Dosis-response curve
Sem, Elif, Qiana, Nardos, Pauline en Cynthia
2026-04-10
Dataverwerking
Laad de paketten
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## chisq.test, fisher.testlibrary(skimr)Gebruik de set seed functie
set.seed(123)Bepaal concentraties en bètablokker
concentratie <- c(0, 1, 2, 3, 4, 5)
bètablokker <- "Metoprolol"Vul de gemiddelde hartslag in bij elk meetpunt
Metoprolol_means <- c(
"0" = 347,
"1" = 112,
"2" = 129,
"3" = 84,
"4" = 76,
"5" = 100
)
Metoprolol_means## 0 1 2 3 4 5
## 347 112 129 84 76 100Maak een dataset
sim_data <- expand.grid(
Compound = bètablokker,
Concentration = concentratie,
Replicate = 1:3,
stringsAsFactors = FALSE
)
sim_data## Compound Concentration Replicate
## 1 Metoprolol 0 1
## 2 Metoprolol 1 1
## 3 Metoprolol 2 1
## 4 Metoprolol 3 1
## 5 Metoprolol 4 1
## 6 Metoprolol 5 1
## 7 Metoprolol 0 2
## 8 Metoprolol 1 2
## 9 Metoprolol 2 2
## 10 Metoprolol 3 2
## 11 Metoprolol 4 2
## 12 Metoprolol 5 2
## 13 Metoprolol 0 3
## 14 Metoprolol 1 3
## 15 Metoprolol 2 3
## 16 Metoprolol 3 3
## 17 Metoprolol 4 3
## 18 Metoprolol 5 3Sorteer op concentratie
sim_data <- sim_data %>%
mutate(
Concentration = as.numeric(Concentration),
gemiddelde_hartslag = Metoprolol_means[as.character(Concentration)]
) %>%
arrange(Concentration)
sim_data## Compound Concentration Replicate gemiddelde_hartslag
## 1 Metoprolol 0 1 347
## 2 Metoprolol 0 2 347
## 3 Metoprolol 0 3 347
## 4 Metoprolol 1 1 112
## 5 Metoprolol 1 2 112
## 6 Metoprolol 1 3 112
## 7 Metoprolol 2 1 129
## 8 Metoprolol 2 2 129
## 9 Metoprolol 2 3 129
## 10 Metoprolol 3 1 84
## 11 Metoprolol 3 2 84
## 12 Metoprolol 3 3 84
## 13 Metoprolol 4 1 76
## 14 Metoprolol 4 2 76
## 15 Metoprolol 4 3 76
## 16 Metoprolol 5 1 100
## 17 Metoprolol 5 2 100
## 18 Metoprolol 5 3 100Bereken gesimuleerde hartslag
sim_data <- sim_data %>%
mutate(
Heart_Rate = round(
rnorm(nrow(.), mean = as.numeric(gemiddelde_hartslag), sd = 3),
1
)
)
sim_data## Compound Concentration Replicate gemiddelde_hartslag Heart_Rate
## 1 Metoprolol 0 1 347 345.3
## 2 Metoprolol 0 2 347 346.3
## 3 Metoprolol 0 3 347 351.7
## 4 Metoprolol 1 1 112 112.2
## 5 Metoprolol 1 2 112 112.4
## 6 Metoprolol 1 3 112 117.1
## 7 Metoprolol 2 1 129 130.4
## 8 Metoprolol 2 2 129 125.2
## 9 Metoprolol 2 3 129 126.9
## 10 Metoprolol 3 1 84 82.7
## 11 Metoprolol 3 2 84 87.7
## 12 Metoprolol 3 3 84 85.1
## 13 Metoprolol 4 1 76 77.2
## 14 Metoprolol 4 2 76 76.3
## 15 Metoprolol 4 3 76 74.3
## 16 Metoprolol 5 1 100 105.4
## 17 Metoprolol 5 2 100 101.5
## 18 Metoprolol 5 3 100 94.1Behoud de definitieve kolommen in de dataframe
sim_data <- sim_data %>%
dplyr::select(Compound, Concentration, Replicate, Heart_Rate)
sim_data## Compound Concentration Replicate Heart_Rate
