Script for analysis

Script_GrowthProfiler.R

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+#Growth profiler script
+#Developed by Simone Zaghen, 18/june/2020
+
+#load packages
+library(readxl)
+library(tidyr)
+library(reshape2)
+library(Rmisc)
+library(ggplot2)
+library(gridExtra)
+library(grid)
+library(lattice)
+library(deSolve)
+library(dplyr)
+library(tibble)
+library(growthrates)
+
+
+setwd("/Users/zaghen/OneDrive - Chalmers/z.LAB/Templates/GrowthProfiler/")
+rawdata <- read_excel("Data.xlsx")
+
+#create the df and plot all the replicates 
+sampleindex = 1
+for (i in 2:ncol(rawdata)) {
+  replicateindex = (i-1) %% 3 #3 for triplicates, 4 for quadruplicates and so on
+
+  if(replicateindex == 0){
+    replicateindex = 3} #3 for triplicates, 4 for quadruplicates and so on
+  if(replicateindex == 1 && i!=2){
+    sampleindex = sampleindex + 1}
+  samplename = paste0("s",sampleindex)
+  colnames(rawdata)[i] = paste0(colnames(rawdata)[i],"_",
+                                samplename,
+                                "_",
+                                replicateindex)}
+
+df_to_visualise_triplicates <- melt(rawdata, 
+                                    id=c("time"), 
+                                    variable.name="sample", 
+                                    value.name="OD", 
+                                    na.rm = TRUE) %>%
+  mutate(sample = as.character(sample)) %>%
+  separate(sample, c("well", "sample", "replicate"), "_")
+
+ggplot(data = df_to_visualise_triplicates) +
+  geom_line(aes(x = time, 
+                y = OD, 
+                group = replicate, 
+                color = replicate)) +
+  facet_wrap(~ sample,
+             scales = "free") +
+  theme_bw()
+
+#create new data frame to store mean and sd of the replicates
+#you will receive a warning because summarise_each has been deprecated.
+#I will fix this sooner or later - but it still works. 
+average_replicates <- df_to_visualise_triplicates %>% 
+  select(time, sample, OD) %>%
+  group_by(time, sample) %>%
+  summarise_each(funs(mean, sd)) %>%
+  rename(OD = mean)
+average_replicates[1] <- average_replicates[1]/60 #convert minutes in hours
+
+#rename the samples
+average_replicates$sample <- revalue(average_replicates$sample, c("s1"= "Strain 1_Condition 1",
+                                                                  "s2"= "Strain 2_Condition 1",
+                                                                  "s3"= "Strain 3_Condition 2",
+                                                                  "s4"= "Strain 3_Condition 1"))
+
+#"_" is used to split the names into more columns
+average_replicates <- average_replicates %>% separate(sample, c("strain", "condition"), "_")
+
+
+#create plot
+plot <- ggplot(average_replicates,
+               mapping = aes(x = time,
+                             y = OD,
+                             group = strain,
+                             color = strain)) +
+  geom_line() +
+  geom_ribbon(aes(x=time, ymin = OD - sd, ymax = OD + sd, fill = strain), alpha = 0.5, colour = NA) +
+  facet_wrap(~ condition,
+             scales = "free")
+plot
+
+#calculate lag phase (as time when OD is over certain threshold [approximation!])
+lag_phase_all_samples <- average_replicates %>%
+  group_by(strain, condition) %>% 
+  filter(OD > 0.25) %>% #set the threshold here 
+  group_by(strain, condition) %>% 
+  summarise(end_lag_phase = time[1])
+
+#calculate maximum OD
+OD_all_samples <- average_replicates %>%
+  group_by(strain, condition) %>% 
+  filter(OD > 0.25) %>% #to filter out strains that did not grow
+  filter(OD == max(OD)) %>%
+  rename(max_OD = OD) %>%
+  select(strain, condition, max_OD)
+
+#calculate the growth rate with the "growthrate" package - cite them if you use it! 
+all_easylinear_fits <- all_easylinear(OD ~ time | strain + condition, data = average_replicates, spar = 0.5)
+par(mfrow=c(2,2)) #set the parameters in order to display all the samples!
+plot(all_easylinear_fits) #check if the growth rate was calculated in the correct part of the curve!
+
+growth_rate <- as.data.frame(coef(all_easylinear_fits)) %>% 
+  select(mumax) %>% 
+  rename(growth_rate = mumax) %>% 
+  rownames_to_column("sample") %>%
+  separate(sample, c("strain", "condition"), ":")  
+
+#merge and export the dataframes
+parameters <- merge(growth_rate, lag_phase_all_samples) %>% 
+  merge(OD_all_samples)
+write.csv2(parameters, "parameters.csv", row.names = FALSE)