Introduction
Our package primarily aims to read, perform quality control, and
normalise raw MBA data. The entire package is made to be as
user-friendly as possible, so in most of your code, you will read the
data from xPONENT or INTELLIFLEX file using the
read_luminex_data function and interacting with the created
Plate object.
Under the hood, the read_luminex_data function uses a
specific function to read data from a given format and later
standardises this output to finally create a Plate
object.
This article will go deeper into the details of our data parsers, illustrate how the reading system works and show you how to use them even outside the PvSTATEM package.
Basic data loading
The simplest way of loading a file is to use the
read_luminex_data function with default values.
library(PvSTATEM)
plate_filepath <- system.file("extdata", "CovidOISExPONTENT.csv", package = "PvSTATEM", mustWork = TRUE)
layout_filepath <- system.file("extdata", "CovidOISExPONTENT_layout.xlsx", package = "PvSTATEM", mustWork = TRUE)
plate <- read_luminex_data(plate_filepath, layout_filepath)#> Reading Luminex data from: /home/runner/work/_temp/Library/PvSTATEM/extdata/CovidOISExPONTENT.csv
#> using format xPONENT
#>
#> New plate object has been created with name: CovidOISExPONTENT!
#>
summary(plate)#> Summary of the plate with name 'CovidOISExPONTENT':
#> Plate examination date: 2022-05-11 16:45:00
#> Total number of samples: 96
#> Number of blank samples: 1
#> Number of standard curve samples: 11
#> Number of positive control samples: 0
#> Number of negative control samples: 0
#> Number of test samples: 84
#> Number of analytes: 30
# display a sample of the dataframe
data.frame(plate)[c(1, 2), c(1, 2)]#> Spike_6P ME
#> 1 24 33
#> 2 14256 1263The function has many parameters that can be used to customise the reading process.
For example, we can change the data type we want to find in the file. By default, the datatype we are looking for is the Median MFI value. This default value can be changed, e.g. for the Mean value, as illustrated below. In this way, we provide more flexibility to the user.
plate <- read_luminex_data(plate_filepath, layout_filepath, default_data_type = "Mean")#> Reading Luminex data from: /home/runner/work/_temp/Library/PvSTATEM/extdata/CovidOISExPONTENT.csv
#> using format xPONENT
#>
#> New plate object has been created with name: CovidOISExPONTENT!
#>
summary(plate)#> Summary of the plate with name 'CovidOISExPONTENT':
#> Plate examination date: 2022-05-11 16:45:00
#> Total number of samples: 96
#> Number of blank samples: 1
#> Number of standard curve samples: 11
#> Number of positive control samples: 0
#> Number of negative control samples: 0
#> Number of test samples: 84
#> Number of analytes: 30
# display a sample of the dataframe
data.frame(plate)[c(1, 2), c(1, 2)]#> Spike_6P ME
#> 1 25.13 32.29
#> 2 13681.42 1352.75For the complete list of parameters and their description, please
refer to the read_luminex_data documentation.
PvSTATEM as an MBA data reader
The read_luminex_data function enforces additional
constraints on the raw MBA data, such as the sample names following a
specific pattern to be correctly classified. If your data does not
follow our standards but you still want to use our parsers, you can
directly use the format-specific functions here: -
read_xponent_format -
read_intelliflex_format
For example, let us read the xPONENT file above using the
read_xponent_format.
output <- read_xponent_format(plate_filepath)
typeof(output)#> [1] "list"
names(output)#> [1] "ProgramMetadata" "Header" "Samples" "Min Events"
#> [5] "Per Bead" "Results" "CRC32"
output[["ProgramMetadata"]]#> $Program
#> [1] "xPONENT"
#>
#> $Build
#> [1] "4.2.1705.0"
#>
#> $Date
#> [1] "05/11/2022"
#>
#> $Time
#> [1] "4:45 PM"
#>
#> $SN
#> [1] "MAGPX16145704"
#>
#> $Batch
#> [1] "IgG_CovidOiseS4_30plex_plate5_20220511"
names(output[["Results"]])#> [1] "Median" "Net MFI"
#> [3] "Count" "Avg Net MFI"
#> [5] "Mean" "%CV"
#> [7] "Peak" "Std Dev"
#> [9] "Trimmed Count" "Trimmed Mean"
#> [11] "Trimmed % CV of Microspheres" "Trimmed Peak"
#> [13] "Trimmed Standard Deviation" "Units"
#> [15] "Per Bead Count" "Acquisition Time"
#> [17] "Dilution Factor" "Analysis Types"
#> [19] "Audit Logs" "Warnings/Errors"#> Location Sample Spike_6P
#> 1 1(1,A1) B 24
#> 2 2(1,A2) S 14256We can see now that the output of that function is a nested list containing the information parsed from the file. As the structure of the output may be different across the formats, this is not the recommended way to read the data, but the package is open enough to allow you to do so.