Package 'ceas' reference manual

Title:	Cellular Energetics Analysis Software
Description:	Analysis and visualization of cellular energetics data from Agilent Seahorse XF96. Cellular energetics is how cells make, use, and distribute units of energy (primarily ATP). Measuring real-time cellular energetics is essential to understanding a tissue or cell’s bioenergetic state and fuel dependencies. The Seahorse machine measures a cell’s or matrix’s oxygen consumption rate (OCR) – a proxy of oxidative phosphorylation – and extracellular acidification rate – a proxy of glycolysis. This package offers flexible and fast analysis and plotting capabilities for such data using the methods described by Mookerjee et al. (2017) <doi:10.1074/jbc.m116.774471>.
Authors:	Rachel House [aut, cre] , James Eapen [aut] , Hui Shen [fnd] , Carrie Graveel [fnd] , Matthew Steensma [fnd] , Van Andel Institute [cph]
Maintainer:	Rachel House <[email protected]>
License:	MIT + file LICENSE
Version:	1.0.0
Built:	2024-09-18 06:10:09 UTC
Source:	https://github.com/jamespeapen/ceas

ATP Plot

Description

Generate the ATP Plot

Usage

atp_plot(
  energetics,
  error_bar = "ci",
  conf_int = 0.95,
  size = 2,
  shape = 21,
  basal_vs_max = "basal",
  glyc_vs_resp = "glyc",
  group_label = "Experimental group"
)
atp_plot(
  energetics,
  error_bar = "ci",
  conf_int = 0.95,
  size = 2,
  shape = 21,
  basal_vs_max = "basal",
  glyc_vs_resp = "glyc",
  group_label = "Experimental group"
)

Arguments

`energetics`	A table of calculated glycolysis and OXPHOS rates. Returned by `get_energetics`
`error_bar`	Whether to plot error bars as standard deviation (`"sd"`) or confidence intervals (`"ci"`)
`conf_int`	The confidence interval percentage. Should be between 0 and 1
`size`	Size of the points
`shape`	Shape of the points
`basal_vs_max`	Whether to plot `"basal"` or `"max"` respiration
`glyc_vs_resp`	Whether to plot glycolysis (`"glyc"`) or respiration (`"resp"`)
`group_label`	Label for the experimental group to populate the legend title

Details

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.

Value

a ggplot

Examples

rep_list <- system.file("extdata", package = "ceas") |>
  list.files(pattern = "*.xlsx", full.names = TRUE)
seahorse_rates <- read_data(rep_list, sheet = 2)
partitioned_data <- partition_data(seahorse_rates)
energetics <- get_energetics(partitioned_data, ph = 7.4, pka = 6.093, buffer = 0.1)
atp_plot(energetics)

atp_plot(energetics, basal_vs_max = "max")

atp_plot(energetics, basal_vs_max = "basal", glyc_vs_resp = "resp")

# to change fill, the geom_point shape number should be between 15 and 25
atp_plot(energetics, shape = 21) + # filled circle
  ggplot2::scale_fill_manual(values = c("#e36500", "#b52356", "#3cb62d", "#328fe1"))

# to change color, use ggplot2::scale_color_manual
atp_plot(energetics) +
  ggplot2::scale_color_manual(values = c("#e36500", "#b52356", "#3cb62d", "#328fe1"))
rep_list <- system.file("extdata", package = "ceas") |>
  list.files(pattern = "*.xlsx", full.names = TRUE)
seahorse_rates <- read_data(rep_list, sheet = 2)
partitioned_data <- partition_data(seahorse_rates)
energetics <- get_energetics(partitioned_data, ph = 7.4, pka = 6.093, buffer = 0.1)
atp_plot(energetics)

atp_plot(energetics, basal_vs_max = "max")

atp_plot(energetics, basal_vs_max = "basal", glyc_vs_resp = "resp")

# to change fill, the geom_point shape number should be between 15 and 25
atp_plot(energetics, shape = 21) + # filled circle
  ggplot2::scale_fill_manual(values = c("#e36500", "#b52356", "#3cb62d", "#328fe1"))

