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Read Seahorse Wave Excel File

Source: R/reader.R
read_data.Rd

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 = " ",
  norm_column = "exp_group",
  norm_method = "minimum"
)

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.

norm_column

Whether to normalize by "Well" or "exp_group" column. The first column of the normalization csv provided should match this value.

norm_method

How to normalize each well or experimental group (specified by norm_column):

  • by its corresponding row in the norm csv ("self") or

  • by the minimum of the measure column in the provided norm csv ("minimum").

See the normalize() function for more details.

Value

a seahorse_rates table

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), use sep_reps = TRUE to 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 by well using raw cell count or protein quantity
norm_csv <- system.file("extdata", package = "ceas") |>
  list.files(pattern = "well_norm.csv", full.names = TRUE)
seahorse_rates.norm <- read_data(
  rep_list,
  norm = norm_csv,
  norm_column = "well",
  norm_method = "self",
  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 0.00000000 0.000000000 0.00000000 Background
#>  2:           1    A02 1.304765 0.06104851 0.006129059 0.06689542    Group_1
#>  3:           1    A03 1.304765 0.05599749 0.006050306 0.06517735    Group_1
#>  4:           1    A04 1.304765 0.06402640 0.009278776 0.09499830    Group_2
#>  5:           1    A05 1.304765 0.07154218 0.010654670 0.10939105    Group_2
#>  6:           1    A06 1.304765 0.05488070 0.007939376 0.08172779    Group_2
#>  7:           1    A07 1.304765 0.08425456 0.010403869 0.10710374    Group_3
#>  8:           1    A08 1.304765 0.06984781 0.008954729 0.09148714    Group_3
#>  9:           1    A09 1.304765 0.06668683 0.008906184 0.09054508    Group_3
#> 10:           1    A10 1.304765 0.04095365 0.005493950 0.06048068    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