Estimate mean and confidence intervals for ATP measures using a mixed-effects model
Source:R/rate_plot.R
rates_lme_summary.RdEstimates 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
Arguments
- measure
Whether to plot
"OCR"or"ECAR"- assay
What assay to plot (e.g. "MITO" or "GLYCO")
- rates
a data.table of Seahorse OCR and ECAR rates (from
get_energetics)- conf_int
The confidence interval percentage. Should be between 0 and 1
- ci_method
The method used to compute confidence intervals for the mixed-effects model:
"Wald","profile", or"boot"passed tolme4::confint.merMod().
Examples
rep_list <- system.file("extdata", package = "ceas") |>
list.files(pattern = "*.xlsx", full.names = TRUE)
seahorse_rates <- read_data(rep_list, sheet = 2)
rates_lme_summary(
measure = "OCR",
assay = "MITO",
rates = seahorse_rates,
conf_int = 0.95,
ci_method = "Wald"
)
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> exp_group Measurement mean lower_bound upper_bound
#> <char> <num> <num> <num> <num>
#> 1: Group_1 1 307.89166 291.96673 323.81660
#> 2: Group_2 1 348.81974 339.21203 358.42746
#> 3: Group_3 1 388.77691 379.16919 398.38462
#> 4: Group_4 1 226.10023 216.28587 235.91458
#> 5: Group_1 2 292.75576 276.83832 308.67320
#> 6: Group_2 2 329.93782 321.71013 338.16550
#> 7: Group_3 2 369.78727 361.55959 378.01496
#> 8: Group_4 2 216.02105 207.61640 224.42569
#> 9: Group_1 3 288.08473 274.17510 301.99437
#> 10: Group_2 3 324.12258 316.38460 331.86056
#> 11: Group_3 3 363.67394 355.93596 371.41192
#> 12: Group_4 3 213.01895 205.11454 220.92336
#> 13: Group_1 4 101.19365 91.72564 110.66166
#> 14: Group_2 4 117.85782 113.52722 122.18843
#> 15: Group_3 4 136.45736 132.12675 140.78796
#> 16: Group_4 4 83.47057 79.04682 87.89431
#> 17: Group_1 5 97.45195 88.44895 106.45496
#> 18: Group_2 5 119.40534 114.82492 123.98576
#> 19: Group_3 5 132.45771 127.87729 137.03813
#> 20: Group_4 5 88.50989 83.83096 93.18882
#> 21: Group_1 6 97.55136 87.45242 107.65030
#> 22: Group_2 6 128.25149 122.51076 133.99222
#> 23: Group_3 6 136.23777 130.49704 141.97850
#> 24: Group_4 6 106.28409 100.41989 112.14829
#> 25: Group_1 7 443.41553 418.06763 468.76342
#> 26: Group_2 7 572.60816 540.62823 604.58810
#> 27: Group_3 7 554.05711 522.07717 586.03704
#> 28: Group_4 7 214.31752 181.64977 246.98528
#> 29: Group_1 8 457.96943 438.71539 477.22347
#> 30: Group_2 8 563.15354 536.49754 589.80955
#> 31: Group_3 8 502.38164 475.72563 529.03765
#> 32: Group_4 8 190.92705 163.69773 218.15637
#> 33: Group_1 9 481.07496 461.12582 501.02411
#> 34: Group_2 9 557.32512 529.70678 584.94346
#> 35: Group_3 9 474.22872 446.61038 501.84706
#> 36: Group_4 9 180.99782 152.78547 209.21017
#> 37: Group_1 10 53.26419 47.68983 58.83855
#> 38: Group_2 10 60.79879 57.54308 64.05449
#> 39: Group_3 10 70.38167 67.12596 73.63738
#> 40: Group_4 10 46.19680 42.87107 49.52253
#> 41: Group_1 11 54.98763 50.31468 59.66057
#> 42: Group_2 11 62.88026 60.26676 65.49376
#> 43: Group_3 11 69.75906 67.14555 72.37256
#> 44: Group_4 11 43.25444 40.58473 45.92416
#> 45: Group_1 12 53.66469 49.25649 58.07289
#> 46: Group_2 12 61.64285 59.07303 64.21268
#> 47: Group_3 12 67.32687 64.75705 69.89670
#> 48: Group_4 12 40.82449 38.19939 43.44959
#> exp_group Measurement mean lower_bound upper_bound