# to change color, use ggplot2::scale_color_manual
atp_plot(energetics) +
  ggplot2::scale_color_manual(values = c("#e36500", "#b52356", "#3cb62d", "#328fe1"))

Bioenergetic Scope Plot

Description

Generate the Bioenergetic Scope Plot

Usage

bioscope_plot(
  energetics,
  error_bar = "ci",
  conf_int = 0.95,
  size = 2,
  basal_shape = 1,
  max_shape = 19,
  group_label = "Experimental Group"
)
bioscope_plot(
  energetics,
  error_bar = "ci",
  conf_int = 0.95,
  size = 2,
  basal_shape = 1,
  max_shape = 19,
  group_label = "Experimental Group"
)

Arguments

`energetics`	A table of calculated glycolysis and OXPHOS rates. Returned by `get_energetics`
`error_bar`	Whether to plot error bars as standard deviation (`"sd"`) or confidence intervals (`"ci"`)
`conf_int`	The confidence interval percentage. Should be between 0 and 1
`size`	Size of the points
`basal_shape`	Shape of the points for basal values
`max_shape`	Shape of the points for max values
`group_label`	Label for the experimental group to populate the legend title
`bioscope_plot`	Creates a 2D plot visualizing the mean and standard deviation basal and maximal ATP production from glycolysis and OXPHOS for each experimental group

Value

a ggplot

Examples

rep_list <- system.file("extdata", package = "ceas") |>
  list.files(pattern = "*.xlsx", full.names = TRUE)
seahorse_rates <- read_data(rep_list, sheet = 2)
partitioned_data <- partition_data(seahorse_rates)
energetics <- get_energetics(
  partitioned_data,
  ph = 7.4,
  pka = 6.093,
  buffer = 0.1
)
bioscope_plot(energetics)

# to change fill, the geom_point shape should be between 15 and 20.
# These shapes are filled without border and will correctly show on the legend.
bioscope_plot(energetics, size = 3, basal_shape = 2, max_shape = 17) + # empty and filled triangle
  ggplot2::scale_fill_manual(
    values = c("#e36500", "#b52356", "#3cb62d", "#328fe1")
  )

# to change color, use ggplot2::scale_color_manual
bioscope_plot(energetics) +
  ggplot2::scale_color_manual(
    values = c("#e36500", "#b52356", "#3cb62d", "#328fe1")
  )
rep_list <- system.file("extdata", package = "ceas") |>
  list.files(pattern = "*.xlsx", full.names = TRUE)
seahorse_rates <- read_data(rep_list, sheet = 2)
partitioned_data <- partition_data(seahorse_rates)
energetics <- get_energetics(
  partitioned_data,
  ph = 7.4,
  pka = 6.093,
  buffer = 0.1
)
bioscope_plot(energetics)

# to change fill, the geom_point shape should be between 15 and 20.
# These shapes are filled without border and will correctly show on the legend.
bioscope_plot(energetics, size = 3, basal_shape = 2, max_shape = 17) + # empty and filled triangle
  ggplot2::scale_fill_manual(
    values = c("#e36500", "#b52356", "#3cb62d", "#328fe1")
  )

# to change color, use ggplot2::scale_color_manual
bioscope_plot(energetics) +
  ggplot2::scale_color_manual(
    values = c("#e36500", "#b52356", "#3cb62d", "#328fe1")
  )

Calculate ATP Production from OXPHOS and Glycolysis

Description

Calculates ATP production from glycolysis and OXPHOS at points defined in patitioned_data

Usage

get_energetics(partitioned_data, ph, pka, buffer)
get_energetics(partitioned_data, ph, pka, buffer)

Arguments

`partitioned_data`	a data.table of organized Seahorse OCR and ECAR rates based on timepoints from the assay cycle. Returned by `partition_data`
`ph`	pH value for energetics calculation (for XF Media, 7.5)
`pka`	pKa value for energetics calculation (for XF Media, 6.063)
`buffer`	buffer for energetics calculation (for XF Media, 0.1 mpH/pmol H+)

Details

TODO: check that all symbols are defined

Proton production rate (PPR):

