tidyplate
Microtiter plates or microplates have become a standard tool in
analytical research and clinical diagnostic testing laboratories. They
are convenient, high-throughput tools for organizing tissue culture, PCR
tests (such as HIV/ COVID screening), or immunological assays such as
ELISA, RIA and FIA. They offer many advantages over traditional assay
formats including reduced sample and reagent volumes, increased
throughput, and ease of automation. The goal of tidyplate
is to help researchers convert different types of microplates into tidy
dataframes which can be used in data analysis. tidyplate
accepts xlsx and csv files formatted in a specific way as input.
tidyplate supports all types of standard microplate formats
namely: 6-well, 12-well, 24-well, 48-well, 96-well, 384-well, and
1536-well plates.
tidyplate has two functions:
tidy_plate: This function takes the input file (xlsx or csv) and transforms into a tidy dataframe.check_plate: This function checks whether the input file is valid for use withtidy_plate()function.
Installation
To install tidyplate from CRAN:
You can install the development version of tidyplate from GitHub with:
Formating the input data file
This figure demonstrates how to format the 12-well plate input file. Colors are for visualization purposes only.
The input xlsx or csv should be formatted in a specific way:
- Top left corner must hold the name for that plate.
- Column names should be: 1, 2, 3, and so on and so forth.
- Row names should be: A, B, C, and so on and so forth.
- There must be an empty row between each plate.
Example
This is an example which shows you how to use the
tidyplate. If the input file is an xlsx file it reads the
first sheet by default. Users can specify sheet using the
sheet argument for an xlsx file. Users can also specify the
variable name of column where well ids will be stored (defaults to
“well”). Please make sure that well_id argument does not
match individual plate names in the input file.
First check if the input file is valid or not:
library(tidyplate)
file_path <- system.file("extdata", "example_12_well.xlsx", package = "tidyplate")
check_plate(file_path)
#> example_12_well.xlsx: OK; Plate type: 12 wellImport the file as a tidy dataframe:
data <- tidy_plate(file_path)
#> Data: example_12_well.xlsx; Plate type: 12 well plate
head(data)
#> # A tibble: 6 × 4
#> well drug cell_line percent_survived
#> <chr> <chr> <chr> <int>
#> 1 A01 Neomycin HEK293 60
#> 2 A02 Puromycin HEK293 22
#> 3 A03 Neomycin Hela 52
#> 4 A04 Puromycin Hela 18
#> 5 B01 Neomycin HEK293 62
#> 6 B02 Puromycin HEK293 23