ELISA_Visualizations
Paloma
2024-10-16
Last updated: 2024-10-17
Checks: 6 1
Knit directory: test-3/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 98024d2 | Paloma | 2024-10-16 | Build site. |
| Rmd | 1953f30 | Paloma | 2024-10-16 | wflow_publish("./analysis/ELISA_visualizations.Rmd") |
| html | b08a78b | Paloma | 2024-10-16 | Build site. |
| Rmd | 0d06b46 | Paloma | 2024-10-16 | wflow_publish("./analysis/ELISA_visualizations.Rmd") |
| Rmd | b03e143 | Paloma | 2024-10-16 | creating plots |
| html | b03e143 | Paloma | 2024-10-16 | creating plots |
| Rmd | 2cbdcc9 | Paloma | 2024-10-16 | merged data, cleaned, visualized some results |
| html | 2cbdcc9 | Paloma | 2024-10-16 | merged data, cleaned, visualized some results |
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 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'
# 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'
# 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'
Boxplots
# 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
# 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()
| Version | Author | Date |
|---|---|---|
| b08a78b | Paloma | 2024-10-16 |
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 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'
| Version | Author | Date |
|---|---|---|
| b08a78b | Paloma | 2024-10-16 |
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))| 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 | 1197 | 3.690 | 44.20 | 31.30 | 37.1 | 250 | 0 | NA | 0.0 |
| 21 | 31 | H9 | 0.701 | 51.2 | 1070 | 1149 | 9.750 | 112.00 | 79.20 | 21.2 | 60 | 0 | NA | 1.2 |
| 24 | 34 | C11 | 0.687 | 51.7 | 1117 | 1124 | 0.867 | 9.74 | 6.89 | 35.5 | 250 | 0 | NA | 1.7 |
| 23 | 33 | B11 | 0.696 | 52.3 | 1087 | 1100 | 1.730 | 19.10 | 13.50 | 33.8 | 250 | 0 | NA | 2.3 |
| 25 | 36 | E11 | 0.695 | 53.2 | 1090 | 1062 | 3.680 | 39.10 | 27.60 | 31.2 | 250 | 0 | NA | 3.2 |
| 15 | 27 | E9 | 0.610 | 46.0 | 1417 | 1393 | 2.430 | 33.80 | 23.90 | 21.6 | 60 | 0 | NA | 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) + # 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()
| Version | Author | Date |
|---|---|---|
| b08a78b | Paloma | 2024-10-16 |
# 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()
# 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, 2 groups",
x = "Weight (mg)", y = "Binding Deviation") +
scale_y_continuous(n.breaks = 10) +
theme_minimal()
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()
| Version | Author | Date |
|---|---|---|
| b08a78b | Paloma | 2024-10-16 |
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
loaded via a namespace (and not attached):
[1] Rcpp_1.0.10 highr_0.11 pillar_1.9.0 compiler_4.1.0
[5] bslib_0.8.0 later_1.3.0 jquerylib_0.1.4 git2r_0.31.0
[9] workflowr_1.7.1 tools_4.1.0 digest_0.6.31 lattice_0.20-44
[13] nlme_3.1-152 gtable_0.3.5 jsonlite_1.8.9 evaluate_1.0.0
[17] lifecycle_1.0.4 tibble_3.2.1 mgcv_1.8-35 pkgconfig_2.0.3
[21] rlang_1.1.0 Matrix_1.3-3 cli_3.6.1 rstudioapi_0.16.0
[25] yaml_2.3.7 xfun_0.47 fastmap_1.1.1 withr_3.0.1
[29] stringr_1.5.1 generics_0.1.3 fs_1.5.2 vctrs_0.6.5
[33] sass_0.4.9 grid_4.1.0 tidyselect_1.2.0 rprojroot_2.0.4
[37] glue_1.6.2 R6_2.5.1 fansi_1.0.4 rmarkdown_2.28
[41] farver_2.1.1 magrittr_2.0.3 whisker_0.4.1 splines_4.1.0
[45] scales_1.3.0 promises_1.2.0.1 htmltools_0.5.8.1 colorspace_2.1-0
[49] httpuv_1.6.9 labeling_0.4.3 utf8_1.2.3 stringi_1.7.12
[53] munsell_0.5.1 cachem_1.0.7