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Normalize Seahorse data

Source: R/normalize.R
normalize.Rd

Normalizes input data according to a sample normalization measure (e.g. cell number or \(\mu\)g of protein). It assumes your data is background normalized.

Usage

normalize(
  seahorse_rates,
  norm_csv,
  norm_column = "well",
  norm_method = "minimum"
)

Arguments

seahorse_rates

The seahorse rates table read by the read_data() function.

norm_csv

A csv file with either well or experimental group in column 1 and the sample normalization measure in column 2. Headers are ignored.

norm_column

Whether to normalize by "well" or "exp_group" group. 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 details.

Value

a normalized seahorse_rates data.table

Details

This normalization is distinct from the background normalization done by the Wave software. If the data are not background normalized, read_data() will output a warning showing rows with OCR, ECAR and PER values greater than 0.

Normalization Methods

When norm_method is set to "self", each OCR, ECAR, and PER value is divided by the measure it"self". OCR and ECAR values are divided by the corresponding raw value in the "measure" column: an intra-well or experimental group normalization. Each normalized value is then interpreted as pmol/min per measure (e.g. pmol/min/cell or pmol/min/\(\mu\)g of protein.

When set to "minimum", each OCR, ECAR, and PER value is normalized by the minimum value in the norm_csv "measure" column. In this method, every "measure" column's value in the provided CSV file is divided by the lowest of the "measure" values to get a normalization factor for each well or experimental group. The OCR, ECAR, and PER values in each well or experimental group are divided by their corresponding normalization factors. Compared to "self", this is an inter-well/experimental group normalization based on the lowest "measure". The results may be interpreted as pmol/min per minimum of the measure (eg: group cell count or \(\mu\)g of protein.)

Examples

rep_list <- system.file("extdata", package = "ceas") |>
  list.files(pattern = "*.xlsx", full.names = TRUE)
norm_csv <- system.file("extdata", package = "ceas") |>
  list.files(pattern = "^norm.csv", full.names = TRUE)
read.csv(norm_csv)
#>   exp_group measure
#> 1   Group_1   30000
#> 2   Group_2   30000
#> 3   Group_3    5000
#> 4   Group_4    5000
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
# normalize by experimental group based on the minimum cell count or protein quantity
seahorse_rates.normalized <- normalize(
  seahorse_rates,
  norm_csv,
  norm_column = "exp_group",
  norm_method = "minimum"
)
head(seahorse_rates.normalized, 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