Multiplate SerolyzeR functionalities

Introduction

In this tutorial we are going to cover all the possible options of reading and writing data from multiple plates implemented in SerolyzeR package.

This tutorial will not be focused on a detailed analysis of the package functionalities that are designed to analyse a single plate at a time, as this is already covered in the .

Reading the data

Firstly, we need to locate our dataset. The SerolyzeR package has a preloaded dataset, which can be found with a command given below:

base_dir <- system.file("extdata", "multiplate_tutorial", package = "SerolyzeR", mustWork = TRUE) # get the filepath of the directory containing all the files

list.files(base_dir) # list all the files

#>  [1] "P1_SEROPED_PBT_62PLEX_PLATE1_03312023_RUN001_layout.xlsx"
#>  [2] "P1_SEROPED_PBT_62PLEX_PLATE1_03312023_RUN001.csv"        
#>  [3] "P10_SEROPED_62PLEX_PLATE10_04062023_RUN004_layout.xlsx"  
#>  [4] "P10_SEROPED_62PLEX_PLATE10_04062023_RUN004.csv"          
#>  [5] "P11_SEROPED_62PLEX_PLATE11_04062023_RUN005_layout.xlsx"  
#>  [6] "P11_SEROPED_62PLEX_PLATE11_04062023_RUN005.csv"          
#>  [7] "P12_SEROPED_62PLEX_PLATE12_04072023_RUN000_layout.xlsx"  
#>  [8] "P12_SEROPED_62PLEX_PLATE12_04072023_RUN000.csv"          
#>  [9] "P13_SEROPED_62PLEX_PLATE13_04072023_RUN001_layout.xlsx"  
#> [10] "P13_SEROPED_62PLEX_PLATE13_04072023_RUN001.csv"          
#> [11] "P14_SEROPED_62PLEX_PLATE14_04072023_RUN002_layout.xlsx"  
#> [12] "P14_SEROPED_62PLEX_PLATE14_04072023_RUN002.csv"          
#> [13] "P2_SEROPED_62PLEX_PLATE2_04042023_RUN000_layout.xlsx"    
#> [14] "P2_SEROPED_62PLEX_PLATE2_04042023_RUN000.csv"            
#> [15] "P3_rerun_SEROPED_PLATE3_04042023_RUN002_layout.xlsx"     
#> [16] "P3_rerun_SEROPED_PLATE3_04042023_RUN002.csv"             
#> [17] "P4_SEROPED_62PLEX_PLATE4_04042023_RUN003_layout.xlsx"    
#> [18] "P4_SEROPED_62PLEX_PLATE4_04042023_RUN003.csv"            
#> [19] "P5_SEROPED_62PLEX_PLATE5_04052023_RUN001_layout.xlsx"    
#> [20] "P5_SEROPED_62PLEX_PLATE5_04052023_RUN001.csv"            
#> [21] "P6_SEROPED_62PLEX_PLATE6_04052023_RUN003_layout.xlsx"    
#> [22] "P6_SEROPED_62PLEX_PLATE6_04052023_RUN003.csv"            
#> [23] "P7_SEROPED_62PLEX_PLATE7_04052023_RUN004_layout.xlsx"    
#> [24] "P7_SEROPED_62PLEX_PLATE7_04052023_RUN004.csv"            
#> [25] "P8_SEROPED_62PLEX_PLATE8_04052023_RUN005_layout.xlsx"    
#> [26] "P8_SEROPED_62PLEX_PLATE8_04052023_RUN005.csv"

As you can see, this directory contains multiple files - both raw output from the multiplex machine and the layout files, that help to identify the samples on the plate. Each of the plate files, e.g. P1_SEROPED_PBT_62PLEX_PLATE1_03312023_RUN001.csv contains its corresponding layout file, for instance P1_SEROPED_PBT_62PLEX_PLATE1_03312023_RUN001_layout.xlsx .

We will explain in detail how the directory should be structured in order to read the data properly in the section below. Let us first read the directory.

