# Basic usage with R¶

The following steps show how to get started with wellmapr in R:

1. Install wellmapr from GitHub. It’s good to be aware that wellmapr is written in python and made available to R using the reticulate package. This detail shouldn’t affect you in normal usage, but may be relevant if the installation doesn’t go smoothly:

> devtools::install_github("kalekundert/wellmap", subdir="wellmapr")

2. Write a TOML file describing the layout of an experiment. For example, the following layout might be used for a standard curve:

# The [row] and [col] sections specify which conditions are being tested in
# which wells.  The fields within these sections (e.g. dilution, replicate)
# can be anything.  If your plates aren't organized by row and column, there
# are other ways to define the plate layout; see the "File format" section for
# more details.

[col]
1.dilution = 1e5
2.dilution = 1e4
3.dilution = 1e3
4.dilution = 1e2
5.dilution = 1e1
6.dilution = 1e0

[row]
A.replicate = 1
B.replicate = 2
C.replicate = 3

3. Confirm that the layout is correct by using wellmapr::show() to produce a visualization of the layout. This is an important step, because it’s much easier to spot mistakes in the visualization than in the layout file itself.

> wellmapr::show("std_curve.toml")


This map shows that:

• Each row is a different replicate.

• Each column is a different dilution.

It’s also possible to create maps like this from the command line, which may be more convenient in some cases. The best way to do this is to use reticulate::py_config() to find the path to the python installation used by reticulate, then to invoke the wellmap command associated with that installation. The alias is optional, but could be saved in your shell configuration to make the command easier to remember:

$Rscript -e 'reticulate::py_config()' python: /home/kale/.local/share/r-miniconda/envs/r-reticulate/bin/python libpython: /home/kale/.local/share/r-miniconda/envs/r-reticulate/lib/libpython3.6m.so pythonhome: /home/kale/.local/share/r-miniconda/envs/r-reticulate:/home/kale/.local/share/r-miniconda/envs/r-reticulate version: 3.6.10 | packaged by conda-forge | (default, Apr 24 2020, 16:44:11) [GCC 7.3.0] numpy: /home/kale/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/numpy numpy_version: 1.18.5$ alias wellmap=/home/kale/.local/share/r-miniconda/envs/r-reticulate/bin/wellmap
\$ wellmap std_curve.toml

4. Load the data from the experiment in question into a tidy data frame. Tidy data are easier to work with in general, and are required by wellmapr in particular. If you aren’t familiar with the concept of tidy data, this article is a good introduction. The basic idea is to ensure that:

• Each variable is represented by a single column.

• Each observation is represented by a single row.

If possible, it’s best to export data from the instrument that collected it directly to a tidy format. When this isn’t possible, though, you’ll need to tidy the data yourself. For example, consider the following data (which corresponds to the layout from above). This is qPCR data, where a higher $$C_q$$ value indicates that less material is present. The data are shaped like the plate itself, e.g. a row in the data for every row on the plate, and a column in the data for every column on the plate. It’s not uncommon for microplate instruments to export data in this format.

std_curve.csv

Cq

1

2

3

4

5

6

A

24.180858612060547

20.74011993408203

17.183801651000977

13.774299621582031

10.29498291015625

6.967061996459961

B

24.15711784362793

20.77970314025879

17.171794891357422

13.768831253051758

10.362966537475586

6.870273113250732

C

24.238229751586914

20.78700828552246

17.147598266601563

13.779314041137695

10.292966842651367

6.735703945159912

Below is the code to load this data into a tidy tibble with the following columns:

• row: A letter identifying a row on the microplate, e.g. A-H

• col: A number identifying a column on the microplate, e.g. 1-12

• Cq: The $$C_q$$ value measured for the identified well.

