Introduction to using gcplyr
gcplyr is a package that implements a number of
functions to make it easier to import, manipulate, and analyze microbial
growth from data collected in multiwell plate readers (“growth curves”).
gcplyr, importing and analyzing plate reader data
can be a complicated process that has to be tailored for each
experiment, requiring many lines of code. With
of those steps are now just a single line of code.
This document gives an introduction of how to use
for each step of a growth curve analysis.
To get started, you need your growth curve data file saved to your computer (.csv, .xls, or .xlsx).
Users often want to combine their data with some information on the
experimental design of their plate(s). You can save this information
into a tabular file as well, or you can just keep it handy to enter
Let’s get started by loading
gcplyr. We’re also going to
load a couple other packages we’ll need.
library(gcplyr) #> ## #> ## gcplyr (Version 1.6.0, Build Date: 2023-09-13) #> ## See http://github.com/mikeblazanin/gcplyr for additional documentation #> ## Please cite software as: #> ## Blazanin, Michael. 2023. gcplyr: an R package for microbial growth #> ## curve data analysis. bioRxiv doi: 10.1101/2023.04.30.538883 #> ## library(dplyr) #> #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': #> #> filter, lag #> The following objects are masked from 'package:base': #> #> intersect, setdiff, setequal, union library(ggplot2)
Before digging into the details, here’s a simple demonstration of
what a final
gcplyr script can look like. This script:
- imports data from files created by a plate reader
- combines it with design files created by the user
- calculates the maximum growth rate and area-under-the-curve
Don’t worry about understanding all the details of how the code works right now. Each of these steps is explained in depth in later articles.
# Read in our data # (our plate reader data is saved in "widedata.csv") data_wide <- read_wides(files = "widedata.csv") # Transform our data to be tidy-shaped data_tidy <- trans_wide_to_tidy(wides = data_wide, id_cols = c("file", "Time")) # Convert our time into hours data_tidy$Time <- as.numeric(data_tidy$Time)/3600 # Import our designs # (saved in the files Bacteria_strain.csv and Phage.csv) designs <- import_blockdesigns(files = c("Bacteria_strain.csv", "Phage.csv")) # Merge our designs and data data_merged <- merge_dfs(data_tidy, designs) #> Joining with `by = join_by(Well)` # Plot the data ggplot(data = data_merged, aes(x = Time, y = Measurements, color = Well)) + geom_line(aes(lty = Phage)) + guides(color = "none")
# Voila! 8 lines of code and all your data is imported & plotted! # Calculate the per-capita growth rate over time in each well data_merged <- mutate( group_by(data_merged, Well), percap_deriv = calc_deriv(y = Measurements, x = Time, percapita = TRUE, blank = 0, window_width_n = 5)) # Calculate two common metrics of bacterial growth: # the maximum growth rate, saving it to a column named max_percap # the area-under-the-curve, saving it to a column named 'auc' data_sum <- summarize( group_by(data_merged, Well, Bacteria_strain, Phage), max_percap = max(percap_deriv, na.rm = TRUE), auc = auc(y = Measurements, x = as.numeric(Time))) #> `summarise()` has grouped output by 'Well', 'Bacteria_strain'. You can override #> using the `.groups` argument. # Print some of the max growth rates and auc's head(data_sum) #> # A tibble: 6 × 5 #> # Groups: Well, Bacteria_strain  #> Well Bacteria_strain Phage max_percap auc #> <chr> <chr> <chr> <dbl> <dbl> #> 1 A1 Strain 1 No Phage 1.00 15.9 #> 2 A10 Strain 4 Phage Added 1.43 5.57 #> 3 A11 Strain 5 Phage Added 1.47 5.99 #> 4 A12 Strain 6 Phage Added 0.789 0.395 #> 5 A2 Strain 2 No Phage 1.31 19.3 #> 6 A3 Strain 3 No Phage 0.915 15.1 # Plot the results for max growth rate and area under the curve in # presence vs absence of phage ggplot(data = data_sum, aes(x = max_percap, y = auc, color = Phage)) + geom_point()
Generally, working with
gcplyr will follow a number of
steps, each of which is likely to be only one or a few lines of code in
your final script. We’ve explained each of these steps in a page linked
below. To start, we’ll learn how to import our data into
and transform it into a convenient format.
- Importing and transforming data:
- Incorporating design information:
- Pre-processing and plotting your data:
- Processing your data:
- Analyzing your data:
- Dealing with noise:
- Statistics, merging other data, and other resources: