mpxtractor: A flexible R package to process data from different plate reader machines.
The main function of mpxtractor is to provide a simple method that can process raw data from microplate readers like SpectraMax, FluorStar, and MultiscanGO and apply different methods to it. This package generates tidy tibble objects, this data can be combined with layout files, and then growth rates are calculated and plot over a microplate frame. This is a critical point in several research lines performing experiments using microplate reader machines mainly due to the time that takes to clean and sort the raw data into a tidy format. Also, the features of this package allow detecting which wells present faulty data e.g wells not filled correctly. Another feature of mpxtractor is that it allows plotting the layout file over a microplate frame, this is very useful because it is easy to visualize the layout, this is handy in the lab to set the experiment. This package main contribution is orientated to save time and get fast into data analysis.
For the reasons mentioned above is why mpxtractor can help to reduce the time to process the raw data into tidy data frames, combine layout files with tidy data. Also, mpxtractor provides features to visualize growth curves and layout files over a microplate frame. The latter can be printed to be used in the lab during the set up of experiments.
This package is divided by two sets of functions:
1. Function to wrangling the data in raw files, this functions are
explained in detail in vignette wrangling_and_layout_functions along
side with some examples.
2. Functions to plot layout files and growth rates, the vignette
plotting_functions explain how they work and some details.
# The development version from GitHub:
# install.packages("mpxtractor")
devtools::install_github("MartinBanchero/mpxtractor")Lets suppose we want to perform an experiment to measure growth rates
under different type of sugars. The first step is to design the layout.
Using mpxtractor we can load the layout file into a tidy data frame.
layout_file <- system.file("extdata", "test_layout_file.csv", package = "mpxtractor")
df_layout <- mpxtractor::read_layout_file(file = layout_file)
head(df_layout)
#> # A tibble: 6 x 5
#> Wells basic condition strain concentration
#> <chr> <chr> <chr> <chr> <dbl>
#> 1 A01 control water water 100
#> 2 A02 control water water 20
#> 3 A03 control water water 4
#> 4 A04 control water water 0.8
#> 5 A05 control water water 0.16
#> 6 A06 control water water 0.032Now we want to set the microplates and run the experiment in the lab,
for this we can use the plot_layout_file() function that provide a
visualization of our experimental design. For example, to avoid edge
effects due to temperature, evaporation, etc. the wells around the
perimeter of the plate might be more sensitive to this effects,
visualizing the layout design over the microplate give an easy way to
check whether the control or the samples are well positioned in the
plate. In Fig.1 is shown the basic and the Medium conditions
over the microplate.
#get the path to file
file <- system.file(
"extdata",
"test_fluorstar_layout.csv",
package = "mpxtractor"
)
plot <- mpxtractor::plot_layout_file(
file = file,
add_conc = TRUE,
var_shape = "Basic",
var_colour = "Medium",
output_filename = "layout_visualization.png"
)
Figure 1: Representation over a microplate mimic of the design file. In this case the medium was chosen to be colored. to be coloured.
Once all the experimental design is complete we are ready to use the
microplate reader machines. In this example we are using a fluorStar to
measure our growth rates. Within mpxtractor we can find one wrangling
function for each machine, in this case we going to use
read_fluorstar_data() (see vignette wrangling_and_layout_functions)
to store the raw data into a tidy dataframe.
file <- system.file("extdata", "test_fluorstar_fluorescence_data.txt", package = "mpxtractor")
# Extract the data stored in the files into a data frame using proper wrangling function
df_fl <- mpxtractor::read_fluorstar_data(
file = file
)
# Show tidy data
head(df_fl)
#> # A tibble: 6 x 5
#> Wells Sample Time Measurement wavelength
#> <chr> <chr> <chr> <int> <chr>
#> 1 A01 " X1" 00:00:00 186705 485, 520
#> 2 A01 " X1" 00:02:00 186950 485, 520
#> 3 A01 " X1" 00:07:00 188978 485, 520
#> 4 A01 " X1" 00:12:00 189018 485, 520
#> 5 A01 " X1" 00:17:00 189620 485, 520
#> 6 A01 " X1" 00:22:00 191198 485, 520Now let’s suppose we want to repeat the experiment but this time we want
to measure absorbance under the same conditions. In this case we have
two raw data files to be analyzed. Using the function
read_multiple_data_files() we can store both files into a tidy
dataframe. There many options that can be used to gather files with this
function, for more information check vignette
wrangling_and_layout_functions .
