Growth Curves

# loading packages
library(tidyverse)
library(ggtext)
library(kableExtra)
library(pracma)
library(lme4)
library(lmerTest)
library(emmeans)

source("R/LMM_functions.R")

Data Input

All data inputs and outputs in this repo are relative to FOLDER_PATH. Please, save the provided data frames in RDS format in the dataframes folder.

# Load the BOX_PATH environment variable
FOLDER_PATH <- Sys.getenv("BOX_PATH") 

# Define subdirectories within the Box folder
dataframes_folder <- file.path(FOLDER_PATH, "dataframes")

outputs_folder <- file.path(FOLDER_PATH, "outputs/GC_OD600")
if (!dir.exists(outputs_folder)) {
  dir.create(outputs_folder, recursive = TRUE)
}

# Read the provided RDS file
dataframeID <- "DatCol"
GC_OD600_data <- readRDS(file.path(dataframes_folder, paste0(dataframeID, "_GC_OD600.rds")))

# Filter the data to include only time points up to 20 hours and exclude the Uncolonized control
GC_OD600_20h <- GC_OD600_data %>%
  filter(time <= 20) %>%
  filter(label != "NB")

# Summarize data by calculating mean and SEM for each time point and condition
GC_OD600_20h_summarized <- GC_OD600_20h %>%
  group_by(label, media, time, strain, vector) %>%
  summarise(
    mean_OD600 = mean(OD600),
    sem_OD600 = sd(OD600) / sqrt(n()),  
    .groups = "drop"
  ) 

GC_plot_function <- function(data, media_filter, ymin = 0, ymax = 1.8) {
  # Filter data based on date and media type
  filtered_data <- data %>%
    filter(media %in% media_filter) %>%
    droplevels()
  
  # Create the growth curve plot
  growth_curve_plot <- ggplot(filtered_data, aes(x = time, y = mean_OD600, color = strain, shape = vector)) +
    geom_line() +
    geom_point(size = 3) +
    
    scale_color_manual(
      values = c(
        Parent  = "#5F05AF",
        "dat::Tn"  = "#2FD612",
        Δalr1 = "#03C0FF"
      ),
      labels = c(
        Parent = "<b><span style='color:#5F05AF'>parent<span></b>",
        "dat::Tn" = "<b><span style='color:#2FD612'><i>dat</i>::Tn<span></b>",
        "Δalr1" = "<b><span style='color:#03C0FF'>Δ  <i>alr1</i><span></b>"
      )
    ) +
    scale_shape_manual(
      values = c(
        None     = 16,  # filled circle
        EV     = 1,   # empty circle
        "PpepV dat"  = 17,  # filled triangle
        "Pdat dat"   = 2,   # empty triangle
        alr1  = 15   # filled square
      ),
      labels = c(
        None = "None",
        EV = "EV",
        "PpepV dat" = "P<i><sub>pepV</sub>dat</i>",
        "Pdat dat" = "P<i><sub>dat</sub>dat</i>",
        alr1 = "P<i><sub>alr1</sub>alr1</i>"
      )
    ) +
    
    geom_errorbar(aes(ymin = mean_OD600 - sem_OD600, ymax = mean_OD600 + sem_OD600), width = 0.2) +
    
    labs(title = paste(paste(media_filter)),
         x = "Time (hours)",
         y = "Mean OD<sub>600</sub> ± SEM") +
    
    scale_y_continuous(limits = c(ymin, ymax), expand = c(0, 0)) +
    theme_bw() +
    theme(
      plot.title = element_text(vjust = -10, hjust = 0.05, size = 14, face = "bold"),
      legend.text = element_markdown(size = 12),
      legend.title = element_markdown(size = 12),
      panel.grid = element_blank(),
      text = element_text(size = 16, color = "black"),
      axis.text = element_markdown(color = "black"),
      axis.title.y = element_markdown()
    ) +
    
    guides(color = guide_legend(override.aes = list(linetype = 0, shape = NA), keywidth = 0))                               
  
  return(growth_curve_plot)
}

Main figure

figMain <- GC_plot_function(GC_OD600_20h_summarized %>% filter (vector != "None"), media_filter = c("CDM19 - ala"), ymin = 0, ymax = 1.5)
figMain

# Save the plot
ggsave(filename = file.path(outputs_folder, "figMain.png"), plot = figMain, width = 10, height = 6, dpi = 300)
saveRDS(figMain, file.path(outputs_folder, "figMain.rds"))

Stats and Supplemental Figures

The Area Under the Curve (AUC) was calculated for each growth curve to quantitatively compare the growth across different conditions. The AUC was computed using the trapezoidal rule from the pracma package.

