Step 1: Upload the dataset

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.

It is also possible to provide a file containing several other variables describing the data, as in the example below:

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:

• check if your file does have a header and row names.
• select the appropriate separator field. Default is semicolon (“;”)

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.

Choose the grouping factor

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 ).

Dataset vizualisation

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.

Step 2: Choose a Project name

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.

Step 3: Plate(s) dimensions

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).

balanced way message

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:

plate dimensions example

The right side of the panel will summarize all these parameters:

parameters summary

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.

Example of error message

Step 4: Forbidden wells

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):

Example of forbidden wells listed in the text input

You will see the plot updated in the right section:

Updated plot with forbidden wells

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.

Step 5: Buffers

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.

Per line mode example with even option

3) Per column: Automatically places buffers every other column. You can choose to start placing in even or odd column.

Per Column mode example with even option

4) Checkerboard: Automatically places buffers like a checkerboard.

Checkerboard mode

5) Choose by hand: It is the same procedure as for specifying forbidden wells.

Step 6: Fixed samples

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.

Number of iterations

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:

WPM succeeded

If the WPM fails, an error message will appear, prompting you to try again:

WPM failed

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.

Starting from a CSV file:

imported_csv <- wpm::convertCSV("path-to-CSV-file")

Starting from an ExpressionSet or MSnSet object

sample_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")

Starting from a 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.

When using a CSV file

In 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")

When using an R-structured dataset (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")

## 2024-11-30 05:36:22.214267 INFO::max_iteration: 20
## 2024-11-30 05:36:22.227708 INFO:backtrack/map:nrow(c): 6
## 2024-11-30 05:36:22.244554 INFO::plate number 1
## 2024-11-30 05:36:22.264957 WARNING:fonctions.generateMapPlate:number of attempts: 1
## 2024-11-30 05:36:22.267547 INFO:backtracking:class(new_df): data.frame

For more details, see ?wpm::wrapperWPM