Getting started with CEAS

In ceas: Cellular Energetics Analysis Software

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Setup

library(ceas)

Importing Seahorse rates data

The read_data function imports a list of Wave excel export file(s), meaning it will import all .xlsx files from a common directory. With that, the user should organize the file(s) they wish to analyze in a single directory (folder). This directory can also contain the normalization CSV file (see Normalization). An easy way to get such a list is to put all your data in a directory and list its contents. For example, if your data is in a directory called "seahorse_data":

rep_list <- list.files("seahorse_data", pattern = "*.xlsx", full.names = TRUE)

In this vignette we use the package's internal datasets:

rep_list <- system.file("extdata", package = "ceas") |>
  list.files(pattern = "*.xlsx", full.names = TRUE)
raw_data <- readxl::read_excel(rep_list[1], sheet = 2)
knitr::kable(head(raw_data))

The data requires the following columns: r colnames(raw_data). The Group column needs to be in the format biological_group<delimiter>Assay_type (with <space> as the default delimiter) as shown above. Upon reading with read_data, the Group column is split into two at the delimiter character and used to populate the group and assay columns. This output format can be set in the Seahorse machine before starting the experiment. The user can choose to either

• name their wells according to the ceas format on the Wave software or

• manually rename each Group in the Wave excel output before importing via read_data. If you already have the data, this column will have to be converted to this format to work with ceas.

seahorse_rates <- read_data(rep_list)
knitr::kable(head(seahorse_rates))

Normalization

There are two types of normalization involved in Seahorse data analysis. The first type of normalization is background normalization, which is typically performed within in the Seahorse Wave software. If the Seahorse data has been background normalized, the “Background” wells should have OCR and ECAR values of 0. ceas will flag the user with a warning if the “Background” OCR and ECAR values are not 0 (see first row of the table above).

The second type of normalization is sample normalization, which can be performed using ceas. Example sample normalization measures include cell count per well and $\mu$g of protein per well. Sample normalization using ceas requires an additional CSV file containing two columns:

1. "exp_group" or "well"

2. experimental measure values (e.g. cell counts) in this format:

norm_csv <- system.file("extdata", package = "ceas") |>
  list.files(pattern = "norm.csv", full.names = TRUE)
norm_csv
exp_group_norm <- norm_csv[1]
well_norm <- norm_csv[2]

read.csv(exp_group_norm) |>
    knitr::kable(caption = "For normalizing by experimental group")
read.csv(well_norm) |> head() |>
    knitr::kable(caption = "For normalizing by well")

For sample normalization ceas can use one of two normalizing methods according to the provided norm_method argument:

• "self": for each experimental group or well, the rows of the Seahorse data are divided by the corresponding measure value. Each OCR, ECAR, and PER value is divided by the measure it"self". OCR and ECAR values are divided by the corresponding raw value in the "measure" column. This can be thought of as an intra-well/experimental group normalization. Each normalized value is then interpreted as pmol/min per cell or pmol/min per \eqn{\mu}g of protein.

• "minimum": When set to "minimum", each OCR, ECAR, and PER value is normalized by the minimum value in the norm_csv "measure" column. In this method, every "measure" column's value in the provided CSV file is divided by the lowest of the "measure" values to get a normalization factor for each well or experimental group. The OCR, ECAR, and PER values in each well or experimental group are divided by their corresponding normalization factors. Compared to "self", this can be thought of as an inter-well/experimental group normalization based on the lowest "measure". The results may be interpreted as pmol/min per minimum of the group cell count or \eqn{\mu}g of protein.

Your normalization CSV file path may be passed into read_data() using the norm argument along with norm_column with either "exp_group" or "well" and norm_method as either "self" or "minimum".

Note: it is important to minimize sample variability during your Seahorse experiment.

read_data(
    rep_list,
    norm = exp_group_norm,
    norm_column = "exp_group",
    norm_method = "self"
) |> head() |> knitr::kable()

Calculating energetics

Partitioning data

Note:
When we use the term 'max' in the package documentation we mean the maximal experimental OCR and ECAR values rather than absolute biological maximums.

The energetics calculation workflow involves partitioning the data into its time point and assay intervals.

partitioned_data <- partition_data(seahorse_rates)

Alternative data formats {.tabset}

The default partition_data() parameters are set to analyze (1) Mito Stress Test and (2) Glycolysis Stress Test assays run in parallel in the same experiment. The assay_types list parameter can be modified to account for alternative experiments (e.g. just a Mito Stress Test assay).