# Calculate AUC for each growth curve using package pracma's trapz function, which performs numerical integration using the trapezoidal rule.
GC_AUC <- GC_OD600_20h %>%
  mutate(
    media = recode(media, "CDM19 - ala" = "CDM19Ala"),
    across(where(is.character), ~ str_replace_all(.x, " ", "")),
    across(where(is.factor), ~ factor(str_replace_all(as.character(.x), " ", "")))
  ) %>%
  group_by(label, media, strain, vector, date) %>%  
  summarise(AUC = trapz(time, OD600), .groups = "drop")

# Make a dataframe for each media condion for stats 
GC_AUC_CDM19Ala <- GC_AUC %>%
  filter(media == "CDM19Ala") %>%
  droplevels()

GC_AUC_TSB <- GC_AUC %>%
  filter(media == "TSB") %>%
  droplevels()

GC_AUC_CDM20 <- GC_AUC %>%
  filter(media == "CDM20") %>%
  droplevels()

Data for each media conditions were analyzed separately using a LMM to determine statistical significance with label as fixed effects and experimental date as a random effect. We analyzed pairwise contrasts between Parent with the EV or no vector compared to all other strains. Also we look at all contrast within the strains dat::Tn and Δalr1. We then adjusted the p-values for multiple comparisons using the Holm method and created a sign annotation column to indicate which comparisons are statistically significant with p<0.05.

TSB

# Run the mixed model with emmeans contrasts for the JE2 dataset.
stats_results_AUC_TSB <- mixed_model_with_emmeans(
  input_data = GC_AUC_TSB, response = "AUC", log_response = FALSE, adjust = "none",
  fixed_effects = c("label"), random_effects1 = "date"
)
stats_formated_AUC_TSB <- format_stats_results(stats_results_AUC_TSB)
stats_formated_AUC_TSB$main = stats_formated_AUC_TSB$main %>%
  filter(condition1 == "Parent" | condition2 == "Parent" | condition1 == "Parent_EV" | condition2 == "Parent_EV" 
         | (grepl("dat::Tn", condition1) & grepl("dat::Tn", condition2))
         | (grepl("Δalr1", condition1) & grepl("Δalr1", condition2))
  ) %>%
  mutate(
    p.value.adj = p.adjust(p.value, method = "holm"),
    sign = case_when(p.value.adj < 0.05 ~ "*", TRUE ~ "")
  )

# Save the stats results
saveRDS(stats_formated_AUC_TSB, file.path(outputs_folder, "stats_results_AUC_TSB.rds"))

# Render the results
cat("Model Formula:", format(stats_results_AUC_TSB$model_formula))

Model Formula: AUC ~ label + (1 | date)

kable(stats_formated_AUC_TSB$random_effects, caption = "Random Effects")

Random Effects

grp	var1	var2	vcov	sdcor	proportion	singular
date	(Intercept)	NA	4.8158862	2.1945127	85.91	FALSE
Residual	NA	NA	0.7895996	0.8885942	14.09	FALSE

kable(stats_formated_AUC_TSB$anova, caption = "ANOVA Table")

ANOVA Table

	Sum Sq	Mean Sq	NumDF	DenDF	F value	Pr(>F)
label	50.14469	4.178724	12	24	5.292207	0.0002623

kable(stats_formated_AUC_TSB$main %>%
    mutate(
      p.value.anova = formatC(p.value.anova, format = "e", digits = 2),
      p.value = formatC(p.value, format = "e", digits = 2),
      p.value.adj = formatC(p.value.adj, format = "e", digits = 2)
    )  %>%
    select(effect_name, p.value.anova,  condition1, condition2, predicted_mean.1, predicted_mean.2, FC, log2FC, p.value, p.value.adj, sign),
  caption = "Main Contrasts with Predicted Log2FC"
)

