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Rates summary

Source: R/rate_plot.R
get_rate_summary.Rd

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,
  model = "ols",
  error_metric = "ci",
  conf_int = 0.95,
  sep_reps = FALSE,
  ci_method = "Wald"
)

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")

model

The model used to estimate mean and confidence intervals:

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

sep_reps

Whether to calculate summary statistics on the groups with replicates combined. The current default FALSE combines replicates, but future releases will default to TRUE providing replicate-specific summaries.

ci_method

The method used to compute confidence intervals for the mixed-effects model: "Wald", "profile", or "boot" passed to lme4::confint.merMod().

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)
combined_reps <- get_rate_summary(
  seahorse_rates,
  measure = "OCR",
  assay = "MITO",
  model = "ols",
  error_metric = "ci",
  conf_int = 0.95,
  sep_reps = FALSE
)
head(combined_reps, n = 10)
#>     exp_group Measurement     mean       sd       se lower_bound upper_bound
#>        <char>       <num>    <num>    <num>    <num>       <num>       <num>
#>  1:   Group_1           1 307.8917 20.68089 4.409180    299.2498    316.5335
#>  2:   Group_2           1 348.8197 16.65077 3.398824    342.1582    355.4813
#>  3:   Group_3           1 388.7769 19.33691 3.947130    381.0407    396.5131
#>  4:   Group_4           1 226.1002 15.78665 3.365726    219.5035    232.6969
#>  5:   Group_1           2 292.7558 19.41210 4.138674    284.6441    300.8674
#>  6:   Group_2           2 329.9378 12.87193 2.627473    324.7881    335.0876
#>  7:   Group_3           2 369.7873 17.35845 3.543279    362.8426    376.7320
#>  8:   Group_4           2 216.0210 14.42133 3.074639    209.9949    222.0472
#>  9:   Group_1           3 288.0847 18.63144 3.972235    280.2993    295.8702
#> 10:   Group_2           3 324.1226 11.68031 2.384234    319.4496    328.7956

# separate replicates
sep_reps <- get_rate_summary(
  seahorse_rates,
  measure = "OCR",
  assay = "MITO",
  model = "ols",
  error_metric = "ci",
  conf_int = 0.95,
  sep_reps = TRUE
)
head(sep_reps, n = 10)
#>     exp_group Measurement replicate     mean       sd       se lower_bound
#>        <char>       <num>    <fctr>    <num>    <num>    <num>       <num>
#>  1:   Group_1           1         1 317.2648 21.57755 6.505875    304.5136
#>  2:   Group_2           1         1 349.2271 17.16613 4.955436    339.5147
#>  3:   Group_3           1         1 399.4677 14.30962 4.130832    391.3714
#>  4:   Group_4           1         1 236.0472 13.21393 3.984150    228.2384
#>  5:   Group_1           2         1 302.3107 20.03568 6.040984    290.4705
#>  6:   Group_2           2         1 330.4760 13.43898 3.879499    322.8723
#>  7:   Group_3           2         1 381.0119 12.00461 3.465432    374.2198
#>  8:   Group_4           2         1 225.7442 11.59172 3.495034    218.8940
#>  9:   Group_1           3         1 296.7702 19.66343 5.928748    285.1501
#> 10:   Group_2           3         1 323.9365 12.40370 3.580640    316.9185
#>     upper_bound
#>           <num>
#>  1:    330.0161
#>  2:    358.9396
#>  3:    407.5640
#>  4:    243.8560
#>  5:    314.1508
#>  6:    338.0797
#>  7:    387.8041
#>  8:    232.5943
#>  9:    308.3903
#> 10:    330.9544

# mixed effects model
reps_as_random_effects <- get_rate_summary(
  seahorse_rates,
  measure = "OCR",
  assay = "MITO",
  model = "mixed",
  error_metric = "ci",
  conf_int = 0.95,
  sep_reps = FALSE
)
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
head(reps_as_random_effects, n = 10)
#>     exp_group Measurement     mean lower_bound upper_bound
#>        <char>       <num>    <num>       <num>       <num>
#>  1:   Group_1           1 307.8917    291.9667    323.8166
#>  2:   Group_2           1 348.8197    339.2120    358.4275
#>  3:   Group_3           1 388.7769    379.1692    398.3846
#>  4:   Group_4           1 226.1002    216.2859    235.9146
#>  5:   Group_1           2 292.7558    276.8383    308.6732
#>  6:   Group_2           2 329.9378    321.7101    338.1655
#>  7:   Group_3           2 369.7873    361.5596    378.0150
#>  8:   Group_4           2 216.0210    207.6164    224.4257
#>  9:   Group_1           3 288.0847    274.1751    301.9944
#> 10:   Group_2           3 324.1226    316.3846    331.8606