$\text{PPR} = \frac{\text{ECAR value}}{\text{buffer}}$

$\text{PPR}_{\text{mito}} = \frac{10^{\text{pH}-\text{pK}_a}}{1+10^{\text{pH}-\text{pK}_a}} \cdot \frac{\text{H}^+}{\text{O}_2} \cdot \text{OCR}$

calculates the proton production from glucose during its conversion to bicarbonate and $\text{H}^+$ assuming max $\frac{\text{H}^+}{\text{O}_2}$ of 1

$\text{PPR}_\text{glyc} = \text{PPR} - \text{PPR}_\text{resp}$

calculates the proton production from glucose during its conversion to lactate + $\text{H}^+$

Joules of ATP (JATP) production:

$\text{ATP}_{\text{glyc}} = \Bigl(\text{PPR}_\text{glyc} \cdot \frac{\text{ATP}}{\text{lactate}}\Bigl) + \Bigl(\text{MITO}_\text{resp} \cdot 2 \cdot \frac{\text{P}}{\text{O}_\text{glyc}}\Bigl)$

$\frac{\text{ATP}}{\text{lactate}} = 1$

with $\frac{\text{P}}{{\text{O}_\text{glyc}}}$ = 0.167 for glucose (0.242 for glycogen).

$\text{ATP}_\text{resp} = \Bigl(\text{coupled MITO}_\text{resp} \cdot 2 \cdot \frac{\text{P}}{\text{O}_\text{oxphos}}\Bigl) + \Bigl(\text{MITO}_\text{resp} \cdot 2 \cdot \frac{\text{P}}{\text{O}_\text{TCA}}\Bigl)$

with $\frac{\text{P}}{{\text{O}_\text{oxphos}}}$ = 2.486 and $\frac{\text{P}}{{\text{O}_\text{TCA}}}$ = 0.167.

Value

a data.table of glycolysis and OXPHOS rates

Examples

rep_list <- system.file("extdata", package = "ceas") |>
  list.files(pattern = "*.xlsx", full.names = TRUE)
seahorse_rates <- read_data(rep_list, sheet = 2)
partitioned_data <- partition_data(seahorse_rates)
energetics <- get_energetics(partitioned_data, ph = 7.4, pka = 6.093, buffer = 0.1)
head(energetics, n = 10)
rep_list <- system.file("extdata", package = "ceas") |>
  list.files(pattern = "*.xlsx", full.names = TRUE)
seahorse_rates <- read_data(rep_list, sheet = 2)
partitioned_data <- partition_data(seahorse_rates)
energetics <- get_energetics(partitioned_data, ph = 7.4, pka = 6.093, buffer = 0.1)
head(energetics, n = 10)

Calculate ATP Production Mean and Standard Deviation

Description

Calculates mean and standard deviation of ATP production from glycolysis and OXPHOS at points defined in partition_data and with values calculated using the get_energetics function

Usage

get_energetics_summary(energetics, error_metric = "ci", conf_int = 0.95)
get_energetics_summary(energetics, error_metric = "ci", conf_int = 0.95)

Arguments

`energetics`	a data.table of Seahorse OCR and ECAR rates (from `get_energetics`)
`error_metric`	Whether to calculate error as standard deviation (`"sd"`) or confidence intervals (`"ci"`)
`conf_int`	The confidence interval percentage. Should be between 0 and 1

Value

a list of groups from the data

Examples

rep_list <- system.file("extdata", package = "ceas") |>
  list.files(pattern = "*.xlsx", full.names = TRUE)
seahorse_rates <- read_data(rep_list, sheet = 2)
partitioned_data <- partition_data(seahorse_rates)
energetics_list <- get_energetics(partitioned_data, ph = 7.4, pka = 6.093, buffer = 0.1)
energetics_summary <- get_energetics_summary(energetics_list)
head(energetics_summary[, c(1:5)], n = 10)
head(energetics_summary[, c(1, 2, 6, 7)], n = 10)
rep_list <- system.file("extdata", package = "ceas") |>
  list.files(pattern = "*.xlsx", full.names = TRUE)
seahorse_rates <- read_data(rep_list, sheet = 2)
partitioned_data <- partition_data(seahorse_rates)
energetics_list <- get_energetics(partitioned_data, ph = 7.4, pka = 6.093, buffer = 0.1)
energetics_summary <- get_energetics_summary(energetics_list)
head(energetics_summary[, c(1:5)], n = 10)
head(energetics_summary[, c(1, 2, 6, 7)], n = 10)

