Last updated: 2024-10-16

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Introduction

# load dataset
data <- read.csv("./data/Data_QC_filtered.csv")

#  since Buffer and spike are not continuous variables, change from numeric to factors
data$Buffer_nl <- as.factor(data$Buffer_nl)
data$Spike <- as.factor(data$Spike)

Calculating deviation from 50% binding

data$Binding_deviation <- abs(data$Binding.Perc - 50)
sorted_data <- data[order(data$Binding_deviation), ]

dim(sorted_data)
[1] 32 14
# View top results (closest to 50% Binding Percentage)
kable(sorted_data)
Sample Wells Raw.OD Binding.Perc Concentration_pg.ml Average_Conc_pg.ml CV.Perc SD SEM Weight_mg Buffer_nl Spike Failed_samples Binding_deviation
22 32 A11 0.656 50.0 1228.0 1197.0 3.690 44.20 31.30 37.1 250 0 NA 0.0
21 31 H9 0.701 51.2 1070.0 1149.0 9.750 112.00 79.20 21.2 60 0 NA 1.2
24 34 C11 0.687 51.7 1117.0 1124.0 0.867 9.74 6.89 35.5 250 0 NA 1.7
23 33 B11 0.696 52.3 1087.0 1100.0 1.730 19.10 13.50 33.8 250 0 NA 2.3
25 36 E11 0.695 53.2 1090.0 1062.0 3.680 39.10 27.60 31.2 250 0 NA 3.2
15 27 E9 0.610 46.0 1417.0 1393.0 2.430 33.80 23.90 21.6 60 0 NA 4.0
8 18 D7 0.734 56.0 967.1 955.4 1.740 16.60 11.70 30.8 250 0 NA 6.0
16 28 F9 0.757 59.5 901.0 839.7 10.300 86.70 61.30 23.2 250 0 NA 9.5
12 23 A8 0.809 60.9 766.3 793.6 4.880 38.70 27.40 24.6 250 0 NA 10.9
14 26 D9 0.477 37.3 2197.0 1991.0 14.600 291.00 206.00 20.2 60 0 NA 12.7
17 29 G9 0.518 36.2 1907.0 2072.0 11.200 232.00 164.00 36.5 60 0 NA 13.8
26 37 F11 0.482 36.1 2158.0 2076.0 5.620 117.00 82.50 30.8 60 0 NA 13.9
13 24 B9 0.835 64.8 705.5 680.2 5.260 35.80 25.30 20.4 250 0 NA 14.8
29 6 H3 0.490 34.7 2099.0 2204.0 6.750 149.00 105.00 13.7 60 1 NA 15.3
18 3 E3 0.478 33.9 2189.0 2287.0 6.090 139.00 98.50 11.0 60 1 NA 16.1
5 15 A7 0.888 66.2 592.9 643.6 11.100 71.60 50.70 12.0 60 0 NA 16.2
11 22 H7 0.453 33.0 2395.0 2377.0 1.030 24.50 17.40 21.5 250 1 NA 17.0
27 38 G11 0.429 32.5 2619.0 2444.0 10.200 248.00 176.00 34.7 60 0 NA 17.5
3 13 G5 0.451 32.1 2412.0 2477.0 3.680 91.10 64.40 16.8 250 1 NA 17.9
4 14 H5 0.437 31.8 2541.0 2504.0 2.110 52.90 37.40 13.8 250 1 NA 18.2
10 21 G7 0.425 31.6 2660.0 2540.0 6.630 169.00 119.00 28.0 250 1 NA 18.4
30 7 A5 0.436 30.5 2551.0 2669.0 6.250 167.00 118.00 16.4 60 1 NA 19.5
32 9 C5 0.432 30.3 2590.0 2693.0 5.460 147.00 104.00 19.2 250 1 NA 19.7
2 12 F5 0.422 30.0 2690.0 2728.0 1.920 52.50 37.10 24.1 250 1 NA 20.0
19 30A A3 0.403 28.8 2899.0 2888.0 0.565 16.30 11.50 29.6 250 1 NA 21.2
1 11 E5 0.939 71.6 496.8 513.2 4.500 23.10 16.30 17.5 250 0 NA 21.6
28 5 G3 0.944 72.1 488.0 501.4 3.790 19.00 13.40 14.4 60 0 NA 22.1
6 16 B7 0.372 26.8 3297.0 3196.0 4.470 143.00 101.00 23.4 60 1 NA 23.2
9 19 E7 0.364 24.1 3413.0 3730.0 12.000 449.00 317.00 27.9 60 1 NA 25.9
31 8 B5 1.030 77.8 354.9 386.8 11.700 45.10 31.90 15.3 250 0 NA 27.8
20 30B B3 1.050 81.1 314.1 324.9 4.700 15.30 10.80 29.6 250 0 ABOVE 80% binding 31.1
7 17 C7 1.080 84.3 278.0 270.6 3.860 10.50 7.39 24.5 60 0 ABOVE 80% binding 34.3