# Path to files
file1 <- system.file("extdata", "test_fluorstar_absorbance_data.txt", package = "mpxtractor")
file2 <- system.file("extdata", "test_fluorstar_fluorescence_data.txt", package = "mpxtractor")
df_two_files <- mpxtractor::read_multiple_data_files(
reader_type = "fluorstar",
filesname = c(file1,file2),
plate_names = c("absorbance", "fluorescence")
)
head(df_two_files)
#> # A tibble: 6 x 6
#> Wells Sample Time Measurement wavelength plate_filename
#> <chr> <chr> <chr> <dbl> <chr> <chr>
#> 1 A01 " X1" 00:00:00 3.5 600 absorbance
#> 2 A01 " X1" 00:02:00 3.44 600 absorbance
#> 3 A01 " X1" 00:07:00 3.5 600 absorbance
#> 4 A01 " X1" 00:12:00 3.5 600 absorbance
#> 5 A01 " X1" 00:17:00 3.21 600 absorbance
#> 6 A01 " X1" 00:22:00 3.35 600 absorbance
tail(df_two_files)
#> # A tibble: 6 x 6
#> Wells Sample Time Measurement wavelength plate_filename
#> <chr> <chr> <chr> <dbl> <chr> <chr>
#> 1 H12 " X96" 20:03:00 40309 485, 520 fluorescence
#> 2 H12 " X96" 20:08:00 40418 485, 520 fluorescence
#> 3 H12 " X96" 20:13:00 40438 485, 520 fluorescence
#> 4 H12 " X96" 20:18:00 40310 485, 520 fluorescence
#> 5 H12 " X96" 20:23:00 40357 485, 520 fluorescence
#> 6 H12 " X96" 20:28:00 40154 485, 520 fluorescenceAt this point we have two measurements under the same layout design. Now
we want to add the layout information to our tidy data frame with both
files. To do this we can use the function combine_data_with_layout()
and store all the information in one tidy data frame.
#get the path to layout file
file_layout <- system.file(
"extdata",
"test_fluorstar_layout.csv",
package = "mpxtractor"
)
df_combine <- mpxtractor::combine_data_with_layout(
df_data = df_two_files,
reader_type = "Fluorstar",
layout_files = file_layout
)
head(df_combine)
#> # A tibble: 6 x 10
#> # Groups: Wells, plate_filename [1]
#> Wells Sample Time Measurement wavelength plate_filename Basic Medium concentration
#> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <dbl>
#> 1 A01 " X1" 00:0… 3.5 600 absorbance poor milk 100
#> 2 A01 " X1" 00:0… 3.44 600 absorbance poor milk 100
#> 3 A01 " X1" 00:0… 3.5 600 absorbance poor milk 100
#> 4 A01 " X1" 00:1… 3.5 600 absorbance poor milk 100
#> 5 A01 " X1" 00:1… 3.21 600 absorbance poor milk 100
#> 6 A01 " X1" 00:2… 3.35 600 absorbance poor milk 100
#> # … with 1 more variable: layout_file <chr>The last step is to calculate and plot the growth curves for each well.
file1 <- system.file("extdata",
"test_fluorstar_absorbance_data.txt",
package = "mpxtractor"
)
layout_file <- system.file(
"extdata",
"test_fluorstar_layout.csv",
package = "mpxtractor"
)
df_fl <- mpxtractor::read_fluorstar_data(file = file1)
df_combined <- mpxtractor::combine_data_with_layout(
df_data = df_fl,
reader_type = "fluorstar",
layout_files = layout_file
)
head(df_combined)
#> # A tibble: 6 x 9
#> Wells Sample Time Measurement wavelength Basic Medium concentration layout_file
#> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <dbl> <chr>
#> 1 A01 " X1" 00:00:00 3.5 600 poor milk 100 test_fluorstar_layout
#> 2 A01 " X1" 00:02:00 3.44 600 poor milk 100 test_fluorstar_layout
#> 3 A01 " X1" 00:07:00 3.5 600 poor milk 100 test_fluorstar_layout
#> 4 A01 " X1" 00:12:00 3.5 600 poor milk 100 test_fluorstar_layout
#> 5 A01 " X1" 00:17:00 3.21 600 poor milk 100 test_fluorstar_layout
#> 6 A01 " X1" 00:22:00 3.35 600 poor milk 100 test_fluorstar_layoutmpxtractor::plot_gr_microplate(df_fl,
var_gr = "Measurement",
ws = "2hs",
cond_to_col = "Medium",
output_filename = "fluorstar.png"
)
Figure 2: Representation of growth rate curves per well mimicking a plate-shaped format. Note that in this case the attribute Medium was chosen to be coloured.
To get started, first read vignette("Introduction_to_mpxtractor").
Then read more about the specific package components that you want to
apply.
- For data wrangling and layout files, read
vignette("wrangling_and_layout_functions"). - For plotting functions, read
vignette("plotting_functions") - For auxiliary functions read
vignette("Auxiliary_functions")