Rates summary

Description

Summarize OCR and ECAR as mean and bounded standard deviations or standard error with confidence intervals

Usage

get_rate_summary(
  seahorse_rates,
  measure = "OCR",
  assay,
  error_metric = "ci",
  conf_int = 0.95
)
get_rate_summary(
  seahorse_rates,
  measure = "OCR",
  assay,
  error_metric = "ci",
  conf_int = 0.95
)

Arguments

`seahorse_rates`	data.table Seahorse OCR and ECAR rates (imported using `read_data` function)
`measure`	Whether to calculate summary for `"OCR"` or `"ECAR"`
`assay`	What assay to calculate summary for (e.g. "MITO" or "GLYCO")
`error_metric`	Whether to calculate error as standard deviations (`"sd"`) or confidence intervals (`"ci"`)
`conf_int`	The confidence interval percentage. Should be between 0 and 1

Value

a data.table with means, standard deviations/standard error with bounds around the mean(sd or confidence intervals)

Examples

rep_list <- system.file("extdata", package = "ceas") |>
  list.files(pattern = "*.xlsx", full.names = TRUE)
seahorse_rates <- read_data(rep_list, sheet = 2)
rates <- get_rate_summary(
  seahorse_rates,
  measure = "OCR",
  assay = "MCIO",
  error_metric = "ci",
  conf_int = 0.95
)
head(rates, n = 10)
rep_list <- system.file("extdata", package = "ceas") |>
  list.files(pattern = "*.xlsx", full.names = TRUE)
seahorse_rates <- read_data(rep_list, sheet = 2)
rates <- get_rate_summary(
  seahorse_rates,
  measure = "OCR",
  assay = "MCIO",
  error_metric = "ci",
  conf_int = 0.95
)
head(rates, n = 10)

Bioenergetic Scope Plot Shortcut

Description

Wrapper to create a 2D plot visualizing the mean and standard deviation basal and maximal ATP production from glycolysis and OXPHOS for each experimental group Create a Bioenergetic scope plot from input Seahorse Wave export, long-form rates excel files

Usage

make_bioscope_plot(rep_list, ph, pka, buffer, sheet = 2)
make_bioscope_plot(rep_list, ph, pka, buffer, sheet = 2)

Arguments

`rep_list`	A list of Seahorse Wave excel export files. One file per replicate. Group all replicates for a given experiment in a single folder, and write that folder's path in "seahorse_data". You can use 'list.files("seahorse_data") "full.names=TRUE") to get the paths to the files.
`ph`	pH value for energetics calculation (for XF Media, 7.5)
`pka`	pKa value for energetics calculation (for XF Media, 6.063)
`buffer`	buffer for energetics calculation (for XF Media, 0.1 mpH/pmol H+)
`sheet`	The number of the excel sheet containing the long-form Seahorse data. Default is 2 because the long-form output from Seahorse Wave is on sheet 2

Value

a ggplot

Examples

rep_list <- system.file("extdata", package = "ceas") |>
  list.files(pattern = "*.xlsx", full.names = TRUE)
make_bioscope_plot(rep_list, ph = 7.4, pka = 6.093, buffer = 0.1)
rep_list <- system.file("extdata", package = "ceas") |>
  list.files(pattern = "*.xlsx", full.names = TRUE)
make_bioscope_plot(rep_list, ph = 7.4, pka = 6.093, buffer = 0.1)

Normalize Seahorse data

Description

Normalizes input data according to cell number or $\mu$ g of protein. It assumes your data is background normalized.

