ELISA curve fitting using ggplot2

Introduction

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

library(dplyr)
library(ggplot2)
library(tidyplate)
library(drc)
library(ggtext)

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

# Converts microwell shaped data to tidy data
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> "6626.1000000000004", "6626.1000000000004", "6626.…
$ 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

# Extracting blank values and calculating their mean
blank_data <- raw_data |>
    dplyr::filter(primary_mab_name == "blank")
mean_blank <- mean(blank_data[["od450"]], na.rm = TRUE)

# Blanking OD values and summarizing their mean and standard deviation
summary <- raw_data |>
    dplyr::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),
      sd_od = sd(blanked_od, na.rm = TRUE),
      .groups = 'drop'
    )
head(summary)

# A tibble: 6 × 4
  primary_mab_name primary_mab_conc mean_od  sd_od
  <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

Compute EC₅₀ for each antibody

# Helper function to fit 4PL and extract EC50 + CI
fit_one_ab <- function(df) {
  fit <- drm(
    mean_od ~ primary_mab_conc,
    data = df,
    fct  = LL.4()
  )

  ed <- ED(fit, 50, interval = "delta")
  pars <- coef(fit)
  bottom <- pars["c:(Intercept)"]
  top    <- pars["d:(Intercept)"]

  y_ec50 <- bottom + (top - bottom) / 2

  tibble(
    antibody = df$antibody[1],
    ec50     = ed[1, "Estimate"],
    ec50_se  = ed[1, "Std. Error"],
    ec50_low = ed[1, "Lower"],
    ec50_up  = ed[1, "Upper"]
  )
}

ec50_df <- summary |>
  group_by(primary_mab_name) |>
  group_modify(~ fit_one_ab(.x)) |>
  ungroup()


Estimated effective doses

       Estimate Std. Error    Lower    Upper
e:1:50 0.134427   0.016674 0.081364 0.187490


Estimated effective doses

       Estimate Std. Error    Lower    Upper
e:1:50 0.448393   0.045076 0.304942 0.591845

print(ec50_df)

# A tibble: 2 × 5
  primary_mab_name  ec50 ec50_se ec50_low ec50_up
  <chr>            <dbl>   <dbl>    <dbl>   <dbl>
1 aBACE            0.134  0.0167   0.0814   0.187
2 aSARS            0.448  0.0451   0.305    0.592

The final plot

I am making this plot a bit fancy.

colors <- c("aBACE" = "#00a5a6", "aSARS" = "#e03d90")
light_colors <- prismatic::clr_lighten(colors, 0.7)

ec50_label <- ec50_df |>
  mutate(
    label = paste0(
      primary_mab_name, ": EC~50~ = ", signif(ec50, 3)
    ),
    x = 0.015,
    y = c(1.3, 1.2)
  )

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,
    fill = primary_mab_name
  )
) +
  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 - sd_od, ymax = mean_od + sd_od),
    width = 0.1,
    show.legend = FALSE
  ) +
  geom_point(size = 3, stroke = 1.5) +
  geom_textbox(
    data = ec50_label,
    aes(x, y, label = label),
    size = 6,
    face = "bold",
    width = 0.35,
    box.color = NA,
    fill = NA
  ) +
  scale_x_log10(labels = scales::label_log()) +
  scale_y_continuous(
    limits = c(0, 1.5), 
    n.breaks = 7, 
    expand = expansion(0)
  ) +
  scale_color_manual(name = NULL, values = colors) +
  scale_fill_manual(name = NULL, values = light_colors) +
  scale_shape_manual(name = NULL, values = c(21, 22)) +
  labs(
    x = "Antibody (\U03BCg/mL), Log",
    y = "OD~450~ (Mean \U00B1 SE)",
    title = "<span style='color:#00a5a6'>anti-BACE</span> is more potent compared to<span style='color:#e03d90'> anti-SARS</span> antibody"
  ) +
  theme(
    legend.position = "none",
    plot.title = element_markdown(size = 20),
    axis.title.y = element_markdown(),
    panel.grid = element_blank()
  )

Introduction

Prepare data before plotting

Compute EC50 for each antibody

The final plot

Compute EC₅₀ for each antibody