This tutorial explains how to use the Well Plate Maker package.
To generate plate maps, the WPM uses an algorithm inspired from the backtracking algorithm. More precisely, WPM loops on the following actions until all of the samples are given a correct location:
This process allows for an experimental design by block randomization.
There are two ways to use the WPM
:
Important: Even in case of command line use, we strongly recommend to read the section about the shiny app section, as this is where all terms and concepts are detailed.
| Input Format | Command line | WPM app | | --------------------- |:------------:| :------:| | CSV | yes | yes | | ExpressionSet | yes | no | | SummarizedExperiment | yes | no | | MSnSet | yes | no |
Make sure you are using a recent version of R ($\geq 4.0.0$). For Windows users who do not have the Edge browser, we recommend using the Chrome browser rather than Internet Explorer.
From GitHub (consider it a devel version):
devtools::install_github("HelBor/wpm", build_vignettes=TRUE)
From Bioconductor (release, stable version):
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("wpm")
Instructions can also be found on the Bioconductor page
library(wpm)
Whether you use RStudio or simply work in an R console, the procedure remains the same to launch the shiny app:
library(wpm) wpm()
If everything is in order, a new window will open in your default browser.
If not, find the line written in the R console that looks like
Listening on http://127.0.0.1:8000
, and paste the URL in your web browser.
WPM has 4 main tabs: Home, Parameters, Results and Help.
This tab briefly presents the aim of the app, shows the last package version, explains how to support our work by citing the associated article and gives contact information.
Overall the page is organized in two sections.
The one on the left hand side contains all the configuration steps. It is divided into 7 main steps, detailed below. It is of the utmost importance to correctly specify all the constraints for generating the desired plate maps.
The one on the right hand side summarizes the input parameters (tuned along the 7 steps of the left panel) as well as the chosen (empty) plate layout. The right section is automatically updated each time a parameter is changed in the left section.
First, you need to upload a Comma-separated values (.CSV) or a text (.txt) file. This file contains at least one piece of information: the list of the sample names.
knitr::kable(data.frame("Sample" = c("s1","s2","s3","s4")))
It is also possible to provide a file containing several other variables describing the data, as in the example below:
knitr::kable( data.frame("Sample" = c("s1","s2","s3","s4"), "Type" = c("A","A","B","C"), "Treatment" = c("trt1","tr1","Ctrl","Ctrl")) )
IMPORTANT Please make sure the data in the CSV file respect the following SPECIFIC ORDER of columns: Sample names in the first column, and other variables in the other columns, like the example below (if there are rownames, then the Samples Column must be the second in the file.):
Sample;Type;Treatment s1;A;trt1 s2;A;trt1 s3;B;Ctrl s4;C;Ctrl
If this is your first time using the WPM, we recommend that you test the capabilities of the WPM using the demo dataset ("Load the demo dataset" tab).
Second, you have to specify if there are quotes in your file or not (If you are using the demo dataset, this is not a requested parameter.):
The default is none, meaning that there is no " or ' characters in your file. If you select the appropriate quote, then you will be able to:
Then, you can select one of the variables that you want to use as the grouping
factor for WPM.
This column will be renamed "Group" in the final dataset.
The names you give to columns in your CSV file do not matter, because the WPM will create a new dataset having 3 fields: "Sample" , "Group" and "ID".
You will see your dataset on the right hand side of the window, as well as another dataset
which will be used by WPM to generate the map(s).
Each sample is assigned a unique ID, which will be used to name it
onto the plate maps (for more details on the ID see the Results section ).
IMPORTANT Please ensure that the dataset is correctly displayed in the right
window and that the number of samples / groups is correct.
If you see that the total number of samples is wrong, this means that you have
not chosen the appropriate options among those described above, so that corrections are needed.
This step is mandatory. It will be used in the plot titles as well as in the output file names. Moreover, it be concatenated with sample IDs to limit confusions.
Here you have to specify the plate dimensions and their number. Currently, WPM supports plate dimensions of 6, 24, 48, 96, 386, 1534 wells; as well as custom dimensions (where you manually specify the number of rows and columns).
To the right of step 2 you can see an information box, warning you that WPM will distribute the samples in a balanced manner within the plates (if there are several of them).