Usage

normalize(seahorse_rates, norm_csv)
normalize(seahorse_rates, norm_csv)

Arguments

`seahorse_rates`	The seahorse rates table read by the `read_data()` function.
`norm_csv`	A csv file with the experimental groups in column 1 and cell count or $\mu$ g of protein in column 2. Headers are ignored.

Details

This normalization is distinct from the background normalization done by the Wave software. If the data are not background normalized, read_data() will output a warning showing rows with OCR, ECAR and PER values greater than 0.

Value

a normalzed seahorse_rates data.table

Examples

rep_list <- system.file("extdata", package = "ceas") |>
  list.files(pattern = "*.xlsx", full.names = TRUE)
norm_csv <- system.file("extdata", package = "ceas") |>
  list.files(pattern = "norm.csv", full.names = TRUE)
read.csv(norm_csv)
seahorse_rates <- read_data(rep_list, sheet = 2)
head(seahorse_rates, n = 10)
seahorse_rates.normalized <- normalize(seahorse_rates, norm_csv)
head(seahorse_rates.normalized, n = 10)
rep_list <- system.file("extdata", package = "ceas") |>
  list.files(pattern = "*.xlsx", full.names = TRUE)
norm_csv <- system.file("extdata", package = "ceas") |>
  list.files(pattern = "norm.csv", full.names = TRUE)
read.csv(norm_csv)
seahorse_rates <- read_data(rep_list, sheet = 2)
head(seahorse_rates, n = 10)
seahorse_rates.normalized <- normalize(seahorse_rates, norm_csv)
head(seahorse_rates.normalized, n = 10)

Organize Seahorse Data

Description

Organizes Seahorse OCR and ECAR rates based on defined time points (i.e. the Measurement column) during the experiment. This time point can be specified if you are modifying the Mito and Glyco Stress Test (i.e. from 3 measurements per cycle to X measurements)

Usage

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
)
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
)

Arguments

`seahorse_rates`	A data.table of OCR and ECAR rates returned by `read_data`
`assay_types`	A list that configures data partitioning based on the type of assay. See details.
`basal_tp`	Basal respiration time point. Must be less than `uncoupled_tp`
`uncoupled_tp`	ATP-coupled respiration time point. Must be less than `maxresp_tp`
`maxresp_tp`	Maximal uncoupled respiration time point. Must be less than `nonmito_tp`
`nonmito_tp`	Non-mitochondrial respiration time point. Must be larger than `maxresp_tp`
`no_glucose_glyc_tp`	No glucose added acidification time point. Must be less than `glucose_glyc_tp`
`glucose_glyc_tp`	Glucose-associated acidification time point. Must be less than `max_glyc_tp`
`max_glyc_tp`	Maximal acidification time point. Must be less than `twodg_glyc_tp`

Details

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.

partition_data sets up the rates data for ATP calculations by the get_energetics function. To do this, it takes a list assay_types with the named values basal, uncoupled, maxresp, nonmito, no_glucose_glyc, glucose_glyc, and max_glyc. In the default setting, it is configured for an experiment with both Mito and Glyco assays. However, partitioning can be configured for other experimental conditions.

Only MITO data:

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
)

Data according to Mookerjee et al. 2017 J Biol Chem;292:7189-207.

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
)

Also see the vignette.

Value

a list of named time points from each assay cycle

Examples

rep_list <- system.file("extdata", package = "ceas") |>
  list.files(pattern = "*.xlsx", full.names = TRUE)
seahorse_rates <- read_data(rep_list, sheet = 2)
partitioned_data <- partition_data(seahorse_rates)
rep_list <- system.file("extdata", package = "ceas") |>
  list.files(pattern = "*.xlsx", full.names = TRUE)
seahorse_rates <- read_data(rep_list, sheet = 2)
partitioned_data <- partition_data(seahorse_rates)

Rate plot

Description

Generate OCR and ECAR plots

Usage

rate_plot(
  seahorse_rates,
  measure = "OCR",
  assay = "MITO",
  error_bar = "ci",
  conf_int = 0.95,
  group_label = "Experimental group"
)
rate_plot(
  seahorse_rates,
  measure = "OCR",
  assay = "MITO",
  error_bar = "ci",
  conf_int = 0.95,
  group_label = "Experimental group"
)

