easyplater is a tool for generating microplate manifests while decoupling clinical data from plate location effects.
In short, easyplater is an easy to use algorithm for generating 96-well plate designs without confounding clinical variables with plate location effects. Simply input clinical data and user-assigned clinical variable weights, and easyplater outputs the most deconvolved plate design it finds in plain text and R Markdown formats.
You can install the development version of easyplater from
GitHub with:
# install.packages("remotes")
remotes::install_github("IMCM-OX/easyplater")library(easyplater)An example input manifest data frame comes loaded with easyplater.
input_manifest
#> # A tibble: 423 × 9
#> SampleID Cohort Group Sex Age plate column row well
#> <dbl> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr>
#> 1 0 C2 D5 2 43 plate 1 Column 1 A A1
#> 2 1 C1 D7 1 66 plate 1 Column 1 B B1
#> 3 2 C1 D7 1 68 plate 1 Column 1 C C1
#> 4 3 C2 D7 2 77 plate 1 Column 1 D D1
#> 5 4 C1 D1 2 54 plate 1 Column 1 E E1
#> 6 5 C1 D7 2 75 plate 1 Column 1 F F1
#> 7 6 C2 D1 2 65 plate 1 Column 1 G G1
#> 8 7 C2 D7 1 58 plate 1 Column 1 H H1
#> 9 8 C2 D1 2 67 plate 1 Column 2 A A2
#> 10 9 C2 D8 2 61 plate 1 Column 2 B B2
#> # ℹ 413 more rowsYou can run easyplater on a multi-plate manifest data frame in one step.
easyplater_manifest <- make_easyplater_design(
manifest_df = input_manifest,
columns_for_scoring = c("Cohort","Group","Sex","AgeGroup"),
column_weights = c(5, 5, 10, 4),
cols_to_categorize = list(c("Age", 10, NULL, "AgeGroup")),
imbalance_fixer = list(TRUE, "Group", list("D1", "HC1", "D7", "D8"), 3),
plate_size = 96
)
#> [1] "[:::] plate 1 [:::]"
#> [1] "Getting and formatting plate data from manifest."
#> [1] "Allocating similar samples to distal wells."
#> [1] "Performing sample switching search."
#> [1] "Storing the easyplater plate design in a data frame."
#> [1] "[:::] plate 2 [:::]"
#> [1] "Getting and formatting plate data from manifest."
#> [1] "Allocating similar samples to distal wells."
#> [1] "Performing sample switching search."
#> [1] "Storing the easyplater plate design in a data frame."
#> [1] "[:::] plate 3 [:::]"
#> [1] "Getting and formatting plate data from manifest."
#> [1] "Allocating similar samples to distal wells."
#> [1] "Performing sample switching search."
#> [1] "Storing the easyplater plate design in a data frame."
#> [1] "[:::] plate 4 [:::]"
#> [1] "Getting and formatting plate data from manifest."
#> [1] "Allocating similar samples to distal wells."
#> [1] "Performing sample switching search."
#> [1] "Storing the easyplater plate design in a data frame."
#> [1] "[:::] plate 5 [:::]"
#> [1] "Getting and formatting plate data from manifest."
#> [1] "Allocating similar samples to distal wells."
#> [1] "Performing sample switching search."
#> [1] "Storing the easyplater plate design in a data frame."The output is a tibble with the plate locations deconvolved from the
columns_for_scoring.
easyplater_manifest
#> # A tibble: 480 × 9
#> SampleID Cohort Group Sex AgeGroup plate column row well
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 13 C1 D1 2 9 plate 1 Column 1 A A1
#> 2 36 C1 D8 1 9 plate 1 Column 1 B B1
#> 3 64 C1 HC2 1 9 plate 1 Column 1 C C1
#> 4 63 C2 D7 2 8 plate 1 Column 1 D D1
#> 5 58 C1 D7 2 9 plate 1 Column 1 E E1
#> 6 66 C2 D8 2 9 plate 1 Column 1 F F1
#> 7 74 C1 D1 2 7 plate 1 Column 1 G G1
#> 8 47 C1 HC1 1 6 plate 1 Column 1 H H1
#> 9 8 C2 D1 2 7 plate 1 Column 2 A A2
#> 10 80 C2 HC1 2 6 plate 1 Column 2 B B2
#> # ℹ 470 more rowsWe can write the resulting manifest and plate layouts to an excel spreadsheet.
write_manifest_excel(easyplater_manifest, "easyplater_output.xlsx")We can then use a function exported from the OlinkAnalyze package to display a single plate layout…
## Subset manifest to a single plate.
plate1_manifest <- easyplater_manifest[easyplater_manifest$plate == "plate 1",]
## Plot plate layout
olink_displayPlateLayout(data = plate1_manifest, fill.color = "Group", include.label = TRUE)
or generate an interactive html report of plate layouts colored by deconvolved variables
write_plate_layout_html(easyplater_manifest)
# An example input manifest data frame comes loaded with easyplater
input_manifest
#> # A tibble: 423 × 9
#> SampleID Cohort Group Sex Age plate column row well
#> <dbl> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr>
#> 1 0 C2 D5 2 43 plate 1 Column 1 A A1
#> 2 1 C1 D7 1 66 plate 1 Column 1 B B1
#> 3 2 C1 D7 1 68 plate 1 Column 1 C C1
#> 4 3 C2 D7 2 77 plate 1 Column 1 D D1
#> 5 4 C1 D1 2 54 plate 1 Column 1 E E1
#> 6 5 C1 D7 2 75 plate 1 Column 1 F F1
#> 7 6 C2 D1 2 65 plate 1 Column 1 G G1
#> 8 7 C2 D7 1 58 plate 1 Column 1 H H1
#> 9 8 C2 D1 2 67 plate 1 Column 2 A A2
#> 10 9 C2 D8 2 61 plate 1 Column 2 B B2
#> # ℹ 413 more rows
# Note also that an output manifest data frame also comes loaded with easyplater, which should be identical to the output from make_easyplater_design() above.
output_manifest
#> # A tibble: 480 × 9
#> SampleID Cohort Group Sex AgeGroup plate column row well
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 13 C1 D1 2 9 plate 1 Column 1 A A1
#> 2 36 C1 D8 1 9 plate 1 Column 1 B B1
#> 3 64 C1 HC2 1 9 plate 1 Column 1 C C1
#> 4 63 C2 D7 2 8 plate 1 Column 1 D D1
#> 5 58 C1 D7 2 9 plate 1 Column 1 E E1
#> 6 66 C2 D8 2 9 plate 1 Column 1 F F1
#> 7 74 C1 D1 2 7 plate 1 Column 1 G G1
#> 8 47 C1 HC1 1 6 plate 1 Column 1 H H1
#> 9 8 C2 D1 2 7 plate 1 Column 2 A A2
#> 10 80 C2 HC1 2 6 plate 1 Column 2 B B2
#> # ℹ 470 more rows