ELISA_Visualizations
Paloma
2024-10-16
Last updated: 2024-10-18
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Knit directory: test-3/
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Introduction
Here I use results from ELISA test-3. I removed the samples that have a Coef of variation higher than 15%.
Binding percentage
Scatterplots
# 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 trend line
geom_hline(yintercept = 50, linetype = "dashed",
color = "gray", linewidth = 1) + # Add horizontal line
labs(title = "Binding Percentage vs Weight, 4 groups",
x = "Weight (mg)",
y = "Binding Percentage") +
scale_y_continuous(n.breaks = 10) +
theme_minimal()
| Version | Author | Date |
|---|---|---|
| a59d14d | Paloma | 2024-10-18 |
# 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 trend line
geom_hline(yintercept = 50, linetype = "dashed",
color = "gray", linewidth = 1) + # Add horizontal line
labs(title = "Binding Percentage vs Weight, by Buffer",
x = "Weight (mg)", y = "Binding Percentage") +
scale_y_continuous(n.breaks = 10) +
theme_minimal()
| Version | Author | Date |
|---|---|---|
| a59d14d | Paloma | 2024-10-18 |
# 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 = "brown") +
geom_smooth(method = "lm", se = FALSE, linewidth = 0.5,
colour = "cyan3") + # Add a linear trend line
geom_hline(yintercept = 50, linetype = "dashed",
color = "gray", linewidth = 1) + # Add horizontal line at y = 20
labs(title = "Binding Percentage vs Weight, by Spike",
x = "Weight (mg)", y = "Binding Percentage") +
scale_y_continuous(n.breaks = 10) +
theme_minimal()
| Version | Author | Date |
|---|---|---|
| a59d14d | Paloma | 2024-10-18 |
Boxplots
# Boxplot of Binding Percentage vs Buffer & Spike
ggplot(data, aes(x = factor(Buffer_nl),
y = Binding.Perc,
fill = factor(Spike))) +
geom_boxplot() +
geom_hline(yintercept = 50, linetype = "dashed", color = "gray", linewidth = 1) +
labs(title = "Binding Percentage by Buffer and Spike",
x = "Buffer (nl)", y = "Binding Percentage") +
scale_y_continuous(n.breaks = 10) +
theme_minimal()
# Boxplot of Binding Percentage vs Buffer
ggplot(data, aes(x = factor(Buffer_nl),
y = Binding.Perc,
fill = factor(Buffer_nl))) +
geom_boxplot() +
geom_hline(yintercept = 50, linetype = "dashed", color = "gray", linewidth = 1) +
labs(title = "Binding Percentage by Buffer",
x = "Buffer (nl)", y = "Bindings Percentage") +
scale_y_continuous(n.breaks = 10) +
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
# 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()
Scatterplot
# Scatterplot CV 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 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
Calculating deviation from 50% binding
Over 80% and under 20% bindings are considered not reliable. Here I calculate a “binding” deviation, or a distance from what I should aim for: 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(head(sorted_data[,c("Sample", "Wells", "Binding.Perc", "Binding_deviation")]))| Sample | Wells | Binding.Perc | Binding_deviation | |
|---|---|---|---|---|
| 22 | 32 | A11 | 50.0 | 0.0 |
| 21 | 31 | H9 | 51.2 | 1.2 |
| 24 | 34 | C11 | 51.7 | 1.7 |
| 23 | 33 | B11 | 52.3 | 2.3 |
| 25 | 36 | E11 | 53.2 | 3.2 |
| 15 | 27 | E9 | 46.0 | 4.0 |
Scatterplots
# 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) +
labs(title = "Binding Deviation vs Weight, 4 groups",
x = "Weight (mg)", y = "Binding Deviation") +
scale_y_continuous(n.breaks = 10) +
theme_minimal()
# 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, by Buffer, 2 groups",
x = "Weight (mg)", y = "Binding Percentage") +
scale_y_continuous(n.breaks = 10) +
theme_minimal()
| Version | Author | Date |
|---|---|---|
| a59d14d | Paloma | 2024-10-18 |
# 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") +
labs(title = "Binding Deviation vs Weight, shape by Spike, 2 groups",
x = "Weight (mg)", y = "Binding Deviation") +
scale_y_continuous(n.breaks = 10) +
theme_minimal()
| Version | Author | Date |
|---|---|---|
| a59d14d | Paloma | 2024-10-18 |
Boxplot
# 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()
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