Reading a whole directory

To read the directory as above, we need to import the library SerolyzeR and run a single command process_dir. This function reads all the files in the directory and processes them. It has an option to generate 3 tabular outputs (similarly to the process_file function). It is: - MFI (mean fluorescence intensity) - the raw input MFI values, with optional blank adjustment only - nMFI (normalised mean fluorescence intensity) - the normalised values, with optional blank adjustment and normalisation to the reference sample - RAU (relative antibody units) - the normalised values, with optional blank adjustment transformed to the relative antibody units

These output types can be selected with the normalisation_types parameter, which is a vector of all tabular outputs to be generated. By default this parameter equals to all possible values - c("RAU", "nMFI", "MFI").

Now, we will discuss how this method works and describe its most important parameters. By default, it saves all the outputs into the input directory, this can be changed with the output_dir parameter. In this tutorial, we will set the output directory to a temporary directory, which can be created with the tempdir() function. We also specify the format of the input files, which is xPONENT in this case. The format parameter can be either set to xPONENT, INTELLIFLEX or NULL. In case the format is NULL, the function will try to infer the format based on the filename. For more details, we refer the reader to the documentation of the ` function.

One of the most useful features of the process_dir function is that it enables us to do all the preprocessing steps in one go. By setting merge_outputs parameter to TRUE, the method will output a single file with all the data from all the plates. This is very useful when we want to analyse the data from multiple plates at once.

This method also supports automatic generation of HTML reports, which could be enabled with setting the parameter generate_reports to TRUE, for the single plate reports, and generate_multiplate_report to TRUE, for the multiplate report. By default both of these values are set to FALSE.

library(SerolyzeR)

output_dir <- file.path(tempdir(), "multiplate-tutorial") # create a temporary directory to store the output data
R.utils::mkdirs(output_dir)

#> [1] TRUE

plates <- process_dir(base_dir, format = "xPONENT", normalisation_types = c("RAU", "nMFI", "MFI"), output_dir = output_dir, merge_outputs = TRUE, return_plates = TRUE, generate_reports = FALSE, generate_multiplate_reports = FALSE)