Main Contrasts with Predicted Log2FC

effect_name	p.value.anova	condition1	condition2	predicted_mean.1	predicted_mean.2	FC	log2FC	p.value	p.value.adj	sign
label	2.62e-04	dat::Tn	dat::Tn_alr1	23.55050	22.88650	1.0290127	0.0412608	3.69e-01	1.00e+00
label	2.62e-04	dat::Tn	dat::Tn_EV	23.55050	23.43250	1.0050357	0.0072468	8.72e-01	1.00e+00
label	2.62e-04	dat::Tn	dat::Tn_Pdatdat	23.55050	23.30633	1.0104764	0.0150356	7.39e-01	1.00e+00
label	2.62e-04	dat::Tn	dat::Tn_PpepVdat	23.55050	22.28233	1.0569135	0.0798574	9.33e-02	1.00e+00
label	2.62e-04	dat::Tn	Parent	23.55050	22.33933	1.0542168	0.0761716	1.08e-01	1.00e+00
label	2.62e-04	dat::Tn	Parent_EV	23.55050	22.74933	1.0352171	0.0499334	2.80e-01	1.00e+00
label	2.62e-04	dat::Tn_alr1	dat::Tn_EV	22.88650	23.43250	0.9766990	-0.0340140	4.59e-01	1.00e+00
label	2.62e-04	dat::Tn_alr1	dat::Tn_Pdatdat	22.88650	23.30633	0.9819863	-0.0262252	5.68e-01	1.00e+00
label	2.62e-04	dat::Tn_alr1	dat::Tn_PpepVdat	22.88650	22.28233	1.0271142	0.0385965	4.13e-01	1.00e+00
label	2.62e-04	dat::Tn_alr1	Parent	22.88650	22.33933	1.0244934	0.0349107	4.58e-01	1.00e+00
label	2.62e-04	dat::Tn_alr1	Parent_EV	22.88650	22.74933	1.0060295	0.0086726	8.52e-01	1.00e+00
label	2.62e-04	dat::Tn_EV	dat::Tn_Pdatdat	23.43250	23.30633	1.0054134	0.0077888	8.63e-01	1.00e+00
label	2.62e-04	dat::Tn_EV	dat::Tn_PpepVdat	23.43250	22.28233	1.0516179	0.0726106	1.26e-01	1.00e+00
label	2.62e-04	dat::Tn_EV	Parent	23.43250	22.33933	1.0489346	0.0689248	1.45e-01	1.00e+00
label	2.62e-04	dat::Tn_EV	Parent_EV	23.43250	22.74933	1.0300302	0.0426866	3.56e-01	1.00e+00
label	2.62e-04	dat::Tn_Pdatdat	dat::Tn_PpepVdat	23.30633	22.28233	1.0459557	0.0648217	1.71e-01	1.00e+00
label	2.62e-04	dat::Tn_Pdatdat	Parent	23.30633	22.33933	1.0432869	0.0611359	1.95e-01	1.00e+00
label	2.62e-04	dat::Tn_Pdatdat	Parent_EV	23.30633	22.74933	1.0244842	0.0348978	4.50e-01	1.00e+00
label	2.62e-04	dat::Tn_PpepVdat	Parent	22.28233	22.33933	0.9974484	-0.0036858	9.38e-01	1.00e+00
label	2.62e-04	dat::Tn_PpepVdat	Parent_EV	22.28233	22.74933	0.9794719	-0.0299240	5.26e-01	1.00e+00
label	2.62e-04	Parent	Parent_alr1	22.33933	23.25467	0.9606387	-0.0579341	2.19e-01	1.00e+00
label	2.62e-04	Parent	Parent_EV	22.33933	22.74933	0.9819775	-0.0262381	5.77e-01	1.00e+00
label	2.62e-04	Parent	Parent_Pdatdat	22.33933	23.08533	0.9676851	-0.0473904	3.14e-01	1.00e+00
label	2.62e-04	Parent	Parent_PpepVdat	22.33933	23.12267	0.9661227	-0.0497217	2.91e-01	1.00e+00
label	2.62e-04	Parent	Δalr1	22.33933	19.68833	1.1346483	0.1822451	1.26e-03	4.40e-02	*
label	2.62e-04	Parent	Δalr1_alr1	22.33933	21.95267	1.0176137	0.0251899	5.99e-01	1.00e+00
label	2.62e-04	Parent	Δalr1_EV	22.33933	20.42250	1.0938589	0.1294266	1.43e-02	4.57e-01
label	2.62e-04	Parent_alr1	Parent_EV	23.25467	22.74933	1.0222131	0.0316960	4.93e-01	1.00e+00
label	2.62e-04	Parent_EV	Parent_Pdatdat	22.74933	23.08533	0.9854453	-0.0211523	6.47e-01	1.00e+00
label	2.62e-04	Parent_EV	Parent_PpepVdat	22.74933	23.12267	0.9838542	-0.0234835	6.12e-01	1.00e+00
label	2.62e-04	Parent_EV	Δalr1	22.74933	19.68833	1.1554728	0.2084833	3.03e-04	1.09e-02	*
label	2.62e-04	Parent_EV	Δalr1_alr1	22.74933	21.95267	1.0362902	0.0514281	2.83e-01	1.00e+00
label	2.62e-04	Parent_EV	Δalr1_EV	22.74933	20.42250	1.1139348	0.1556648	3.78e-03	1.28e-01
label	2.62e-04	Δalr1	Δalr1_alr1	19.68833	21.95267	0.8968538	-0.1570552	4.65e-03	1.53e-01
label	2.62e-04	Δalr1	Δalr1_EV	19.68833	20.42250	0.9640511	-0.0528185	3.22e-01	1.00e+00
label	2.62e-04	Δalr1_alr1	Δalr1_EV	21.95267	20.42250	1.0749255	0.1042367	4.56e-02	1.00e+00

