o2eie

The goal of o2eie is to provide an easy and consistent workflow for processing O2 data generated from the O2-microcompensation devices at the EIE labs.

Preparation

To start you need to have all your weighing sheets formatted to make sure that the data can be read correctly. Use the template available in “inst/template/w_template.xlsx” to align your entries properly. Be sure also to include the names of the raw data files (with extension) at the appropriate place and put all the files in the same folder.

Installation

You can install the o2eie package from GitHub by running:

remotes::install_github("gpatoine/o2eie")

Example

The easiest way to process all the files is using the function o2_process_all() which is a comprehensive wrapper for reading the data and calculating the main measurements. The function only requires a vector of weighing sheets, and it returns a tibble (a modern data.frame) with all raw values and derived measurements, including basal respiration, microbial biomass, and microbial growth.

library(o2eie)

Normally, you can write the file paths manually, or for example use all the Excel files from a folder using list.files().

weights <- list.files("myfolder", pattern = "\\.xlsx", full.names = T)

In this case, we are using the example dataset from the package, with two weighing sheets. We use regular expressions to find the weighing sheets that start with a “w” and end with “.xlsx”.

weights <- list.files(system.file("extdata", package = "o2eie"),
                      pattern = "^w_.*\\.xlsx",
                      full.names = T)

weights
#> [1] "C:/Users/gp63dyte/R/win-library/4.3/o2eie/extdata/w_DIETER_6391-6420_2019-11-26_Project1.xlsx"
#> [2] "C:/Users/gp63dyte/R/win-library/4.3/o2eie/extdata/w_DIETER_6661-6690_2020-02-10_Project1.xlsx"

o2meas <- o2_process_all(files = weights)
#> Processing w_DIETER_6391-6420_2019-11-26_Project1.xlsx
#> Processing w_DIETER_6661-6690_2020-02-10_Project1.xlsx

Reports

To review the datapoints used for individual measurements, it is possible to generate PDF reports with time series of incubation.

o2meas %>% 
  bas_report("bas.pdf") %>% 
  cmic_report("cmic.pdf") %>% 
  mgrow_report("mgrowth.pdf")

For example, the PDF report for basal respiration looks like this:

Manually adjusting calculation periods

It is often necessary to check the datapoints used for basal respiration and cmic measurements individually using the reports. In cases where the chosen timepoints are not satisfactory, we can adjust the period considered for the calculation of basal respiration and microbial biomass. The easiest way to do this is by creating a tibble with the function tribble() that contains the new values, with column names “name_c” and “times”, as shown below, and use set_bas_times() or set_cmic_times() provided in the package.

o2_bas() is used to recalculate the basal respiration and automatically uses the column “bas_set” (created by set_bas_times()) if it is available.

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

adjust_bas <- tribble(
  ~name_c, ~times,
  "326_04_1", c(10:17, 19:20),
  "121_2", c(25:35)
)

o2meas <- o2meas %>% 
  set_bas_times(tib = adjust_bas) %>% 
  o2_bas(only_sets = TRUE)

# View the changes
o2meas %>% 
  filter(name_c %in% adjust_bas$name_c) %>% 
  select(name_c, bas_set)
#> # A tibble: 2 × 2
#>   name_c   bas_set   
#>   <chr>    <list>    
#> 1 326_04_1 <int [10]>
#> 2 121_2    <int [11]>

The report can then be generated again with the changes implemented.

bas_report(o2meas, "bas_updated.pdf")

Vignette

You can read the vignette with a slightly extended example: o2eie vignette

Do not hesitate to contact me if you run into issues with the package or would like to adapt it to your use case.

Contact: [email protected]