Scatterplots

Binding percentage

# Scatter plot of Binding Percentage vs Weight, 4 groups
ggplot(data, aes(x = Weight_mg, y = Binding.Perc, color = factor(Spike), shape = factor(Buffer_nl))) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE, linewidth = 0.5) +  # Add a linear trend line
  labs(title = "Binding Percentage vs Weight, 4 groups",
       x = "Weight (mg)", y = "Binding Percentage") +
   scale_y_continuous(n.breaks = 10) +  
    geom_hline(yintercept = 50, linetype = "dashed", color = "gray", linewidth = 1) +  # Add horizontal line at y = 20
  theme_minimal()
`geom_smooth()` using formula = 'y ~ x'

Version Author Date
b03e143 Paloma 2024-10-16 
# Scatter plot of Binding Percentage vs Weight, 2 groups (Buffer)
ggplot(data, aes(x = Weight_mg, y = Binding.Perc, color = factor(Buffer_nl))) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE, linewidth = 0.5) +  # Add a linear trend line
  labs(title = "Binding Percentage vs Weight, by Buffer",
       x = "Weight (mg)", y = "Binding Percentage") +
   scale_y_continuous(n.breaks = 10) +  
    geom_hline(yintercept = 50, linetype = "dashed", color = "gray", linewidth = 1) +  # Add horizontal line at y = 20
  theme_minimal()
`geom_smooth()` using formula = 'y ~ x'

Version Author Date
b03e143 Paloma 2024-10-16 
# Scatter plot of Binding Percentage vs Weight, 2 groups (Spike)
ggplot(data, aes(x = Weight_mg, y = Binding.Perc, shape = factor(Spike))) +
  geom_point(size = 3, colour = "cyan4") +
  geom_smooth(method = "lm", se = FALSE, linewidth = 0.5, colour = "cyan3") +  # Add a linear trend line
  labs(title = "Binding Percentage vs Weight, by Spike",
       x = "Weight (mg)", y = "Binding Percentage") +
   scale_y_continuous(n.breaks = 10) +  
    geom_hline(yintercept = 50, linetype = "dashed", color = "gray", linewidth = 1) +  # Add horizontal line at y = 20
  theme_minimal()
`geom_smooth()` using formula = 'y ~ x'

Version Author Date
b03e143 Paloma 2024-10-16

Coef. of variation percentage

# Scatterplot of CV Percentage vs Buffer & Spike
ggplot(data, aes(x = factor(Buffer_nl), y = CV.Perc, fill = factor(Spike))) +
  geom_boxplot() +
  labs(title = "Coef. of variation by Buffer and Spike",
       x = "Buffer (nl)", y = "Coef of variation %") +
   scale_y_continuous(n.breaks = 10) +  
  theme_minimal()

# Scatter plot of Binding Percentage vs Weight, 4 groups
ggplot(data, aes(x = Weight_mg, y = CV.Perc, color = factor(Spike), shape = factor(Buffer_nl))) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE, linewidth = 0.5) +  # Add a linear trend line
  labs(title = "Coef. of variation vs Weight, 4 groups",
       x = "Weight (mg)", y = "Coef of variation %") +
   scale_y_continuous(n.breaks = 10) +  
  theme_minimal()
`geom_smooth()` using formula = 'y ~ x'

Deviation from 50% binding

# Scatter plot of Binding Deviation vs Weight, 4 groups
ggplot(data, aes(x = Weight_mg, y = Binding_deviation, color = factor(Spike), shape = factor(Buffer_nl))) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE, linewidth = 0.5) +  # Add a linear trend line
  labs(title = "Binding Deviation vs Weight, 4 groups",
       x = "Weight (mg)", y = "Binding Deviation") +
   scale_y_continuous(n.breaks = 10) +  
  theme_minimal()
`geom_smooth()` using formula = 'y ~ x'

# Scatter plot of Binding Deviation vs Weight, colored by Buffer
ggplot(data, aes(x = Weight_mg, y = Binding_deviation, color = factor(Buffer_nl))) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = FALSE) +  # Add a linear trend line
  labs(title = "Binding Deviation vs Weight, colored by Buffer",
       x = "Weight (mg)", y = "Binding Percentage") +
   scale_y_continuous(n.breaks = 10) +  
  theme_minimal()
`geom_smooth()` using formula = 'y ~ x'