figSuppA <- GC_plot_function(GC_OD600_20h_summarized, media_filter = c("TSB"), ymin = 0, ymax = 1.5)
figSuppA

# Save the plot
ggsave(filename = file.path(outputs_folder, "figSuppA.png"), plot = figSuppA, width = 10, height = 6, dpi = 300)
saveRDS(figSuppA, file.path(outputs_folder, "figSuppA.rds"))

CDM20

# Run the mixed model with emmeans contrasts for the JE2 dataset.
stats_results_AUC_CDM20 <- mixed_model_with_emmeans(
  input_data = GC_AUC_CDM20, response = "AUC", log_response = FALSE, adjust = "none",
  fixed_effects = c("label"), random_effects1 = "date"
)
stats_formated_AUC_CDM20 <- format_stats_results(stats_results_AUC_CDM20)
stats_formated_AUC_CDM20$main = stats_formated_AUC_CDM20$main %>%
  filter(condition1 == "Parent" | condition2 == "Parent" | condition1 == "Parent_EV" | condition2 == "Parent_EV" 
         | (grepl("dat::Tn", condition1) & grepl("dat::Tn", condition2))
         | (grepl("Δalr1", condition1) & grepl("Δalr1", condition2))
  ) %>%
  mutate(
    p.value.adj = p.adjust(p.value, method = "holm"),
    sign = case_when(p.value.adj < 0.05 ~ "*", TRUE ~ "")
  )

# Save the stats results
saveRDS(stats_formated_AUC_CDM20, file.path(outputs_folder, "stats_results_AUC_CDM20.rds"))

# Render the results
cat("Model Formula:", format(stats_results_AUC_CDM20$model_formula))

Model Formula: AUC ~ label + (1 | date)

kable(stats_formated_AUC_CDM20$random_effects, caption = "Random Effects")