Arguments

`seahorse_rates`	data.table Seahorse OCR and ECAR rates (imported using `read_data` function)
`measure`	Whether to plot `"OCR"` or `"ECAR"`
`assay`	What assay to plot (e.g. "MITO" or "GLYCO")
`error_bar`	Whether to plot error bars as standard deviation (`"sd"`) or confidence intervals (`"ci"`)
`conf_int`	The confidence interval percentage. Should be between 0 and 1
`group_label`	Label for the experimental group to populate the legend title

Value

a ggplot

Examples

rep_list <- system.file("extdata", package = "ceas") |>
  list.files(pattern = "*.xlsx", full.names = TRUE)
seahorse_rates <- read_data(rep_list, sheet = 2)
rate_plot(seahorse_rates, measure = "OCR", error_bar = "ci", conf_int = 0.95)
rep_list <- system.file("extdata", package = "ceas") |>
  list.files(pattern = "*.xlsx", full.names = TRUE)
seahorse_rates <- read_data(rep_list, sheet = 2)
rate_plot(seahorse_rates, measure = "OCR", error_bar = "ci", conf_int = 0.95)

Read Seahorse Wave Excel File

Description

Reads input seahore data from an excel Seahorse Wave File. It assumes your data is background normalized.

Usage

read_data(rep_list, norm = NULL, sheet = 2, delimiter = " ")
read_data(rep_list, norm = NULL, sheet = 2, delimiter = " ")

Arguments

`rep_list`	A list of Seahorse Wave excel export files. One file per replicate. If your data is in a directory called "seahorse_data", use `list.files("seahorse_data", pattern = "*.xlsx", full.names = TRUE)` to make a list of the excel files.
`norm`	A csv file with the experimental groups and their normalization values. Leave unset if normalization is not required. See `normalize()`.
`sheet`	The number of the excel sheet containing the long-form Seahorse data. Default is 2 because the long-form output from Seahorse Wave is on sheet 2
`delimiter`	The delimiter between the group name and the assay type in the Group column of the wave output. e.g. "Group1 MITO" would use a space character as delimiter.

Value

a seahorse_rates table

Examples

rep_list <- system.file("extdata", package = "ceas") |>
  list.files(pattern = "*.xlsx", full.names = TRUE)
seahorse_rates <- read_data(rep_list, sheet = 2)
head(seahorse_rates, n = 10)

# normalization
norm_csv <- system.file("extdata", package = "ceas") |>
  list.files(pattern = "norm.csv", full.names = TRUE)
seahorse_rates.norm <- read_data(rep_list, norm = norm_csv, sheet = 2)
head(seahorse_rates.norm, n = 10)
rep_list <- system.file("extdata", package = "ceas") |>
  list.files(pattern = "*.xlsx", full.names = TRUE)
seahorse_rates <- read_data(rep_list, sheet = 2)
head(seahorse_rates, n = 10)

# normalization
norm_csv <- system.file("extdata", package = "ceas") |>
  list.files(pattern = "norm.csv", full.names = TRUE)
seahorse_rates.norm <- read_data(rep_list, norm = norm_csv, sheet = 2)
head(seahorse_rates.norm, n = 10)

Package 'ceas'

Help Index

ATP Plot

Description

Usage

Arguments

Details

Value

Examples

Bioenergetic Scope Plot

Description

Usage

Arguments

Value

Examples

Calculate ATP Production from OXPHOS and Glycolysis

Description

Usage

Arguments

Details

Value

Examples

Calculate ATP Production Mean and Standard Deviation

Description

Usage

Arguments

Value

Examples

Rates summary

Description

Usage

Arguments

Value

Examples

Bioenergetic Scope Plot Shortcut

Description

Usage

Arguments

Value

Examples

Normalize Seahorse data

Description

Usage

Arguments

Details

Value

Examples

Organize Seahorse Data

Description

Usage

Arguments

Details

Value

Examples

Rate plot

Description

Usage

Arguments

Value

Examples

Read Seahorse Wave Excel File

Description

Usage

Arguments

Value

Examples