Cyano Interlab study

This a repository to analyze the data from the Cyano Interlab study.

Contents

• Project structure
• Input data
• Analysis

Project structure

This project contains the following directories:

• data/: contains raw data files
• figures/: contains the figures for the manuscript
• R/: contains all functions for data loading and processing, and figure generation
• reports/: contains reports to load raw data, process it and export it and to create the figures for the manuscript
• _targets: directory created by the targets package required to run he workflow
• renv: directory created by the renv package required to manage project dependencies

Input data

Participants submitted data in standard spreadsheet templates from experimental runs and reference measurements. These are stored indata/experiments/ and data//reference/ respectively. In addition, two extra datasets are needed to run the analysis. data/dilutions contains two spreadsheets with information about the dilutions factors applied by each participant in the micro-titer plates. data/location_labels contains a table to match the location name in the raw files to the names that will be displayed in the figures.

Analysis

The analysis of this project is done using a targets pipeline. This pipeline includes reading raw files, wrangling data, normalization of plate-reader measurements, and creating the figures and statistical analyses presented in the manuscript.

The code to run the pipeline is found in the _targets.R file and can be executed by running targets::tar_make(). This pipeline makes use of custom functions stored in the R/ directory. Below, we describe every step of the process:

Reading data

Raw data is loaded with the functions read_dilutions(), read_raw_experiment_data(), and read_raw_reference_data(). The first function will create a regular data frame. Tast two, will create a list-column tibble containing a row per lab, experimental run and dataset, stored in the raw_data column. These tibbles can be obtained by providing the corresponding path and file name pattern to each function or by using targets::tar_load() after running the pipeline:

targets::tar_load(data.experiments.raw)
data.experiments.raw

## # A tibble: 170 × 5
##    location  sheet                           raw_data            exper…¹ exper…²
##    <chr>     <chr>                           <list>              <chr>   <chr>  
##  1 Amsterdam Fluorescence Plate Reader       <tibble [28 × 11]>  202111… 202111…
##  2 Amsterdam OD Plate Reader                 <tibble [28 × 11]>  202111… 202111…
##  3 Amsterdam OD 730 nm Spectrophotometer     <tibble [8 × 10]>   202111… 202111…
##  4 Amsterdam Chlorophyll 665 nm Spectrophoto <tibble [8 × 6]>    202111… 202111…
##  5 Amsterdam Spectrum Spectrophotometer      <tibble [351 × 21]> 202111… 202111…
##  6 Amsterdam Fluorescence Plate Reader       <tibble [28 × 11]>  202111… 202111…
##  7 Amsterdam OD Plate Reader                 <tibble [28 × 11]>  202111… 202111…
##  8 Amsterdam OD 730 nm Spectrophotometer     <tibble [8 × 10]>   202111… 202111…
##  9 Amsterdam Chlorophyll 665 nm Spectrophoto <tibble [8 × 6]>    202111… 202111…
## 10 Amsterdam Spectrum Spectrophotometer      <tibble [351 × 21]> 202111… 202111…
## # … with 160 more rows, and abbreviated variable names ¹​experiment_date,
## #   ²​experiment_id

Processing raw data

Two functions (process_experiments_data() and process_reference_data()) are used to process the tibbles generated in the previous step. These functions will wrangle the raw data into the right format and process the plate-reader (background-signal correction and reference-strain normalization of relative fluorescence units). The output is a list-column tibble, containing a dataset per row. These tibbles can be obtained by providing the corresponding datasets to each function or by using targets::tar_load() after running the pipeline:

targets::tar_load(data.experiments.processed)
data.experiments.processed

## # A tibble: 9 × 2
##   data_id          data                  
##   <chr>            <list>                
## 1 pr.fl.raw        <tibble [6,664 × 10]> 
## 2 pr.od.raw        <tibble [6,727 × 10]> 
## 3 pr.fl            <tibble [8,232 × 11]> 
## 4 pr.od            <tibble [8,295 × 11]> 
## 5 pr.bc            <tibble [1,904 × 16]> 
## 6 pr.norm          <tibble [1,904 × 17]> 
## 7 sp.od            <tibble [1,904 × 8]>  
## 8 sp.full.spectrum <tibble [279,180 × 8]>
## 9 chl              <tibble [360 × 9]>

The table shows the content of each dataset:

Dataset	Description
pr.fl.raw	Long-format plate-reader fluorescence
pr.od.raw	Long-format plate-reader OD
pr.fl	Long-format, dilution corrected, plate-reader fluorescence
pr.od	Long-format, dilution corrected, plate-reader OD
pr.bc	Long-format, dilution corrected, background-signal corrected, plate-reader fluorescence
pr.norm	Long-format, dilution corrected, background-signal corrected, reference-strain normalized plate-reader RFU
sp.od	Long-format spectrophotometer OD
sp.full.spectrum	Long-format spectrophotometer full spectrum
chl	Long-format spectrophotometer chlorophyll content

Processed datasets can be accessed with:

targets::tar_load(data.experiments.processed)
# example to extract spectrophotometer OD
data.experiments.processed |> 
      dplyr::filter(data_id == "sp.od") |> 
      tidyr::unnest(data) |> 
      dplyr::select(-data_id)

## # A tibble: 1,904 × 8
##    location  strain induction bio_replicate experiment_d…¹ exper…² OD_730 time_h
##    <chr>     <chr>  <chr>             <dbl> <chr>          <chr>    <dbl>  <dbl>
##  1 Amsterdam EVC    -                     1 20211118       202111…  0.532      0
##  2 Amsterdam EVC    -                     1 20211118       202111…  0.501      2
##  3 Amsterdam EVC    -                     1 20211118       202111…  0.574      4
##  4 Amsterdam EVC    -                     1 20211118       202111…  0.584      5
##  5 Amsterdam EVC    -                     1 20211118       202111…  0.627      6
##  6 Amsterdam EVC    -                     1 20211118       202111…  0.658      7
##  7 Amsterdam EVC    -                     1 20211118       202111…  1.89      24
##  8 Amsterdam EVC    +                     1 20211118       202111…  0.532      0
##  9 Amsterdam EVC    +                     1 20211118       202111…  0.512      2
## 10 Amsterdam EVC    +                     1 20211118       202111…  0.542      4
## # … with 1,894 more rows, and abbreviated variable names ¹​experiment_date,
## #   ²​experiment_id

Figures

The code to produce each figure in the manuscript can be found in the R/ directory. Each figure is created with a custom function stored in a separate file. The targets pipeline includes these functions and the output is stored in the figures/ directory (.tiff files are not include in this repository due to file size).

Dependencies

We provide renv files to facilitate dependency management. We include the .Rprofile file so when cloning the repository and opening the project, renv should be automatically downloaded and installed and project dependencies can be restore with renv::restore(). See this resource for futher details.