Random Effects

grp	var1	var2	vcov	sdcor	proportion	singular
date	(Intercept)	NA	0.1615282	0.4019057	45.43	FALSE
Residual	NA	NA	0.1940174	0.4404741	54.57	FALSE

kable(stats_formated_AUC_CDM20$anova, caption = "ANOVA Table")

ANOVA Table

	Sum Sq	Mean Sq	NumDF	DenDF	F value	Pr(>F)
label	4.220684	0.3517237	12	24	1.812846	0.103848

kable(stats_formated_AUC_CDM20$main %>%
    mutate(
      p.value.anova = formatC(p.value.anova, format = "e", digits = 2),
      p.value = formatC(p.value, format = "e", digits = 2),
      p.value.adj = formatC(p.value.adj, format = "e", digits = 2)
    )  %>%
    select(effect_name, p.value.anova,  condition1, condition2, predicted_mean.1, predicted_mean.2, FC, log2FC, p.value, p.value.adj, sign),
  caption = "Main Contrasts with Predicted Log2FC"
)

Main Contrasts with Predicted Log2FC

effect_name	p.value.anova	condition1	condition2	predicted_mean.1	predicted_mean.2	FC	log2FC	p.value	p.value.adj	sign
label	1.04e-01	dat::Tn	dat::Tn_alr1	17.96267	18.24067	0.9847593	-0.0221569	4.47e-01	1.00e+00
label	1.04e-01	dat::Tn	dat::Tn_EV	17.96267	17.98150	0.9989526	-0.0015118	9.59e-01	1.00e+00
label	1.04e-01	dat::Tn	dat::Tn_Pdatdat	17.96267	18.17517	0.9883082	-0.0169671	5.60e-01	1.00e+00
label	1.04e-01	dat::Tn	dat::Tn_PpepVdat	17.96267	17.76317	1.0112311	0.0161127	5.84e-01	1.00e+00
label	1.04e-01	dat::Tn	Parent	17.96267	18.09783	0.9925313	-0.0108154	7.10e-01	1.00e+00
label	1.04e-01	dat::Tn	Parent_EV	17.96267	18.21117	0.9863545	-0.0198218	4.96e-01	1.00e+00
label	1.04e-01	dat::Tn_alr1	dat::Tn_EV	18.24067	17.98150	1.0144130	0.0206451	4.78e-01	1.00e+00
label	1.04e-01	dat::Tn_alr1	dat::Tn_Pdatdat	18.24067	18.17517	1.0036038	0.0051899	8.57e-01	1.00e+00
label	1.04e-01	dat::Tn_alr1	dat::Tn_PpepVdat	18.24067	17.76317	1.0268815	0.0382697	1.97e-01	1.00e+00
label	1.04e-01	dat::Tn_alr1	Parent	18.24067	18.09783	1.0078923	0.0113415	6.95e-01	1.00e+00
label	1.04e-01	dat::Tn_alr1	Parent_EV	18.24067	18.21117	1.0016199	0.0023351	9.35e-01	1.00e+00
label	1.04e-01	dat::Tn_EV	dat::Tn_Pdatdat	17.98150	18.17517	0.9893444	-0.0154552	5.95e-01	1.00e+00
label	1.04e-01	dat::Tn_EV	dat::Tn_PpepVdat	17.98150	17.76317	1.0122914	0.0176246	5.49e-01	1.00e+00
label	1.04e-01	dat::Tn_EV	Parent	17.98150	18.09783	0.9935720	-0.0093036	7.49e-01	1.00e+00
label	1.04e-01	dat::Tn_EV	Parent_EV	17.98150	18.21117	0.9873887	-0.0183100	5.29e-01	1.00e+00
label	1.04e-01	dat::Tn_Pdatdat	dat::Tn_PpepVdat	18.17517	17.76317	1.0231941	0.0330798	2.63e-01	1.00e+00
label	1.04e-01	dat::Tn_Pdatdat	Parent	18.17517	18.09783	1.0042731	0.0061516	8.32e-01	1.00e+00
label	1.04e-01	dat::Tn_Pdatdat	Parent_EV	18.17517	18.21117	0.9980232	-0.0028548	9.21e-01	1.00e+00
label	1.04e-01	dat::Tn_PpepVdat	Parent	17.76317	18.09783	0.9815079	-0.0269282	3.61e-01	1.00e+00
label	1.04e-01	dat::Tn_PpepVdat	Parent_EV	17.76317	18.21117	0.9753997	-0.0359346	2.25e-01	1.00e+00
label	1.04e-01	Parent	Parent_alr1	18.09783	17.97667	1.0067402	0.0096915	7.39e-01	1.00e+00
label	1.04e-01	Parent	Parent_EV	18.09783	18.21117	0.9937767	-0.0090064	7.55e-01	1.00e+00
label	1.04e-01	Parent	Parent_Pdatdat	18.09783	18.11183	0.9992270	-0.0011156	9.69e-01	1.00e+00
label	1.04e-01	Parent	Parent_PpepVdat	18.09783	18.15983	0.9965859	-0.0049340	8.65e-01	1.00e+00
label	1.04e-01	Parent	Δalr1	18.09783	17.07667	1.0597989	0.0837906	9.06e-03	3.08e-01
label	1.04e-01	Parent	Δalr1_alr1	18.09783	18.17167	0.9959369	-0.0058738	8.39e-01	1.00e+00
label	1.04e-01	Parent	Δalr1_EV	18.09783	17.43733	1.0378785	0.0536376	7.87e-02	1.00e+00
label	1.04e-01	Parent_alr1	Parent_EV	17.97667	18.21117	0.9871233	-0.0186978	5.21e-01	1.00e+00
label	1.04e-01	Parent_EV	Parent_Pdatdat	18.21117	18.11183	1.0054844	0.0078908	7.85e-01	1.00e+00
label	1.04e-01	Parent_EV	Parent_PpepVdat	18.21117	18.15983	1.0028268	0.0040724	8.88e-01	1.00e+00
label	1.04e-01	Parent_EV	Δalr1	18.21117	17.07667	1.0664357	0.0927970	4.29e-03	1.54e-01
label	1.04e-01	Parent_EV	Δalr1_alr1	18.21117	18.17167	1.0021737	0.0031326	9.13e-01	1.00e+00
label	1.04e-01	Parent_EV	Δalr1_EV	18.21117	17.43733	1.0443780	0.0626439	4.17e-02	1.00e+00
label	1.04e-01	Δalr1	Δalr1_alr1	17.07667	18.17167	0.9397414	-0.0896644	5.58e-03	1.95e-01
label	1.04e-01	Δalr1	Δalr1_EV	17.07667	17.43733	0.9793164	-0.0301530	3.26e-01	1.00e+00
label	1.04e-01	Δalr1_alr1	Δalr1_EV	18.17167	17.43733	1.0421127	0.0595113	5.23e-02	1.00e+00

