Basic example In designit: Blocking and Randomization for Experimental Design

```knitr::opts_chunk\$set(
collapse = TRUE,
comment = "#>"
)
```
```library(designit)
library(ggplot2)
library(dplyr)
library(tidyr)
```

Plate layout with two factors

The samples

Samples of a 2-condition in-vivo experiment are to be placed on 48 well plates.

These are the conditions

```# conditions to use
conditions <- data.frame(
group = c(1, 2, 3, 4, 5),
treatment = c(
"vehicle", "TRT1", "TRT2",
"TRT1", "TRT2"
),
dose = c(0, 25, 25, 50, 50)
)

gt::gt(conditions)
```

We will have 3 animals per groups with 4 replicates each

```# sample table (2 animals per group with 3 replicates)
n_reps <- 4
n_animals <- 3
animals <- bind_rows(replicate(n_animals, conditions, simplify = FALSE),
.id = "animal"
)
samples <- bind_rows(replicate(n_reps, animals, simplify = FALSE),
.id = "replicate"
) |>
mutate(
SampleID = paste0(treatment, "_", animal, "_", replicate),
AnimalID = paste0(treatment, "_", animal)
) |>
mutate(dose = factor(dose))

samples |>
gt::gt()
```

Plate layout requirements

Corner wells of the plates should be left empty. This means on a 48 well plate we can place 44 samples. Since we have `r nrow(samples)` samples, they will fit on `r ceiling(nrow(samples)/44)` plates

```n_samp <- nrow(samples)
n_loc_per_plate <- 48 - 4
n_plates <- ceiling(n_samp / n_loc_per_plate)

exclude_wells <- expand.grid(plate = seq(n_plates), column = c(1, 8), row = c(1, 6))
```

Setting up a Batch container

Create a BatchContainer object that provides all possible locations

```bc <- BatchContainer\$new(
dimensions = c("plate" = n_plates, "column" = 8, "row" = 6),
exclude = exclude_wells
)
bc

bc\$n_locations
bc\$exclude
```

Moving samples

Use random assignment function to place samples to plate locations

```bc <- assign_random(bc, samples)

bc\$get_samples()
bc\$get_samples(remove_empty_locations = TRUE)
```

Plot of the result using the `plot_plate` function

```plot_plate(bc,
plate = plate, column = column, row = row,
.color = treatment, .alpha = dose
)
```

To not show empty wells, we can directly plot the sample table as well

```plot_plate(bc\$get_samples(remove_empty_locations = TRUE),
plate = plate, column = column, row = row,
.color = treatment, .alpha = dose
)
```

To move individual samples or manually assigning all locations we can use the `batchContainer\$move_samples()` method

To swap two or more samples use:

Warning: This will change your BatchContainer in-place.

```bc\$move_samples(src = c(1L, 2L), dst = c(2L, 1L))

plot_plate(bc\$get_samples(remove_empty_locations = TRUE),
plate = plate, column = column, row = row,
.color = treatment, .alpha = dose
)
```

To assign all samples in one go, use the option `location_assignment`.

Warning: This will change your BatchContainer in-place.

The example below orders samples by ID and adds the empty locations afterwards

```bc\$move_samples(
location_assignment = c(
1:nrow(samples),
rep(NA, (bc\$n_locations - nrow(samples)))
)
)

plot_plate(bc\$get_samples(remove_empty_locations = TRUE, include_id = TRUE),
plate = plate, column = column, row = row,
.color = .sample_id
)
```

Run an optimization

The optimization procedure is invoked with e.g. `optimize_design`. Here we use a simple shuffling schedule: swap 10 samples for 100 times, then swap 2 samples for 400 times.

To evaluate how good a layout is, we need a scoring function.

This function will assess how well treatment and dose are balanced across the two plates.

```bc <- optimize_design(bc,
scoring = osat_score_generator(
batch_vars = "plate",
feature_vars = c("treatment", "dose")
),
# shuffling schedule
n_shuffle = c(rep(10, 200), rep(2, 400))
)
```

Development of the score can be viewed with

```bc\$plot_trace()
```

The layout after plate batching looks the following

```plot_plate(bc\$get_samples(remove_empty_locations = TRUE),
plate = plate, column = column, row = row,
.color = treatment, .alpha = dose
)
```

Looking at treatment, we see it's evenly distributed across the plates

```ggplot(
bc\$get_samples(remove_empty_locations = TRUE),
aes(x = treatment, fill = treatment)
) +
geom_bar() +
facet_wrap(~plate)
```

Customizing the plate layout

To properly distinguish between empty and excluded locations one can do the following.

• Supply the BatchContainer directly
• set `add_excluded = TRUE`, set `rename_empty = TRUE`
• supply a custom color palette
• excluded wells have NA values and can be colored with `na.value`
```color_palette <- c(
TRT1 = "blue", TRT2 = "purple",
vehicle = "orange", empty = "white"
)

plot_plate(bc,
plate = plate, column = column, row = row,
.color = treatment, .alpha = dose,
add_excluded = TRUE, rename_empty = TRUE
) +
scale_fill_manual(values = color_palette, na.value = "darkgray")
```

To remove all empty wells from the plot, hand the pruned sample list. to plot_plate rather than the whole BatchContainer. You can still assign your own colors.

```plot_plate(bc\$get_samples(remove_empty_locations = TRUE),
plate = plate, column = column, row = row,
.color = treatment, .alpha = dose
) +
scale_fill_viridis_d()
```

Note: removing all empty and excluded wells will lead to omitting completely empty rows or columns!

```plot_plate(bc\$get_samples(remove_empty_locations = TRUE) |>
filter(column != 2),
plate = plate, column = column, row = row,
.color = treatment, .alpha = dose
) +
scale_fill_viridis_d()
```

Try the designit package in your browser

Any scripts or data that you put into this service are public.

designit documentation built on May 29, 2024, 12:04 p.m.