partition_data {ceas}

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
)

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)