figSuppB <- GC_plot_function(GC_OD600_20h_summarized, media_filter = c("CDM20"), ymin = 0, ymax = 1.5)
figSuppB

# Save the plot
ggsave(filename = file.path(outputs_folder, "figSuppB.png"), plot = figSuppB, width = 10, height = 6, dpi = 300)
saveRDS(figSuppB, file.path(outputs_folder, "figSuppB.rds"))

CDM19 - ala

# Run the mixed model with emmeans contrasts for the JE2 dataset.
stats_results_AUC_CDM19Ala <- mixed_model_with_emmeans(
  input_data = GC_AUC_CDM19Ala, response = "AUC", log_response = FALSE, adjust = "none",
  fixed_effects = c("label"), random_effects1 = "date"
)
stats_formated_AUC_CDM19Ala <- format_stats_results(stats_results_AUC_CDM19Ala)
stats_formated_AUC_CDM19Ala$main = stats_formated_AUC_CDM19Ala$main %>%
  filter(condition1 == "Parent" | condition2 == "Parent" | condition1 == "Parent_EV" | condition2 == "Parent_EV" 
         | (grepl("dat::Tn", condition1) & grepl("dat::Tn", condition2))
         | (grepl("Δalr1", condition1) & grepl("Δalr1", condition2))
  ) %>%
  mutate(
    p.value.adj = p.adjust(p.value, method = "holm"),
    sign = case_when(p.value.adj < 0.05 ~ "*", TRUE ~ "")
  )