If you select a plate size compatible with the total number of samples, you will see two blue boxes and a plate plan appear on the right hand side. They summarize all the elements of your configuration. In the example below, we selected the pre-defined dimension of 96 wells and only one plate:
The right side of the panel will summarize all these parameters:
This plot updates with each modification of the parameters, thus making it possible to see if one has made an error.
IMPORTANT: If the WPM detects a problem or incompatibility between parameters, you will see an error message instead of the plate map, providing hints on the possible origin of the problem.
In this step are listed the Forbidden wells, if any (optional):
A Forbidden well will not be filled with any kind of sample, either because the user does not want to (e.g. plate corners in case of non-uniform heat distribution), or because of material constraints (e.g. dirty wells, broken pipettes).
You fill the text input with the coordinates of the wells (a combination of letters and numbers, as in the example below):
You will see the plot updated in the right section:
The wells filled with forbidden wells will have the "forbidden" ID in the final dataset. On the resulting map, these wells will be colored in red.
At this stage you can specify the wells which correspond to buffers, if there are any.
A buffer well corresponds to a well filled filled with solution but without biological material (e.g. to avoid/check for cross-contamination).
Five patterns are available for placing the buffers:
1) no buffers: there will be no buffer on the plate(s).
2) Per line: Automatically places buffers every other row. You can choose to start placing in even or odd row.
3) Per column: Automatically places buffers every other column. You can choose to start placing in even or odd column.
4) Checkerboard: Automatically places buffers like a checkerboard.
5) Choose by hand: It is the same procedure as for specifying forbidden wells.
These are the spatial constraints that the WPM needs to respect when designing the plates. Currently, 4 types of them are proposed. Note that the patterns are available only if they are compatible with the chosen buffer pattern. The question here is: Should samples from the same group be found side by side?
Schematically, the spatial constraints can be summarized as follows (the blue well is the current well evaluated by WPM; The wells in green are those assessed for compliance with the chosen constraint. The blue well therefore has the possibility (but not the obligation since the filling of the plate is done randomly) to be filled with a sample belonging to the same group as the samples in the wells evaluated.
NS (North South): samples from the same group will not be placed side by side
column-wise.
WE (West East): samples from the same group will not be placed side by side
row-wise.
NSEW (North South East West): samples from the same group will not be placed
side by side either row-wise or column-wise.
None: samples from the same group can be placed anywhere, including side by side.
The wells filled with buffer solution will have the "buffer" ID in the final dataset. On the resulting map, these wells will be colored in grey.
At this stage you can specify the wells which correspond to fixed samples, if there are any.
A fixed sample corresponds to a quality control sample or standard. The precise location of these samples must be controlled by the researcher.
This step works in exactly the same way as the forbidden well step. The only difference is that the fixed samples will appear in black on the plot.
The fixed samples will have the "fixed" ID in the final dataset.
Choose a maximum number of iterations to find a solution, then start the WPM by clicking the "start WPM" button. If the samples do not have a group, then the samples will be placed completely randomly on the plates. If there are groups, the WPM will use an algorithm inspired by the backtracking algorithm (to place the samples in the wells while respecting the specified constraints).
The default value is 20, but if your configuration is somewhat complex, then it is advised to increase the number.
An iteration corresponds to an attempt by the WPM to find a solution. The algorithm used is not fully backtracked: the WPM stops as soon as there are no more possibilities to finalize the current solution; then, it starts back from scratch the plate map, until a solution that fits all the constraints is found. With this approach, not all possible combinations are explored, but it does reduce execution time.
When you start the computations, a progress bar appears.
If the WPM finds a solution, you will see this pop in the browser, inviting you to go to the Result Panel:
If the WPM fails, an error message will appear, prompting you to try again:
IMPORTANT If after launching WPM and generating the results, you realize that one or more parameters do not work, you can always return to the "Parameters" tab and modify them. The data displayed in the "Results" tab will not be automatically changed, you will have to click again on the "start WPM" button to take into account the new changes.
NOTE If you want to create a new plate plan for another project, press
ctrl + f5
, this will reset the application.
The Result panel allows you to look at the final dataset containing the well chosen for each sample, as well as a plot of your final well-plate map. Dataframe and plots are downloadable separately.