## 1 Metoprolol 0 1 345.3
## 2 Metoprolol 0 2 346.3
## 3 Metoprolol 0 3 351.7
## 4 Metoprolol 1 1 112.2
## 5 Metoprolol 1 2 112.4
## 6 Metoprolol 1 3 117.1
## 7 Metoprolol 2 1 130.4
## 8 Metoprolol 2 2 125.2
## 9 Metoprolol 2 3 126.9
## 10 Metoprolol 3 1 82.7
## 11 Metoprolol 3 2 87.7
## 12 Metoprolol 3 3 85.1
## 13 Metoprolol 4 1 77.2
## 14 Metoprolol 4 2 76.3
## 15 Metoprolol 4 3 74.3
## 16 Metoprolol 5 1 105.4
## 17 Metoprolol 5 2 101.5
## 18 Metoprolol 5 3 94.1Print de eerste paar rijen
print(head(sim_data))## Compound Concentration Replicate Heart_Rate
## 1 Metoprolol 0 1 345.3
## 2 Metoprolol 0 2 346.3
## 3 Metoprolol 0 3 351.7
## 4 Metoprolol 1 1 112.2
## 5 Metoprolol 1 2 112.4
## 6 Metoprolol 1 3 117.1Gebruik de ‘janitor’ functie
data_clean <- sim_data |>
janitor::clean_names() ## remove capitals etc.Voor
names(sim_data)## [1] "Compound" "Concentration" "Replicate" "Heart_Rate"Na
names(data_clean)## [1] "compound" "concentration" "replicate" "heart_rate"Analyseer de data
sim_data |>
skimr::skim()| Name | sim_data |
| Number of rows | 18 |
| Number of columns | 4 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| numeric | 3 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| Compound | 0 | 1 | 10 | 10 | 0 | 1 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Concentration | 0 | 1 | 2.50 | 1.76 | 0.0 | 1.00 | 2.5 | 4.00 | 5.0 | ▇▃▃▃▃ |
| Replicate | 0 | 1 | 2.00 | 0.84 | 1.0 | 1.00 | 2.0 | 3.00 | 3.0 | ▇▁▇▁▇ |
| Heart_Rate | 0 | 1 | 141.77 | 96.45 | 74.3 | 85.75 | 108.8 | 126.48 | 351.7 | ▇▁▁▁▂ |
Maak een scatterplot om de trend in de data waar te nemen
scatterplot_metoprolol <- data_clean |>
ggplot(aes(
x = concentration,
y = heart_rate
)) +
geom_point(aes(colour = compound), position = "jitter") +
theme_bw() +
labs(
title = "Scatterplot hartslag per minuut tegen metoprolol concentratie",
x = "Hartslag per minuut",
y = "Concentratie metoprolol (mg/ml)"
)
scatterplot_metoprolol
Figuur 1: scatterplot data.
Maken dose-response curve
Geef een dosis-response model vorm
model_metoprolol <- drm(
heart_rate ~ concentration,
data = data_clean,
fct = LL.4()
)Ontwerp een vloeiende lijn voor de curve
newdata <- data.frame(
concentration = seq(0, 5, length.out = 100)
)Bereken voorspelde hartslagwaarden o.b.v. het model
predictions <- tibble(
concentration = newdata$concentration,
heart_rate = predict(model_metoprolol, newdata = newdata)
)
predictions## # A tibble: 100 × 2
## concentration heart_rate
## <dbl> <dbl>
## 1 0 348.
## 2 0.0505 181.
## 3 0.101 167.
## 4 0.152 159.
## 5 0.202 153.
## 6 0.253 149.
## 7 0.303 145.
## 8 0.354 142.
## 9 0.404 139.
## 10 0.455 136.
## # ℹ 90 more rowsResultaten
Plot de dosis-response curve
dosis_response_curve <- ggplot() +
geom_point(
data = data_clean,
aes(x = concentration, y = heart_rate),
position = position_jitter(width = 0.1, height = 0)
) +
geom_line(
data = predictions,
aes(x = concentration, y = heart_rate),
linewidth = 1
) +
theme_bw() +
labs(
title = "Dosis-response curve voor Metoprol",
x = "Concentratie Metoprolol (mg/mL)",
y = "Hartslag per minuut",
)
dosis_response_curve