl("outlier",dataS1$Note)==TRUE),]
dim(dataS1)
# Only CC22 data
#dd = dataS1[which(dataS1$CC1 == 22),];
# All data
dd = dataS1;
# Same day
dd0 = dataS1[which(dd$TimeGap == 0),] # 104 pairs
##########################################################################################################
### 1a. MODEL FIT ####
##########################################################################################################
u<-sort(unique(dd$TimeGap))
coef_store<-c(); data_store<-c()
# Run through every time gap. If more than 10 data points then fit exponential to the data and store coefficients
for(i in 1:length(u)){
# For this time gap grab the specific data
w<-which(dd$TimeGap == u[i])
if(length(w)>10){ # need > 10? for fit to be ok
print(i)
# Histogram of distribution
h <- hist(dd[w,]$SNPs,breaks=seq(0,max(dd[w,"SNPs"]),1),plot = T)
data <- data.frame( x = h$breaks[-length(h$breaks)], y = h$counts)
# Fit exponential to distribution of SNPs at each time point
mod <- nls(y ~ exp(a + b * x), data = data, start = list(a = 1.5, b = -0.2), # works for CC22/CC30
control = list(maxiter = 500))
# Can plot as go or all together below
#plot(data$x,data$y)
#lines(data$x, predict(mod, list(x = data$x)))
# store coefficients
data_store <- rbind(data_store,cbind(u[i],data,predict(mod, list(x = data$x))))
coef_store <- rbind(coef_store, c(u[i],coef(mod)))
}
}
colnames(data_store) <- c("day","x","y","m")
coef_store <- as.data.frame(coef_store)
colnames(coef_store) <- c("day","a","b")
ggplot(data_store,aes(x=x, y=y)) + geom_point() + geom_line(aes(x=x,y=m),col="red") +
facet_wrap(~day,scales="free")
ggsave(paste0("output/fit_exponential_per_day_",map,".pdf"),width = 12, height = 12)
### What is the trend in the coefficients?
## for a?
plot(coef_store$day,coef_store$a,ylim = c(0,4))
# Assuming expoential link between day and coefficient
mod_a <- nls(a ~ exp(aa + bb * day), data = coef_store, start = list(aa = 1.4, bb = -0.2),
control = list(maxiter = 500))
lines(coef_store$day, predict(mod_a, list(x = coef_store$day)),col="red")
mod_a_mod <- coef(mod_a)
pdf(paste0("output/a_exponential_",map,".pdf"))
plot(coef_store$day,coef_store$a,ylim = c(0,4),xlab = "Time between samples",ylab="Intercept")
lines(coef_store$day, predict(mod_a, list(x = coef_store$day)),col="red")
dev.off()
## What is the trend for b?
plot(coef_store[,1],coef_store[,3],xlab = "Time between samples",ylab = "Slope")
## Assume no change by day in b
pdf(paste0("output/b_flat_",map,".pdf"))
plot(coef_store[,1],coef_store[,3],xlab = "Time between samples",ylab = "Slope")
# Assume all v similar - take mean without the v small outliers greater than -8
mean(coef_store[,3])
p_aa <- mean(coef_store[which(coef_store[,3] > (-4)),3]) # remove outliers
abline(h = p_aa,col="red",lty="dashed")
dev.off()
## General model for the fit
data_store$mod_general <- exp(exp(mod_a_mod[1] + mod_a_mod[2]*data_store[,"day"]) + p_aa*data_store[,"x"])
# plot the general model for the fit (blue) against individual (red)
ggplot(data_store,aes(x=x, y=y)) + geom_point() + geom_line(aes(x=x,y=m),col="red") +
facet_wrap(~day,scales="free") + geom_line(aes(x=x,y=mod_general),col="blue") +
scale_x_continuous("SNP distance") + scale_y_continuous("Count")
ggsave(paste0("output/fit_exponential_gen&ind_per_day_",map,".pdf"),width = 12, height = 12)
# Parameters - output
mod_a_mod[1]
mod_a_mod[2]
p_aa
param_general_fit = c(mod_a_mod[1],mod_a_mod[2],p_aa)
write.csv(param_general_fit,paste0("output/param_general_fit_",map,".csv"))