The dataset contains 7 columns giving all the information needed to implement the experiment: The sample name with its corresponding group; its ID for the plot; the well chosen; the row and the column to which the well corresponds to; and the number of the plate on which the sample must be placed.
This tab also shows the generated plot(s) of the final well-plate map(s). One color corresponds to one group label. The numbers are the IDs used in place of the sample names which could be too long to keep the plot readable.
Below is an example of 80 samples distributed in 10 groups (of unequal sizes) and placed on a 96 well-plate, with the North-South-East-West neighborhood constraint:
As explained before, the WPM can also be used through R command lines by following these steps:
The user can work with CSV files, ExpressionSet
, MSnSet
or
SummarizedExperiment
objects.
The first step is to create a dataframe containing all the necessary information for the WPM
to work correctly. Notably, it is needed to specify which column in the file
corresponds to the grouping factor, if any.
imported_csv <- wpm::convertCSV("path-to-CSV-file")
ExpressionSet
or MSnSet
objectsample_names <- c("s1","s2","s3","s4", "s5") M <- matrix(NA, nrow = 4, ncol = 5) colnames(M) <- sample_names rownames(M) <- paste0("id", LETTERS[1:4]) pd <- data.frame(Environment = rep_len(LETTERS[1:3], 5), Category = rep_len(1:2, 5), row.names = sample_names) rownames(pd) <- colnames(M) my_MSnSet_object <- MSnbase::MSnSet(exprs = M,pData = pd)
Then, run convertESet
by specifying the object and the variable to use as
grouping factor for samples:
df <- wpm::convertESet(my_MSnSet_object, "Environment")
SummarizedExperiment
nrows <- 200 ncols <- 6 counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows) colData <- data.frame(Treatment=rep(c("ChIP", "Input"), 3), row.names=LETTERS[1:6]) se <- SummarizedExperiment::SummarizedExperiment(assays=list(counts=counts), colData=colData) df <- wpm::convertSE(se, "Treatment")
For more details about the functions, please use ?wpm::<functionName>
R command.
The next step is to run the wrapperWPM
function by giving it all the parameters
needed:
convertXXX
functionsIn the running toy example (see code shunks around), we do not specify any buffer well.
wpm_result <- wpm::wrapperWPM(user_df = imported_csv$df_wpm, plate_dims = list(8,12), nb_plates = 1, forbidden_wells = "A1,A2,A3", fixed_wells = "B1,B2", spatial_constraint = "NS")
ExpressionSet
, MSnSet
or SummarizedExperiment
)wpm_result <- wpm::wrapperWPM(user_df = df, plate_dims = list(8,12), nb_plates = 1, forbidden_wells = "A1,A2,A3", fixed_wells = "B1,B2", spatial_constraint = "NS")
For more details, see ?wpm::wrapperWPM
The final step is to create a visual output of the generated plate plan(s)
using the drawMap()
function:
drawned_map <- wpm::drawMap(df = wpm_result, sample_gps = length(levels(as.factor(colData$Treatment))), gp_levels = gp_lvl <- levels(as.factor(colData$Treatment)), plate_lines = 8, plate_cols = 12, project_title = "my Project Title")
drawned_map
For more details, see ?wpm::drawMap
Plots can be saved with:
ggplot2::ggsave( filename = "my file name", plot = drawned_map, width = 10, height = 7, units = "in" )
IMPORTANT If multiple plates where specified, then wpm_result
will be a
list containing a dataset for each generated plate. Then, each of them can be accessed with wpm_result[[numberOfThePlate]]
:
numberOfThePlate <- 1 drawned_map <- wpm::drawMap(df = wpm_result[[numberOfThePlate]], sample_gps = length(levels(as.factor(pd$Environment))), gp_levels = gp_lvl <- levels(as.factor(pd$Environment)), plate_lines = 8, plate_cols = 12, project_title = "my Project Title")
Borges, H., Hesse, A. M., Kraut, A., Couté, Y., Brun, V., & Burger, T. (2021). Well Plate Maker: A user-friendly randomized block design application to limit batch effects in largescale biomedical studies. Bioinformatics (link to the publication).
sessionInfo()
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