> library(tidyverse)
>
+   rename(row = Cq) %>%
+   pivot_longer(
+       !row,
+       names_to = "col",
+       values_to = "Cq",
+   )
+ }
> data
# A tibble: 18 x 3
row   col      Cq
<chr> <chr> <dbl>
1 A     1     24.2
2 A     2     20.7
3 A     3     17.2
4 A     4     13.8
5 A     5     10.3
6 A     6      6.97
7 B     1     24.2
8 B     2     20.8
9 B     3     17.2
10 B     4     13.8
11 B     5     10.4
12 B     6      6.87
13 C     1     24.2
14 C     2     20.8
15 C     3     17.1
16 C     4     13.8
17 C     5     10.3
18 C     6      6.74

5. Use wellmapr::load() to associate the labels specified in the TOML file (e.g. the dilutions and replicates) with the experimental data (e.g. the $$C_q$$ values). This process has three steps:

• Load a data frame containing the data (see above).

• Load another data frame containing the labels.

• Merge the two data frames.

For the sake of clarity and completeness, we will first show how to perform these steps manually. Practically, though, it’s easier to let wellmapr perform them automatically.

Manual merge

Use the wellmapr::load() function to create a tibble containing the information from the TOML file. This data frame will have columns for each label we specified: replicate, dilution. It will also have six columns identifying the wells in different ways: well, well0, row, col, row_i, col_j. These columns are redundant, but this redundancy makes it easier to merge the labels with the data. For example, if the wells are named “A1,A2,…” in the data, the well column can be used for the merge. If the wells are named “A01,A02,…”, the well0 column can be used instead. If the wells are named in some non-standard way, the row_i and col_j columns can be used to calculate an appropriate merge column.

> layout <- wellmapr::load("std_curve.toml")
> layout
well well0 row col row_i col_j replicate dilution
1    A1   A01   A   1     0     0         1    1e+05
2    A2   A02   A   2     0     1         1    1e+04
3    A3   A03   A   3     0     2         1    1e+03
4    A4   A04   A   4     0     3         1    1e+02
5    A5   A05   A   5     0     4         1    1e+01
6    A6   A06   A   6     0     5         1    1e+00
7    B1   B01   B   1     1     0         2    1e+05
8    B2   B02   B   2     1     1         2    1e+04
9    B3   B03   B   3     1     2         2    1e+03
10   B4   B04   B   4     1     3         2    1e+02
11   B5   B05   B   5     1     4         2    1e+01
12   B6   B06   B   6     1     5         2    1e+00
13   C1   C01   C   1     2     0         3    1e+05
14   C2   C02   C   2     2     1         3    1e+04
15   C3   C03   C   3     2     2         3    1e+03
16   C4   C04   C   4     2     3         3    1e+02
17   C5   C05   C   5     2     4         3    1e+01
18   C6   C06   C   6     2     5         3    1e+00


Use the dplyr::inner_join() function to associate the labels with the data. In this case, both data frames have columns named row and col, so those columns are automatically used for the merge (as indicated). It is also easy to merge using columns with different names; see the documentation on dplyr::inner_join() for more information.

> inner_join(layout, data)
Joining, by = c("row", "col")
well well0 row col row_i col_j replicate dilution        Cq
1    A1   A01   A   1     0     0         1    1e+05 24.180859
2    A2   A02   A   2     0     1         1    1e+04 20.740120
3    A3   A03   A   3     0     2         1    1e+03 17.183802
4    A4   A04   A   4     0     3         1    1e+02 13.774300
5    A5   A05   A   5     0     4         1    1e+01 10.294983
6    A6   A06   A   6     0     5         1    1e+00  6.967062
7    B1   B01   B   1     1     0         2    1e+05 24.157118
8    B2   B02   B   2     1     1         2    1e+04 20.779703
9    B3   B03   B   3     1     2         2    1e+03 17.171795
10   B4   B04   B   4     1     3         2    1e+02 13.768831
11   B5   B05   B   5     1     4         2    1e+01 10.362967
12   B6   B06   B   6     1     5         2    1e+00  6.870273
13   C1   C01   C   1     2     0         3    1e+05 24.238230
14   C2   C02   C   2     2     1         3    1e+04 20.787008
15   C3   C03   C   3     2     2         3    1e+03 17.147598
16   C4   C04   C   4     2     3         3    1e+02 13.779314
17   C5   C05   C   5     2     4         3    1e+01 10.292967
18   C6   C06   C   6     2     5         3    1e+00  6.735704