#> Reading Luminex data from: /home/runner/work/_temp/Library/SerolyzeR/extdata/multiplate_tutorial/P10_SEROPED_62PLEX_PLATE10_04062023_RUN004.csv
#> using format xPONENT
#> 
#> New plate object has been created with name: P10_SEROPED_62PLEX_PLATE10_04062023_RUN004!
#> 
#> Processing plate 'P10_SEROPED_62PLEX_PLATE10_04062023_RUN004'
#> Reading Luminex data from: /home/runner/work/_temp/Library/SerolyzeR/extdata/multiplate_tutorial/P11_SEROPED_62PLEX_PLATE11_04062023_RUN005.csv
#> using format xPONENT
#> 
#> New plate object has been created with name: P11_SEROPED_62PLEX_PLATE11_04062023_RUN005!
#> 
#> Processing plate 'P11_SEROPED_62PLEX_PLATE11_04062023_RUN005'
#> Reading Luminex data from: /home/runner/work/_temp/Library/SerolyzeR/extdata/multiplate_tutorial/P12_SEROPED_62PLEX_PLATE12_04072023_RUN000.csv
#> using format xPONENT
#> 
#> New plate object has been created with name: P12_SEROPED_62PLEX_PLATE12_04072023_RUN000!
#> 
#> Processing plate 'P12_SEROPED_62PLEX_PLATE12_04072023_RUN000'
#> Reading Luminex data from: /home/runner/work/_temp/Library/SerolyzeR/extdata/multiplate_tutorial/P13_SEROPED_62PLEX_PLATE13_04072023_RUN001.csv
#> using format xPONENT
#> 
#> New plate object has been created with name: P13_SEROPED_62PLEX_PLATE13_04072023_RUN001!
#> 
#> Processing plate 'P13_SEROPED_62PLEX_PLATE13_04072023_RUN001'
#> Reading Luminex data from: /home/runner/work/_temp/Library/SerolyzeR/extdata/multiplate_tutorial/P14_SEROPED_62PLEX_PLATE14_04072023_RUN002.csv
#> using format xPONENT
#> 
#> New plate object has been created with name: P14_SEROPED_62PLEX_PLATE14_04072023_RUN002!
#> 
#> Processing plate 'P14_SEROPED_62PLEX_PLATE14_04072023_RUN002'
#> Reading Luminex data from: /home/runner/work/_temp/Library/SerolyzeR/extdata/multiplate_tutorial/P1_SEROPED_PBT_62PLEX_PLATE1_03312023_RUN001.csv
#> using format xPONENT
#> 
#> New plate object has been created with name: P1_SEROPED_PBT_62PLEX_PLATE1_03312023_RUN001!
#> 
#> Processing plate 'P1_SEROPED_PBT_62PLEX_PLATE1_03312023_RUN001'
#> Reading Luminex data from: /home/runner/work/_temp/Library/SerolyzeR/extdata/multiplate_tutorial/P2_SEROPED_62PLEX_PLATE2_04042023_RUN000.csv
#> using format xPONENT
#> 
#> New plate object has been created with name: P2_SEROPED_62PLEX_PLATE2_04042023_RUN000!
#> 
#> Processing plate 'P2_SEROPED_62PLEX_PLATE2_04042023_RUN000'
#> Reading Luminex data from: /home/runner/work/_temp/Library/SerolyzeR/extdata/multiplate_tutorial/P3_rerun_SEROPED_PLATE3_04042023_RUN002.csv
#> using format xPONENT
#> 
#> New plate object has been created with name: P3_rerun_SEROPED_PLATE3_04042023_RUN002!
#> 
#> Processing plate 'P3_rerun_SEROPED_PLATE3_04042023_RUN002'
#> Reading Luminex data from: /home/runner/work/_temp/Library/SerolyzeR/extdata/multiplate_tutorial/P4_SEROPED_62PLEX_PLATE4_04042023_RUN003.csv
#> using format xPONENT
#> 
#> New plate object has been created with name: P4_SEROPED_62PLEX_PLATE4_04042023_RUN003!
#> 
#> Processing plate 'P4_SEROPED_62PLEX_PLATE4_04042023_RUN003'
#> Reading Luminex data from: /home/runner/work/_temp/Library/SerolyzeR/extdata/multiplate_tutorial/P5_SEROPED_62PLEX_PLATE5_04052023_RUN001.csv
#> using format xPONENT
#> 
#> New plate object has been created with name: P5_SEROPED_62PLEX_PLATE5_04052023_RUN001!
#> 
#> Processing plate 'P5_SEROPED_62PLEX_PLATE5_04052023_RUN001'
#> Reading Luminex data from: /home/runner/work/_temp/Library/SerolyzeR/extdata/multiplate_tutorial/P6_SEROPED_62PLEX_PLATE6_04052023_RUN003.csv
#> using format xPONENT
#> 
#> New plate object has been created with name: P6_SEROPED_62PLEX_PLATE6_04052023_RUN003!
#> 
#> Processing plate 'P6_SEROPED_62PLEX_PLATE6_04052023_RUN003'
#> Reading Luminex data from: /home/runner/work/_temp/Library/SerolyzeR/extdata/multiplate_tutorial/P7_SEROPED_62PLEX_PLATE7_04052023_RUN004.csv
#> using format xPONENT
#> 
#> New plate object has been created with name: P7_SEROPED_62PLEX_PLATE7_04052023_RUN004!
#> 
#> Processing plate 'P7_SEROPED_62PLEX_PLATE7_04052023_RUN004'
#> Reading Luminex data from: /home/runner/work/_temp/Library/SerolyzeR/extdata/multiplate_tutorial/P8_SEROPED_62PLEX_PLATE8_04052023_RUN005.csv
#> using format xPONENT
#> 
#> New plate object has been created with name: P8_SEROPED_62PLEX_PLATE8_04052023_RUN005!
#> 
#> Processing plate 'P8_SEROPED_62PLEX_PLATE8_04052023_RUN005'
#> Fitting the models and predicting RAU for each analyte

#> Fitting the models and predicting RAU for each analyte
#> Fitting the models and predicting RAU for each analyte

#> Fitting the models and predicting RAU for each analyte

#> Fitting the models and predicting RAU for each analyte

#> Fitting the models and predicting RAU for each analyte
#> Fitting the models and predicting RAU for each analyte
#> Fitting the models and predicting RAU for each analyte

#> Fitting the models and predicting RAU for each analyte
#> Fitting the models and predicting RAU for each analyte
#> Fitting the models and predicting RAU for each analyte

