Getting Started

MiDAR is an R package to reproducibly manage, post-process, visualize, apply quality control, and analyze small-molecule mass spectrometry (MS) datasets, e.g. from targeted lipidomics and metabolomics experiments.

MiDAR is tailored to handle different analytical designs, data types and data processing strategies. As such, this package provides functions to import data files from from different commercial and open-source tools. Data processing functions include, internal standard and sample amount-based normalization, quantification, as well as drift and batch corrections. Quality control (QC) functions provide QC metrics and plots of raw and processed data, and QC-based feature filtering.

Datasets and processing steps are tracked, and can be saved as MiDAR S4 class object (RDS) files, Excel, PowerPoint and interactive HTML-based reports, enabling sharing and reporting of all data, metadata and applied data processing steps.

Example Workflow

Below is example of a simple data processing workflow, starting with a MassHunter CSV file, metadata in an Excel template

library(midar)
#> Loading required package: ggplot2
#> Registered S3 method overwritten by 'ggpmisc':
#>   method                  from   
#>   as.character.polynomial polynom

# Get paths of example files included with this package
masshunter_file <- system.file("extdata", "Example_MHQuant_1.csv", package = "midar", mustWork = TRUE)
metadata_file <- system.file("extdata", "Example_Metadata_1.xlsm", package = "midar", mustWork = TRUE)

# Create a MidarExperiment object (S4)
mexp <- MidarExperiment()

# Load data and metadata
mexp <- read_masshunter_csv(data = mexp, file_dir_names = masshunter_file)
#> Reading [Example_MHQuant_1.csv] ...
#> ✓ Imported 215 samples with 428 transitions. 
mexp <- read_msorganizer_xlm(data = mexp, filename = metadata_file)
#> ✓ Metadata successfully associated with 215 samples and 428 features.


# Normalize and quantitate each feature by internal standards
mexp <- normalize_by_istd(mexp)
#> ✓ 428 features normalized with 15 ISTDs. 
mexp <- quantitate_by_istd(mexp)
#> ✓ 428 features quantitated in 215 samples using 15 spiked-in ISTDs and sample amounts.
#> Concentration unit: [μmol/L].

# Get QC metrics for each feature
mexp <- calculate_qc_metrics(mexp)

# Filter features according to QC criteria
mexp <- apply_qc_filter(data = mexp,
                        CV_BQC_max = 30,
                        Intensity_BQC_min = 100,
                        SB_RATIO_min = 5,
                        R2_min = 0.8,
                        RQC_CURVE = 1)
#> ✓ QC filtering applied: 170 of 413 valid features passed QC criteria

#Plot the intensities (peak areas) of the internal standards
plot_runscatter(data = mexp,
  y_var = "Intensity",
  feature_filter = "ISTD",
  show_driftcorrection = FALSE,
  QC_TYPE_fit = "BQC",
  show_batches = TRUE,
  cap_values = TRUE, cols_page = 4, rows_page = 4,point_size = 1.5,
  outputPDF = FALSE, filename = "RunScatter_Areas_ISTD.pdf", return_plot_list = FALSE)
#> [1] "Plotting 1 pages..."
#> [1] "page 1"

Contributor Code of Conduct

Please note that the midar project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.