Automatic merge

While it’s good to understand how the labels are merged with the data, it’s better to let wellmapr perform the merge for you. Not only is this more succinct, it also handles some tricky corner cases behind the scenes, e.g. layouts with multiple data files.

To load and merge the data using wellmapr::load(), you need to provide the following arguments:

• data_loader: A function that accepts a path to a file and returns a tibble containing the data from that file. Note that the function we wrote in the previous section fulfills these requirements. If the raw data are tidy to begin with, it is often possible to directly use readr::read_csv() or similar for this argument.

• merge_cols: An indication of which columns to merge. In the snippet below, TRUE means to use any columns that are shared between the two data frames (e.g. that have the same name). You can also use a dictionary to be more explicit about which columns to merge on.

Here we also provide the path_guess argument, which specifies that the experimental data can be found in a CSV file with the same base name as the layout. Note that this argument uses the syntax for string formatting in python, as described in the API documentation. It also would’ve been possible to specify the path to the CSV directly from the TOML file (see meta.path), in which case this argument would’ve been unnecessary.

> wellmapr::load(
+     "std_curve.toml",
+     merge_cols = TRUE,
+     path_guess = "{0.stem}.csv",
+ )
well well0 row col row_i col_j                          path replicate dilution        Cq
0    A1   A01   A   1     0     0 <environment: 0x56501964bc60>         1    1e+05 24.180859
1    A2   A02   A   2     0     1 <environment: 0x565019653a68>         1    1e+04 20.740120
2    A3   A03   A   3     0     2 <environment: 0x56501965d790>         1    1e+03 17.183802
3    A4   A04   A   4     0     3 <environment: 0x565019665598>         1    1e+02 13.774300
4    A5   A05   A   5     0     4 <environment: 0x56501966f2c0>         1    1e+01 10.294983
5    A6   A06   A   6     0     5 <environment: 0x565019673298>         1    1e+00  6.967062
6    B1   B01   B   1     1     0 <environment: 0x56501967b0a0>         2    1e+05 24.157118
7    B2   B02   B   2     1     1 <environment: 0x565019684dc8>         2    1e+04 20.779703
8    B3   B03   B   3     1     2 <environment: 0x56501968cbd0>         2    1e+03 17.171795
9    B4   B04   B   4     1     3 <environment: 0x5650196968f8>         2    1e+02 13.768831
10   B5   B05   B   5     1     4 <environment: 0x56501969e700>         2    1e+01 10.362967
11   B6   B06   B   6     1     5 <environment: 0x5650196a8428>         2    1e+00  6.870273
12   C1   C01   C   1     2     0 <environment: 0x5650196b0230>         3    1e+05 24.238230
13   C2   C02   C   2     2     1 <environment: 0x5650196b9f58>         3    1e+04 20.787008
14   C3   C03   C   3     2     2 <environment: 0x5650196c3c80>         3    1e+03 17.147598
15   C4   C04   C   4     2     3 <environment: 0x5650196cba88>         3    1e+02 13.779314
16   C5   C05   C   5     2     4 <environment: 0x5650196d57b0>         3    1e+01 10.292967
17   C6   C06   C   6     2     5 <environment: 0x5650196dd5b8>         3    1e+00  6.735704

6. Analyze the data given the connection between the labels and the data. This step doesn’t involve wellmap, but is included here for completeness. The example below makes a linear regression of the data in log-space:

library(tidyverse)

rename(row = Cq) %>%
pivot_longer(
!row,
names_to = "col",
values_to = "Cq",
)
}