Reads input seahore data from an excel Seahorse Wave File. It assumes your data is background normalized.
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. Add multiple replicates with care - see details.- 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.
Details
Although ceas enables integration of multiple biological and/or technical replicates, previous work has reported high inter-plate variation (Yepez et. al 2018). If you don't want your replicate data combined, you can either:
make sure that the names of the common groups between the replicates are different.
in downstream analyses (
get_energetics_summary,bioscope_plot,rate_plot,atp_plot), usesep_reps = TRUEto do all calculations and plotting separately for each replicate.
NOTE: to maintain backwards compatibility sep_reps is currently
FALSE by default, but will be set to TRUE in a future release.
References
Yépez et al. 2018 OCR-Stats: Robust estimation and statistical testing of mitochondrial respiration activities using Seahorse XF Analyzer PLOS ONE 2018;13:e0199938. doi:10.1371/journal.pone.0199938
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)
#> Measurement Well Time OCR ECAR PER exp_group
#> <num> <char> <num> <num> <num> <num> <char>
#> 1: 1 A01 1.304765 0.0000 0.00000 0.0000 Background
#> 2: 1 A02 1.304765 305.2426 30.64529 334.4771 Group_1
#> 3: 1 A03 1.304765 307.9862 33.27668 358.4754 Group_1
#> 4: 1 A04 1.304765 339.3399 49.17751 503.4910 Group_2
#> 5: 1 A05 1.304765 321.9398 47.94602 492.2597 Group_2
#> 6: 1 A06 1.304765 323.7962 46.84232 482.1940 Group_2
#> 7: 1 A07 1.304765 379.1455 46.81741 481.9668 Group_3
#> 8: 1 A08 1.304765 391.1478 50.14648 512.3280 Group_3
#> 9: 1 A09 1.304765 393.4523 52.54649 534.2160 Group_3
#> 10: 1 A10 1.304765 217.0543 29.11793 320.5476 Group_4
#> assay_type replicate
#> <char> <fctr>
#> 1: <NA> 1
#> 2: MITO 1
#> 3: MITO 1
#> 4: MITO 1
#> 5: MITO 1
#> 6: MITO 1
#> 7: MITO 1
#> 8: MITO 1
#> 9: MITO 1
#> 10: MITO 1
# 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)
#> Measurement Well Time OCR ECAR PER exp_group
#> <num> <char> <num> <num> <num> <num> <char>
#> 1: 1 A01 1.304765 0.00000 0.000000 0.00000 Background
#> 2: 1 A02 1.304765 50.87376 5.107549 55.74619 Group_1
#> 3: 1 A03 1.304765 51.33103 5.546114 59.74590 Group_1
#> 4: 1 A04 1.304765 56.55665 8.196252 83.91516 Group_2
#> 5: 1 A05 1.304765 53.65663 7.991003 82.04329 Group_2
#> 6: 1 A06 1.304765 53.96603 7.807053 80.36566 Group_2
#> 7: 1 A07 1.304765 379.14553 46.817412 481.96685 Group_3
#> 8: 1 A08 1.304765 391.14776 50.146484 512.32798 Group_3
#> 9: 1 A09 1.304765 393.45230 52.546486 534.21600 Group_3
#> 10: 1 A10 1.304765 217.05432 29.117934 320.54760 Group_4
#> assay_type replicate
#> <char> <fctr>
#> 1: <NA> 1
#> 2: MITO 1
#> 3: MITO 1
#> 4: MITO 1
#> 5: MITO 1
#> 6: MITO 1
#> 7: MITO 1
#> 8: MITO 1
#> 9: MITO 1
#> 10: MITO 1