Our package’s main purpose is to read, perform quality control, and normalise raw MBA data. Unfortunately, different devices and labs have different data formats. We gathered a few datasets on which our package could be tested. This document describes the datasets and their sources.
The majority of our datasets, available for the public, are stored in
the extdata folder of the package. The remaining private
and the more significant number of publicly available datasets are
stored in the OneDrive folder, which is accessible to the
package developers.
The simple way of accessing the files is to download them from our GitHub repository.
Another way is to source the files using the system.file
function. The function returns the path to the file, which can be used
to read the data. The function has the following syntax:
dataset_name <- "CovidOISExPONTENT.csv"
dataset_filepath <- system.file("extdata", dataset_name, package = "PvSTATEM", mustWork = TRUE)The variable dataset_filepath now contains the path to
the specified dataset on your computer. Since we know the filepath to
the desired dataset, we can execute the read_data function
to read the data. The function has the following syntax:
#> Reading Luminex data from: /tmp/RtmpNm98u4/Rinst10f447d7b334/PvSTATEM/extdata/CovidOISExPONTENT.csv
#> using format xPONENT
#> ([31mWARNING[39m)
#> Layout file not provided. Setting `use_layout_sample_names`,
#> `use_layout_types` and `use_layout_dilutions` to FALSE.
#> ([31mWARNING[39m)
#> All dilutions in the plate are set to NA. Please check the dilutions in the layout file or sample names.[32m
#> New plate object has been created with name: CovidOISExPONTENT!
#> [39m#> Plate with 96 samples and 30 analytesOur datasets are divided into three main categories:
To perform simple unit tests and validate the most basic reading
functionalities of the package, we created a few artificial datasets.
The datasets are stored in the package’s extdata folder.
The datasets are:
random.csv - a simple dataset with random values used
to test the basic functionalities of the packagerandom2.csv - another simple dataset with random values
used to test the basic functionalities of the package. This file has a
corresponding, artificial layout - random_layout.csvrandom_broken_colB.csv - this dataset has a broken
column, which should be detected by the package and reported as a
warningThe datasets from this category are the most important for package development since the package’s primary purpose is to simplify the preprocessing of the data in the scope of the PvSTATEM project.
Most of them are stored in the package’s OneDrive
folder. The datasets available in the extdata folder are
two files coming from the Covid oise examination:
CovidOISExPONTENT.csv, which is a
IG4DC2~1.csv plate from examination
IgG_CovidOise4_30plex. It contains the corresponding layout
file CovidOISExPONTENT_layout.xlsxCovidOISExPONTENT_CO.csv, which is a
IGG_CO~1.csv plate from examination
IgG_CovidOise2_30plex and corresponding layout fileMost of the examples and vignettes in the package are based on these datasets.
We gathered a few datasets from the public domain to check the
package functionalities of the data from different sources. The datasets
are also stored in the package OneDrive folder and in the
subfolder external of the extdata directory.
The datasets are:
Chul_IgG3_1.csv - GitHub repo RTSS_Kisumu_Schisto source
Chul_TotalIgG_2.csv - GitHub repo
RTSS_Kisumu_Schisto source
pone.0187901.s001.csv - data shipped with drLumi
package source
New_Batch_6_20160309_174224.csv - dataset included
in the paper A single-nucleotide-polymorphism-based genotyping assay
for simultaneous detection of different carbendazim-resistant genotypes
in the Fusarium graminearum species complex, H. Zhang et.
al.
New_Batch_14_20140513_082522.csv - dataset included
in the paper A single-nucleotide-polymorphism-based genotyping assay
for simultaneous detection of different carbendazim-resistant genotypes
in the Fusarium graminearum species complex, H. Zhang et.
al.