# Save the stats results
saveRDS(stats_formated_AUC_CDM19Ala, file.path(outputs_folder, "stats_results_AUC_CDM19Ala.rds"))

# Render the results
cat("Model Formula:", format(stats_results_AUC_CDM19Ala$model_formula))

Model Formula: AUC ~ label + (1 | date)

kable(stats_formated_AUC_CDM19Ala$random_effects, caption = "Random Effects")

Random Effects

grp	var1	var2	vcov	sdcor	proportion	singular
date	(Intercept)	NA	0.0078936	0.0888461	1.55	FALSE
Residual	NA	NA	0.5020733	0.7085713	98.45	FALSE

kable(stats_formated_AUC_CDM19Ala$anova, caption = "ANOVA Table")

ANOVA Table

	Sum Sq	Mean Sq	NumDF	DenDF	F value	Pr(>F)
label	1297.176	108.098	12	24	215.3032	0

kable(stats_formated_AUC_CDM19Ala$main %>%
    mutate(
      p.value.anova = formatC(p.value.anova, format = "e", digits = 2),
      p.value = formatC(p.value, format = "e", digits = 2),
      p.value.adj = formatC(p.value.adj, format = "e", digits = 2)
    )  %>%
    select(effect_name, p.value.anova,  condition1, condition2, predicted_mean.1, predicted_mean.2, FC, log2FC, p.value, p.value.adj, sign),
  caption = "Main Contrasts with Predicted Log2FC"
)

