Phenotypic Heterogeneity in Aspergillus niger Strains

1. Introduction This analysis explores phenotypic heterogeneity in Aspergillus niger strains using time-lapse imaging data. We focus on the first time point (time = 0) to capture inherent variability.

Phenotypic traits studied:

Area (spore size)

Contrast (visual texture/color variation)

Strains:

N402 (Mature reference strain)

N402 38h (Immature reference strain)

SJS128 (mutant strain)

2. Load & Filter Data

# Read CSVs (replace paths if needed)
data1 <- read.csv("G:/sjs samples/reloadSJS/results/n/1.csv")
data2 <- read.csv("G:/sjs samples/reloadSJS/results/n/2.csv")
data3 <- read.csv("G:/sjs samples/reloadSJS/results/n/3.csv")

# Combine
data <- bind_rows(data1, data2, data3)

# Filter for time 0 and species of interest
filtered_data_t0 <- data %>%
  filter(time == 0, Area <= 200) %>%
  filter(species %in% c("N402", "N402 38h", "SJS128"))

3. Area Distributions by Species

ggplot(filtered_data_t0, aes(x = species, y = Area, fill = species)) +
  geom_boxplot(alpha = 0.6) +
  scale_fill_manual(values = c("SJS128" = "khaki", "N402" = "darkgreen", "N402 38h" = "olivedrab3")) +
  theme_minimal(base_size = 14) +
  labs(title = "Distribution of Spore Area by Species", x = "Species", y = "Area") +
  theme(legend.position = "none")

4. Tukey HSD Post Hoc Test

# ANOVA and TukeyHSD
anova_result <- aov(Area ~ species, data = filtered_data_t0)
summary(anova_result)

##                Df  Sum Sq Mean Sq F value Pr(>F)    
## species         2  410002  205001    1508 <2e-16 ***
## Residuals   18287 2485395     136                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

TukeyHSD(anova_result)

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = Area ~ species, data = filtered_data_t0)
## 
## $species
##                       diff        lwr        upr p adj
## N402 38h-N402    -5.860185  -6.392253  -5.328117     0
## SJS128-N402     -11.438209 -11.928544 -10.947874     0
## SJS128-N402 38h  -5.578024  -6.063493  -5.092555     0

5. Area vs. Contrast with Marginal Distributions This figure shows the heterogeneity within and between strains in terms of both size and color/contrast

# Main scatter plot
scatter_plot <- ggplot(filtered_data_t0, aes(x = Area, y = Contrast, color = species)) +
  geom_point(size = 0.8) +
  scale_color_manual(values = c("SJS128" = "khaki", "N402" = "darkgreen", "N402 38h" = "olivedrab3")) +
  theme_classic(base_size = 16) + 
  theme(legend.position = "none") +
  labs(x = "Area", y = "Contrast") +
  scale_x_continuous(breaks = seq(50, 180, by = 20), limits = c(70, 170))

# Marginal density (top)
density_top <- ggplot(filtered_data_t0, aes(x = Area, fill = species, y = species)) +
  stat_density_ridges(quantile_lines = TRUE, quantiles = 0.5, scale = 1.1, alpha = 0.7) +
  scale_fill_manual(values = c("SJS128" = "khaki", "N402" = "darkgreen", "N402 38h" = "olivedrab3")) +
  theme_void() +
  theme(legend.position = "none")

# Marginal density (right - Contrast)
density_right <- ggplot(filtered_data_t0, aes(x = Contrast, fill = species, y = species)) +
  stat_density_ridges(quantile_lines = TRUE, quantiles = 0.5, scale = 1.1, alpha = 0.7) +
  scale_fill_manual(values = c("SJS128" = "khaki", "N402" = "darkgreen", "N402 38h" = "olivedrab3")) +
  coord_flip() +
  theme_void() +
  theme(legend.position = "none")

# Empty for spacing
empty_plot <- ggplot() + theme_void()

# Combine
combined_plot <- cowplot::plot_grid(
  cowplot::plot_grid(density_top, empty_plot, ncol = 2, rel_widths = c(4, 1)),
  cowplot::plot_grid(scatter_plot, density_right, ncol = 2, rel_widths = c(4, 1)),
  nrow = 2, rel_heights = c(1, 3)
)

# Final plot with margin
final_plot <- ggdraw(combined_plot) +
  theme(plot.margin = margin(10, 10, 10, 10, "mm"))

final_plot

6. Conclusion Aspergillus niger strains show inherent phenotypic heterogeneity in both spore size (Area) and contrast at time zero.

Tukey’s HSD confirms statistically significant differences in Area between the three species.

Visualization reveals both within-strain variation and between-strain differences.

These findings support the presence of biological diversity that may influence growth dynamics.

Appendix: Summary Stats

filtered_data_t0 %>%
  group_by(species) %>%
  summarise(
    Mean_Area = round(mean(Area), 1),
    SD_Area = round(sd(Area), 1),
    Mean_Contrast = round(mean(Contrast), 3),
    SD_Contrast = round(sd(Contrast), 3),
    Count = n()
  )

## # A tibble: 3 x 6
##   species  Mean_Area SD_Area Mean_Contrast SD_Contrast Count
##   <chr>        <dbl>   <dbl>         <dbl>       <dbl> <int>
## 1 N402          116.    11.7         0.363       0.033  5188
## 2 N402 38h      110.    13.1         0.312       0.054  5365
## 3 SJS128        104.    10.5         0.334       0.038  7737