Plotting
Bioenergetic scope plot
The bioscope_plot function plots a 2D representation of
group “bioenergetic scope.” Bioenergetic scope describes the theoretical
energetic space in which a matrix operates. The bioenergetic scope
coordinates are JATP from OXPHOS on the y-axis and JATP from glycolysis
on the x-axis. The points represent mean basal and/or max JATP from
OXPHOS and glycolysis and the vertical and horizontal lines represent
the standard deviation or confidence interval of JATP from OXPHOS or
glycolysis, respectively.
Rate plots
The rate_plot function provides an overview of OCR or
ECAR for each assay type over time, which enables cross-group energetic
comparisons before and after the addition of energetic-modulating
compounds. The rate_plot line represents mean group OCR or
ECAR over the sequential measurements (x-axis) and the shaded variance
region represents standard deviation or specified confidence
interval.
ATP plots
The atp_plot function plots group JATP values, which
enables cross-group OXPHOS and glycolytic JATP comparisons at basal and
max conditions. The atp_plot symbols represent the mean
basal or max JATP from OXPHOS or glycolysis, and the crossbar boundaries
represent the standard deviation or confidence interval JATP
variance.
Basal glycolysis
Basal respiration
Customizing plots
CEAS is designed to work with existing ggplot2
customization functionality and doesn’t include more than shape and size
options for its plots.
For example, to change the colors used in the plot, simply make the plot and add the custom colors you’d like:
Colors
Labels
Editing functions
We are working on making the plots as customizable as possible.
However, if there are options that cannot be set in the calls to the
plotting functions or with ggplot2 functions, you can get
the code used to make the plots by running the function name without
parenthesis and modify it. Further, since every step in the ceas
workflow provides a dataset, you can run the modified function or your
own custom plotting functions on those datasets.
function (seahorse_rates, measure = "OCR", assay = "MITO", error_bar = "ci",
conf_int = 0.95, group_label = "Experimental group")
{
stopifnot(`'measure' should be 'OCR' or 'ECAR'` = measure %in%
c("OCR", "ECAR"))
stopifnot(`'error_bar' should be 'sd' or 'ci'` = error_bar %in%
c("sd", "ci"))
stopifnot(`'conf_int' should be between 0 and 1` = conf_int >
0 && conf_int < 1)
data_cols <- c("Measurement", "Well", "OCR", "ECAR", "PER",
"exp_group", "assay_type", "replicate")
missing_cols <- setdiff(data_cols, colnames(seahorse_rates))
if (length(missing_cols) != 0) {
stop(paste0("'", missing_cols, "'", " column was not found in input data\n"))
}
Measurement <- NULL
exp_group <- NULL
lower_bound <- NULL
upper_bound <- NULL
plot_data <- get_rate_summary(seahorse_rates, measure, assay,
error_bar, conf_int)
y_labels <- list(OCR = paste0(assay, " OCR (pmol/min)"),
ECAR = paste0(assay, " ECAR (mpH/min)"))
ggplot(plot_data, aes(x = Measurement, y = mean, color = exp_group,
group = exp_group, fill = exp_group)) + geom_line(size = 2) +
geom_ribbon(aes(ymin = lower_bound, ymax = upper_bound),
alpha = 0.2, color = NA) + scale_x_continuous(breaks = seq(1,
12, by = 1)) + xlab("Measurement") + ylab(y_labels[measure]) +
labs(color = group_label, fill = group_label) + theme_bw()
}In RStudio, you can run utils::edit to modify a
function.