Package index

MidarExperiment

Functions to create, access and query MidarExperiment objects, which are the central data object in the MiDAR workflow.

MidarExperiment(): Constructor for the MidarExperiment object.

MidarExperiment-class: S4 Class Representing the MIDAR Dataset

analysis_type(<MidarExperiment>): Get analysis_type

`analysis_type<-`(<MidarExperiment>): Set analysis_type

`analysis_type<-`(): Get analysis type

metadata_responsecurves(<MidarExperiment>): Get response curve metadata

`metadata_responsecurves<-`(<MidarExperiment>): Set response curve metadata

`metadata_responsecurves<-`(): metadata_responsecurves method

analysis_type(): Set analysis type

`$`(<MidarExperiment>): Getter for specific slots of an MidarExperiments object

set_analysis_order(): Set Analysis Order

get_batch_boundaries(): Get the start and end analysis numbers of specified batches

exclude_analyses(): Exclude Analyses from the Dataset

exclude_features(): Exclude features from the dataset

combine_experiments(): Combines a list of MidarExperiments into one

get_analyticaldata(): Get the annotated or the originally imported analytical data

set_intensity_var(): Set default variable to be used as feature raw signal value

Analysis data import

Functions to import analytical data from different sources into MidarExperiment objects. Additionally, the file parser function used internally by these import functions are available for direct use, i.e. to import different analytical data into data frames.

import_data_mrmkit(): Imports MRMkit peak integration results

import_data_masshunter(): Imports Agilent MassHunter Quantitative Analysis CSV files

import_data_csv(): Import plain analysis results

parse_mrmkit_result(): Parses MRMkit peak integration results into a tibble

parse_masshunter_csv(): Reads and parses one Agilent MassHunter Quant CSV result file

parse_plain_csv(): Reads a long CSV file with Feature Intensities

Metadata import

Functions to import metadata describing the analyses (samples), features (analytes), internal standards and other relevant information from the MiDAR Excel template or CSV files.

import_metadata_midarxlm(): Import Metadata from MIDAR Template

import_metadata_from_data(): Retrieve Metadata from Imported Analysis Data

import_metadata_analyses(): Import analysis metadata

import_metadata_features(): Import feature metadata

import_metadata_istds(): Import Internal Standards (ISTD) metadata

import_metadata_responsecurves(): Import response curves metadata

import_metadata_qcconcentrations(): Import calibration curves metadata

metadata_responsecurves(): metadata_responsecurves method

assert_metadata(): Add metadata an MidarExperiment object

add_metadata(): Add metadata an MidarExperiment object

External Calibration

Function to plot and analyze external calibration curves

plot_calibrationcurves(): Plots calibration curves for each measured feature.

calc_calibration_results(): Calculate external calibration curve results

get_calibration_results(): Get a calibration regression results

Normalization and Quantification

Functions to normalization by internal standards and sample amounts, to calculate analyte concentrations based on internal standards amounts or external calibration curves.

normalize_by_istd(): Normalize Feature Intensities Using Internal Standards

quantify_by_istd(): Calculate Analyte Concentrations Based on Internal Standards

quantify_by_calibration(): Calculate concentrations based on external calibration

Data Correction and Adjustments

Function for drift and batch correction correction

correct_drift_loess(): Drift Correction by LOESS Smoothing

correct_drift_gaussiankernel(): Drift Correction by Gaussian Kernel Smoothing

corr_drift_fun(): Drift Correction by Custom Function

fun_gauss.kernel.smooth(): Gaussian Kernel smoothing helper function

fun_loess(): Loess smoothing helper function

correct_batch_centering(): Batch Centering Correction

Isotope correction

Functions to perform type II isotopic correction

correct_interferences(): Substract interferences contributed by another feature

Quality Control and Filtering

Functions to calculate feature QC metrics and apply QC filtering, and vizualize the filtering results.

qc_calc_metrics(): Calculate feature quality control (QC) metrics

filter_features_qc(): Feature Filtering Based on Quality Control Criteria

plot_qc_summary_byclass(): Plot QC Filtering Summary by Feature Class

plot_qc_summary_overall(): Plot Overall QC Filtering Summary

Quality Control Plots

Functions to plots diverse QC visualizatios.

plot_runsequence(): Run Sequence Plot to Visualize Analysis Design and Timelines

plot_runscatter(): Create a Run Scatter Plot to Visualize Analyte Distributions

plot_rla_boxplot(): Relative Log Abundance (RLA) Plot

plot_pca(): PCA Plot for Quality Control

plot_cv_normalization(): Comparison of CV values before and after normalization

plot_x_vs_y(): Contrast two variables from QC metrics table for all features per feature class

Response Curves

Functions to calculate and visualize response curves

plot_responsecurves(): Plot Response Curves

get_response_curve_stats(): Linear regression statistics of response curves

Functions to export processed and raw datasets and the processing steps in different formats.

save_report_xlsx(): Writes all a data processing report to an EXCEL file

save_dataset_csv(): Export any parameter to a wide-format table

report_write_qc_metrics(): Save the QC table to a CSV file

Lipidomics

Functions specific to lipidomics data processing and analysis.

get_lipid_class_names(): Retrieve lipid name, lipid class and transition from feature names

Datasets

Example datasets for testing and demonstration.

lipidomics_dataset: Plasma Lipidomics Peak Areas Dataset

data_load_example(): Load an example MidarExperiment dataset

Helper functions

A collection of functions that may be useful in the context of mass spectrometry is also available.

cv_log(): Coefficient of variation (CV) using log-transformed data

save_dataset_csv(): Export any parameter to a wide-format table