# Scatter plot of Binding Deviation vs Weight, shape by Spike
ggplot(data, aes(x = Weight_mg, y = Binding_deviation, shape = factor(Spike))) +
  geom_point(size = 3, colour = "cyan4") + 
  geom_smooth(method = "lm", se = FALSE, linewidth = 0.5, colour = "cyan3") +  # Add a linear trend line
  labs(title = "Binding Deviation vs Weight, shape by Spike",
       x = "Weight (mg)", y = "Binding Deviation") +
   scale_y_continuous(n.breaks = 10) +  
  theme_minimal()
`geom_smooth()` using formula = 'y ~ x'

Boxplots

Binding percentage

# Boxplot of Binding Percentage vs Buffer & Spike
ggplot(data, aes(x = factor(Buffer_nl), y = Binding.Perc, fill = factor(Spike))) +
  geom_boxplot() +
  labs(title = "Binding Percentage by Buffer and Spike",
       x = "Buffer (nl)", y = "Binding Percentage") +
   scale_y_continuous(n.breaks = 10) +  
      geom_hline(yintercept = 50, linetype = "dashed", color = "gray", linewidth = 1) + 
  theme_minimal()

# Boxplot of Binding Percentage vs Buffer
ggplot(data, aes(x = factor(Buffer_nl), y = Binding.Perc, fill = factor(Buffer_nl))) +
  geom_boxplot() +
  labs(title = "Binding Percentage by Buffer",
       x = "Buffer (nl)", y = "Bindings Percentage") +
   scale_y_continuous(n.breaks = 10) +  
      geom_hline(yintercept = 50, linetype = "dashed", color = "gray", linewidth = 1) + 
  theme_minimal()

# Boxplot of Binding Percentage vs Spike
ggplot(data, aes(x = factor(Spike), y = Binding.Perc, fill = Spike)) +
  geom_boxplot() +
  labs(title = "Binding Percentage by Spike",
       x = "Spike", y = "Binding Percentage") +
   scale_y_continuous(n.breaks = 10) +  
   geom_hline(yintercept = 50, linetype = "dashed", color = "gray", linewidth = 1) +
  theme_minimal()

Coef. of variation percentage

# Boxplot of CV Percentage vs Buffer & Spike
ggplot(data, aes(x = factor(Buffer_nl), y = CV.Perc, fill = factor(Spike))) +
  geom_boxplot() +
  labs(title = "Coef. of variation by Buffer and Spike",
       x = "Buffer (nl)", y = "Coef of variation %") +
   scale_y_continuous(n.breaks = 10) +  
  theme_minimal()

Deviation from 50% binding

# Box plot to visualize distribution of Binding deviation by Buffer and Spike
ggplot(data, aes(x = factor(Buffer_nl), y = Binding_deviation, fill = factor(Spike))) +
  geom_boxplot() +
  labs(title = "Binding deviation by Buffer and Spike",
       x = "Buffer (nl)", y = "Binding deviation") +
   scale_y_continuous(n.breaks = 10) +  
  theme_minimal()

wflow_publish(“./analysis/ELISA_visualizations.Rmd”)


sessionInfo()
R version 4.1.0 (2021-05-18)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.1252 
[2] LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] RColorBrewer_1.1-3 ggplot2_3.5.1      knitr_1.48         dplyr_1.1.2       
[5] workflowr_1.7.1   

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.0  xfun_0.47         bslib_0.8.0       splines_4.1.0    
 [5] lattice_0.20-44   colorspace_2.1-0  vctrs_0.6.5       generics_0.1.3   
 [9] htmltools_0.5.8.1 yaml_2.3.7        mgcv_1.8-35       utf8_1.2.3       
[13] rlang_1.1.0       jquerylib_0.1.4   later_1.3.0       pillar_1.9.0     
[17] glue_1.6.2        withr_3.0.1       lifecycle_1.0.4   stringr_1.5.1    
[21] munsell_0.5.1     gtable_0.3.5      evaluate_1.0.0    labeling_0.4.3   
[25] callr_3.7.6       fastmap_1.1.1     httpuv_1.6.9      ps_1.7.5         
[29] fansi_1.0.4       highr_0.11        Rcpp_1.0.10       promises_1.2.0.1 
[33] scales_1.3.0      cachem_1.0.7      jsonlite_1.8.9    farver_2.1.1     
[37] fs_1.5.2          digest_0.6.31     stringi_1.7.12    processx_3.8.1   
[41] getPass_0.2-2     rprojroot_2.0.4   grid_4.1.0        cli_3.6.1        
[45] tools_4.1.0       magrittr_2.0.3    sass_0.4.9        tibble_3.2.1     
[49] whisker_0.4.1     pkgconfig_2.0.3   Matrix_1.3-3      rmarkdown_2.28   
[53] httr_1.4.7        rstudioapi_0.16.0 R6_2.5.1          nlme_3.1-152     
[57] git2r_0.31.0      compiler_4.1.0