Mito + Glyco (our default)

partitioned_data <- partition_data(
  seahorse_rates,
  assay_types = list(
    basal = "MITO",
    uncoupled = "MITO",
    maxresp = "MITO",
    nonmito = "MITO",
    no_glucose_glyc = "GLYCO",
    glucose_glyc = "GLYCO",
    max_glyc = "GLYCO"
  ),
  basal_tp = 3,
  uncoupled_tp = 6,
  maxresp_tp = 8,
  nonmito_tp = 12,
  no_glucose_glyc_tp = 3,
  glucose_glyc_tp = 6,
  max_glyc_tp = 8
)

Data in the form of @mookerjee2017

partitioned_data <- partition_data(
  seahorse_rates,
  assay_types = list(
    basal = "RefAssay",
    uncoupled = "RefAssay",
    maxresp = NA,
    nonmito = "RefAssay",
    no_glucose_glyc = "RefAssay",
    glucose_glyc = "RefAssay",
    max_glyc = NA
  ),
  basal_tp = 5,
  uncoupled_tp = 10,
  nonmito_tp = 12,
  maxresp = NA,
  no_glucose_glyc_tp = 1,
  glucose_glyc_tp = 5,
  max_glyc = NA
)

Just Mito

partitioned_data <- partition_data(
  seahorse_rates,
  assay_types = list(
    basal = "MITO",
    uncoupled = "MITO",
    maxresp = "MITO",
    nonmito = "MITO",
    no_glucose_glyc = NA,
    glucose_glyc = "MITO",
    max_glyc = NA
  ),
  basal_tp = 3,
  uncoupled_tp = 6,
  maxresp_tp = 8,
  nonmito_tp = 12,
  no_glucose_glyc_tp = NA,
  glucose_glyc_tp = 3,
  max_glyc_tp = NA
)

Respiratory control ratio (RCR) and glycolytic capacity (GC) assay

partitioned_data <- partition_data(
  seahorse_rates,
  assay_types = list(
    basal = "RCR",
    uncoupled = "RCR",
    maxresp = "RCR,"
    nonmito = "RCR",
    no_glucose_glyc = NA,
    glucose_glyc = "GC",
    max_glyc = "GC"
  ),
  basal_tp = 3,
  uncoupled_tp = 6,
  maxresp_tp = 8,
  nonmito_tp = 12,
  no_glucose_glyc = NA,
  glucose_glyc_tp = 3,
  max_glyc_tp = 9
)

Note that the time point parameters (maxresp_tp and no_glucose_glyc_tp) also need to be changed accordingly.

The get_energetics function requires pH, pK$_a$ and buffer values.

energetics <- get_energetics(partitioned_data, ph = 7.4, pka = 6.093, buffer = 0.10)

For more information on the calculations see the article on ATP calculations.

Plotting

Bioenergetic scope plot {.tabset}

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.

Replicates combined

bioscope <- bioscope_plot(
  energetics,
  model = "ols",
  sep_reps = FALSE
)
bioscope

Replicates as random effects

bioscope_plot(energetics, sep_reps = FALSE, model = "mixed")

Replicates separated

bioscope_plot(energetics, sep_reps = TRUE, model = "ols")

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.

Oxygen consumption rate (OCR) {.tabset}

Replicates combined

ocr <- rate_plot(
  seahorse_rates,
  measure = "OCR",
  assay = "MITO",
  model = "ols",
  sep_reps = FALSE
)
ocr

Replicates as random effects

rate_plot(
  seahorse_rates,
  measure = "OCR",
  assay = "MITO",
  model = "mixed",
  sep_reps = FALSE
)

Replicates separated

rate_plot(
  seahorse_rates,
  measure = "OCR",
  assay = "MITO",
  model = "ols",
  sep_reps = TRUE,
  linewidth = 1
)

Extracellular Acidification Rate (ECAR) {.tabset}

Replicates combined

ecar <- rate_plot(
  seahorse_rates,
  measure = "ECAR",
  assay = "GLYCO",
  model = "ols",
  sep_reps = FALSE
)
ecar

Replicates as random effects

rate_plot(
  seahorse_rates,
  measure = "ECAR",
  assay = "GLYCO",
  model = "mixed",
  sep_reps = FALSE
)

Replicates separated

rate_plot(
  seahorse_rates,
  measure = "ECAR",
  assay = "GLYCO",
  model = "ols",
  sep_reps = TRUE,
  linewidth = 1

)

ATP plots {.tabset}

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_glyc <- atp_plot(
  energetics,
  basal_vs_max = "basal",
  glyc_vs_resp = "glyc",
  sep_reps = FALSE
)
basal_glyc 

Basal respiration

atp_plot(
  energetics,
  basal_vs_max = "basal",
  glyc_vs_resp = "resp",
  model = "ols",
  sep_reps = TRUE
)

Maximal glycolysis

atp_plot(
  energetics,
  basal_vs_max = "max",
  glyc_vs_resp = "glyc",
  model = "mixed",
  sep_reps = FALSE
)

Maximal respiration

atp_plot(
  energetics,
  basal_vs_max = "max",
  glyc_vs_resp = "resp",
  model = "ols",
  sep_reps = TRUE
)

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

custom_colors <- c("#e36500", "#b52356", "#3cb62d", "#328fe1")

bioscope +
ggplot2::scale_color_manual(
  values = custom_colors
)

ocr +
ggplot2::scale_color_manual(
  values = custom_colors
)

Labels {.tabset}

Change axis labels

ecar +
    ggplot2::labs(x = "Time points")

Change label size

basal_glyc +
    ggplot2::theme(axis.text = ggplot2::element_text(size = 20))

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.

rate_plot

func_code <- capture.output(dput(rate_plot))
cat("```r\n")
cat(func_code, sep = "\n")
cat("\n```")

In RStudio, you can run utils::edit to modify a function.

edit(rate_plot)

References

ceas documentation built on April 3, 2025, 8:34 p.m.