MiDAR

Caution

midar has moved to MRMhub, where it is now named {mrmhub} and is actively maintained and extended.

Please only use {mrmhub}. Thank you! 👏

MiDAR is an R package designed for the reproducible post-processing, quality control, and reporting of quantitative small-molecule mass spectrometry (MS) data. Its modular functionalities and defined data structure, support diverse analytical designs, data formats, and processing tasks, including metabolomics and lipidomics. MiDAR is intended for both analytical and bioinformatics scientists and to facilitate collaboration between them. It enables the creation of customizable, supervisable, and documented data processing workflows through intuitive high-level R functions and data objects.

MiDAR's core tools, accessible also to those with limited R experience, allow analysts to annotate, inspect, and process data. This includes importing data and metadata from various file formats, managing and organizing data with integrity checks, performing processing tasks such as quantification, drift/batch correction, and applying QC-based feature filtering. Users can assess data quality using QC metrics and diagnostic plots, and share both raw and processed data along with the entire processing workflow for further analyses and documentation.

MiDAR also serves as a validated software framework for building robust and scalable data processing pipelines.

Example Workflow

# Path of example files included with this package
dir_path <- system.file("extdata",  package = "midar")

# Create a MidarExperiment object
mexp <- MidarExperiment()

# Load data and metadata
mexp <- import_data_mrmkit(mexp,
                           path = file.path(dir_path, "MRMkit_demo.tsv"),
                           import_metadata = TRUE)

mexp <- import_metadata_analyses(mexp, 
                                 path = file.path(dir_path, "MRMkit_AnalysesAnnot.csv"), 
                                 ignore_warnings = T)
mexp <- import_metadata_istds(mexp, 
                              path = file.path(dir_path, "MRMkit_ISTDconc.csv"))

# Normalize and quantitate features by internal standards
mexp <- normalize_by_istd(mexp)
mexp <- quantify_by_istd(mexp)

# Drift and batch-effect correction
mexp <- correct_drift_cubicspline(mexp, variable = "conc", ref_qc_types = "BQC")
mexp <- correct_batch_centering(mexp, variable = "conc", ref_qc_types = "BQC")

# Plot analysis time-trends of final concentrations of each feature 
plot_runscatter(mexp,
                variable = "conc",
                qc_types = c("BQC", "TQC", "SPL", "PBLK", "SBLK"),
                cap_outliers = TRUE,
                output_pdf = FALSE,
                path = "./output/runscatter_istd.pdf")

# Set features QC-filter criteria   
 mexp <- filter_features_qc(mexp,
                            max.cv.conc.bqc = 25,
                            min.signalblank.median.spl.pblk = 3,
                            include_qualifier = FALSE,
                            include_istd = FALSE)
 
# Save concentration data
 save_dataset_csv( mexp, 
                   path = "mydata.csv", 
                   variable = "conc", 
                   filter_data = TRUE)