ELISA curve fitting using ggplot2 and drc

ggplot2
drc
broom
ELISA
Author

Shubham Dutta

Published

June 8, 2023

Introduction

Here is an example of how to fit and analyse ELISA data using ggplot2, drc, and broom.

library(dplyr)
library(ggplot2)
library(tidyplate)
library(drc)
library(ggsci)

First we import plate data using the tidy_plate function from the tidyplate package:

raw_data <- tidy_plate("data/elisa_example.xlsx")
glimpse(raw_data)
Rows: 50
Columns: 10
$ well                <chr> "A01", "A02", "A03", "A04", "A05", "A06", "B01", "…
$ coat_protein_name   <chr> "sBACE", "sBACE", "sBACE", "sBACE", "sBACE", "sBAC…
$ coat_protein_ug     <dbl> 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, …
$ coat_protein_source <chr> "NS0", "NS0", "NS0", "NS0", "NS0", "NS0", "NS0", "…
$ primary_mab_name    <chr> "aBACE", "aBACE", "aBACE", "aSARS", "aSARS", "aSAR…
$ primary_mab_clone   <chr> "6626.1", "6626.1", "6626.1", "CC6.29", "CC6.29", …
$ primary_mab_conc    <dbl> 10.0000000, 10.0000000, 10.0000000, 10.0000000, 10…
$ secondary_mab_name  <chr> "goat-aHuman", "goat-aHuman", "goat-aHuman", "goat…
$ secondary_mab_dil   <chr> "1to5000", "1to5000", "1to5000", "1to5000", "1to50…
$ od450               <dbl> 1.396, 1.170, 1.299, 1.324, 1.170, 1.299, 1.374, 1…

Prepare data before plotting

blank_data <- raw_data |>
    filter(primary_mab_name == "blank")
mean_blank <- mean(blank_data[["od450"]], na.rm = TRUE)

summary <- raw_data |>
    filter(primary_mab_name != "blank") |>
    mutate(blanked_od = od450 - mean_blank) |>
    group_by(primary_mab_name, primary_mab_conc) |>
    summarise(
      mean_od = mean(blanked_od, na.rm = TRUE),
      mean_sd = sd(blanked_od, na.rm = TRUE),
      .groups = 'drop'
    )
head(summary)
# A tibble: 6 × 4
  primary_mab_name primary_mab_conc mean_od mean_sd
  <chr>                       <dbl>   <dbl>   <dbl>
1 aBACE                     0.00244   0.138  0.0797
2 aBACE                     0.00977   0.214  0.0626
3 aBACE                     0.0391    0.395  0.0644
4 aBACE                     0.156     0.740  0.0531
5 aBACE                     0.625     1.11   0.0358
6 aBACE                     2.5       1.28   0.0486

The final plot

theme_set(theme_bw(base_size = 20))
ggplot(summary, aes(x = primary_mab_conc, 
                    y = mean_od, 
                    group = primary_mab_name, 
                    color = primary_mab_name,
                    shape = primary_mab_name)) +
  geom_point(size = 3, stroke = 1.5, show.legend = TRUE) +
  geom_smooth(method = drc::drm, 
              method.args = list(fct = drc::L.4()),
              se = FALSE, linewidth = 1, 
              show.legend = FALSE) +
  geom_errorbar(aes(ymin = mean_od - mean_sd,
                    ymax = mean_od + mean_sd),
                width = 0.1, show.legend = FALSE) +
  scale_x_log10() +
  scale_color_npg(name = NULL) +
  scale_shape(name = NULL) +
  labs(x = "[Antibody] (Log)",
       y = expression(OD[450])) +
  theme(legend.position = c(0.15, 0.8),
        legend.box.background = element_rect())