Main Contrasts with Predicted Log2FC

effect_name	p.value.anova	condition1	condition2	predicted_mean.1	predicted_mean.2	FC	log2FC	p.value	p.value.adj	sign
label	2.19e-21	dat::Tn	dat::Tn_alr1	2.729500	2.567000	1.0633035	0.0885534	7.81e-01	1.00e+00
label	2.19e-21	dat::Tn	dat::Tn_EV	2.729500	2.612333	1.0448513	0.0632977	8.41e-01	1.00e+00
label	2.19e-21	dat::Tn	dat::Tn_Pdatdat	2.729500	2.421833	1.1270387	0.1725371	6.00e-01	1.00e+00
label	2.19e-21	dat::Tn	dat::Tn_PpepVdat	2.729500	16.661167	0.1638241	-2.6097808	2.55e-18	8.42e-17	*
label	2.19e-21	dat::Tn	Parent	2.729500	12.746500	0.2141372	-2.2233926	4.59e-15	1.24e-13	*
label	2.19e-21	dat::Tn	Parent_EV	2.729500	12.601167	0.2166069	-2.2068487	6.36e-15	1.59e-13	*
label	2.19e-21	dat::Tn_alr1	dat::Tn_EV	2.567000	2.612333	0.9826464	-0.0252557	9.38e-01	1.00e+00
label	2.19e-21	dat::Tn_alr1	dat::Tn_Pdatdat	2.567000	2.421833	1.0599408	0.0839837	8.04e-01	1.00e+00
label	2.19e-21	dat::Tn_alr1	dat::Tn_PpepVdat	2.567000	16.661167	0.1540708	-2.6983342	1.95e-18	6.83e-17	*
label	2.19e-21	dat::Tn_alr1	Parent	2.567000	12.746500	0.2013886	-2.3119460	3.21e-15	9.95e-14	*
label	2.19e-21	dat::Tn_alr1	Parent_EV	2.567000	12.601167	0.2037113	-2.2954021	4.42e-15	1.24e-13	*
label	2.19e-21	dat::Tn_EV	dat::Tn_Pdatdat	2.612333	2.421833	1.0786594	0.1092394	7.45e-01	1.00e+00
label	2.19e-21	dat::Tn_EV	dat::Tn_PpepVdat	2.612333	16.661167	0.1567917	-2.6730785	2.10e-18	7.15e-17	*
label	2.19e-21	dat::Tn_EV	Parent	2.612333	12.746500	0.2049451	-2.2866903	3.55e-15	1.03e-13	*
label	2.19e-21	dat::Tn_EV	Parent_EV	2.612333	12.601167	0.2073088	-2.2701464	4.89e-15	1.27e-13	*
label	2.19e-21	dat::Tn_Pdatdat	dat::Tn_PpepVdat	2.421833	16.661167	0.1453580	-2.7823179	1.54e-18	5.54e-17	*
label	2.19e-21	dat::Tn_Pdatdat	Parent	2.421833	12.746500	0.1899999	-2.3959297	2.34e-15	7.48e-14	*
label	2.19e-21	dat::Tn_Pdatdat	Parent_EV	2.421833	12.601167	0.1921912	-2.3793858	3.21e-15	9.95e-14	*
label	2.19e-21	dat::Tn_PpepVdat	Parent	16.661167	12.746500	1.3071170	0.3863883	5.34e-07	6.95e-06	*
label	2.19e-21	dat::Tn_PpepVdat	Parent_EV	16.661167	12.601167	1.3221924	0.4029321	2.96e-07	4.14e-06	*
label	2.19e-21	Parent	Parent_alr1	12.746500	13.254500	0.9616734	-0.0563811	3.89e-01	1.00e+00
label	2.19e-21	Parent	Parent_EV	12.746500	12.601167	1.0115333	0.0165438	8.04e-01	1.00e+00
label	2.19e-21	Parent	Parent_Pdatdat	12.746500	13.220833	0.9641223	-0.0527120	4.20e-01	1.00e+00
label	2.19e-21	Parent	Parent_PpepVdat	12.746500	18.009333	0.7077719	-0.4986436	3.02e-09	5.74e-08	*
label	2.19e-21	Parent	Δalr1	12.746500	3.197333	3.9866034	1.9951601	1.33e-14	3.19e-13	*
label	2.19e-21	Parent	Δalr1_alr1	12.746500	7.745000	1.6457715	0.7187640	7.79e-09	1.40e-07	*
label	2.19e-21	Parent	Δalr1_EV	12.746500	3.318667	3.8408497	1.9414255	1.76e-14	4.06e-13	*
label	2.19e-21	Parent_alr1	Parent_EV	13.254500	12.601167	1.0518471	0.0729249	2.70e-01	1.00e+00
label	2.19e-21	Parent_EV	Parent_Pdatdat	12.601167	13.220833	0.9531295	-0.0692558	2.95e-01	1.00e+00
label	2.19e-21	Parent_EV	Parent_PpepVdat	12.601167	18.009333	0.6997020	-0.5151875	1.80e-09	3.61e-08	*
label	2.19e-21	Parent_EV	Δalr1	12.601167	3.197333	3.9411489	1.9786162	1.87e-14	4.10e-13	*
label	2.19e-21	Parent_EV	Δalr1_alr1	12.601167	7.745000	1.6270067	0.7022202	1.33e-08	2.27e-07	*
label	2.19e-21	Parent_EV	Δalr1_EV	12.601167	3.318667	3.7970571	1.9248817	2.48e-14	5.22e-13	*
label	2.19e-21	Δalr1	Δalr1_alr1	3.197333	7.745000	0.4128255	-1.2763961	4.30e-08	6.88e-07	*
label	2.19e-21	Δalr1	Δalr1_EV	3.197333	3.318667	0.9634391	-0.0537346	8.36e-01	1.00e+00
label	2.19e-21	Δalr1_alr1	Δalr1_EV	7.745000	3.318667	2.3337686	1.2226615	6.89e-08	1.03e-06	*

figSuppC <- GC_plot_function(GC_OD600_20h_summarized, media_filter = c("CDM19 - ala"), ymin = 0, ymax = 1.5)
figSuppC

# Save the plot
ggsave(filename = file.path(outputs_folder, "figSuppC.png"), plot = figSuppC, width = 10, height = 6, dpi = 300)
saveRDS(figSuppC, file.path(outputs_folder, "figSuppC.rds"))