#> Fitting the models and predicting RAU for each analyte
#> Fitting the models and predicting RAU for each analyte
#> Merged output saved to: /tmp/RtmpVFWd6i/multiplate-tutorial/merged_RAU_20250414_150744.csv
#> Computing nMFI values for each analyte
#> Computing nMFI values for each analyte
#> Computing nMFI values for each analyte
#> Computing nMFI values for each analyte
#> Computing nMFI values for each analyte
#> Computing nMFI values for each analyte
#> Computing nMFI values for each analyte
#> Computing nMFI values for each analyte
#> Computing nMFI values for each analyte
#> Computing nMFI values for each analyte
#> Computing nMFI values for each analyte
#> Computing nMFI values for each analyte
#> Computing nMFI values for each analyte
#> Merged output saved to: /tmp/RtmpVFWd6i/multiplate-tutorial/merged_nMFI_20250414_150744.csv
#> Extracting the raw MFI to the output dataframe
#> Extracting the raw MFI to the output dataframe
#> Extracting the raw MFI to the output dataframe
#> Extracting the raw MFI to the output dataframe
#> Extracting the raw MFI to the output dataframe
#> Extracting the raw MFI to the output dataframe
#> Extracting the raw MFI to the output dataframe
#> Extracting the raw MFI to the output dataframe
#> Extracting the raw MFI to the output dataframe
#> Extracting the raw MFI to the output dataframe
#> Extracting the raw MFI to the output dataframe
#> Extracting the raw MFI to the output dataframe
#> Extracting the raw MFI to the output dataframe
#> Merged output saved to: /tmp/RtmpVFWd6i/multiplate-tutorial/merged_MFI_20250414_150744.csv

Remember, if you want to read all your files properly, you need to follow the naming convention of the files. The layout file should have the same name as the plate file, with the added suffix _layout. The detailed description of the convention, along with the list of all the possible arguments can be found in the documentation of the function, which can be accessed with ?process_dir command.

Now, let us investigate the merged output of the process_dir function, which contains the data for all the samples and analytes, for each data type: MFI, nMFI and RAU.

For detailed description of the normalised output for a single plate, we refer the reader to the . For the detailed description of the reports, we refer the reader to the . If you are interested in specific plots genrerated by our package, for quality control of both single and multiple plates, we refer you to the vignette .

Let us investigate the output directory and the files that were created.

list.files(output_dir)

#> [1] "merged_MFI_20250414_150744.csv"  "merged_nMFI_20250414_150744.csv"
#> [3] "merged_RAU_20250414_150744.csv"

Since, we have selected the merge_outputs parameter to be TRUE, we should see 3 files in the output directory: merged_MFI_{current_timestamp}.csv, merged_nMFI_{current_timestamp}.csv and merged_RAU_{current_timestamp}.csv.

merged_RAU_filepath <- list.files(output_dir, pattern = "RAU", full.names = TRUE) # get the exact path to the RAU file


RAU_data <- read.csv(merged_RAU_filepath)

This file is a standard CSV dataframe. Its rows correspond to the samples and the columns to the analytes. The first column is the plate name from the sample originates, the second one is the sample name. The remaining columns contain the analytes.

RAU_data[1:5, 1:5]

#>                                     plate_name sample_name Adenovirus.T3
#> 1 P1_SEROPED_PBT_62PLEX_PLATE1_03312023_RUN001 2-SERPR4187      565.6949
#> 2 P1_SEROPED_PBT_62PLEX_PLATE1_03312023_RUN001 2-SERPR4188     4989.9653
#> 3 P1_SEROPED_PBT_62PLEX_PLATE1_03312023_RUN001 2-SERPR4189     7876.6624
#> 4 P1_SEROPED_PBT_62PLEX_PLATE1_03312023_RUN001 2-SERPR4190      484.5717
#> 5 P1_SEROPED_PBT_62PLEX_PLATE1_03312023_RUN001 2-SERPR4191     1317.9263
#>   Adenovirus.T5 Bordetella.p..Toxin
#> 1      772.2534            2224.569
#> 2     5380.3923            4628.242
#> 3     3477.8758            1428.297
#> 4      567.0823            4953.693
#> 5     1005.1900            2324.620

Tymoteusz Kwieciński

2025-04-14

Introduction

Reading the data

Reading a whole directory