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

`$`(<MidarExperiment>): Access Slots of a MidarExperiment Object via $ Syntax

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

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

get_analysis_count(): Get the number of analyses in the dataset

get_analyis_start(): Get the start time of the analysis sequence

get_analyis_end(): Get the end time of the analysis sequence

get_analysis_breaks(): Get the number of analysis breaks in the analysis

get_analysis_duration(): Get the total duration of the analysis

get_runtime_median(): Get the median run time

get_feature_count(): Get the number of features in the dataset

get_featurelist(): Get feature IDs

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(): Import MRMkit peak integration results

import_data_masshunter(): Import Agilent MassHunter Quantitative Analysis CSV files

import_data_csv(): Import Analysis Results from Plain CSV Files

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_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

import_metadata_msorganiser(): Import Metadata from a MIDAR Metadata Organizer file

import_metadata_from_data(): Retrieve Metadata from Imported Analysis Data

save_metadata_templates(): Saves a Excel (xlsx) file with metadata templates

save_metadata_msorganiser_template(): Saves a MiDAR Metadata Organizer template

add_metadata(): Add metadata an MidarExperiment object

assert_metadata(): Add metadata an MidarExperiment object

Isotope correction

Functions to perform type II isotopic correction

correct_interferences(): Apply interference correction

correct_interference_manual(): Manual isotopic interference correction

External Calibration

Function to plot and analyze external calibration curves

quantify_by_calibration(): Calculate concentrations based on external calibration

plot_calibrationcurves(): Plot Calibration Curves

calc_calibration_results(): Calculate external calibration curve results

get_calibration_metrics(): Get Calibration Metrics

get_qc_bias_variability(): Retrieve 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 Using Internal Standards

quantify_by_calibration(): Calculate concentrations based on external calibration

Drift/Batch Correction

Function for drift and batch correction correction

correct_drift_gaussiankernel(): Drift Correction by Gaussian Kernel Smoothing

correct_drift_cubicspline(): Drift Correction by Cubic Spline Smoothing

correct_drift_loess(): Drift Correction by LOESS Smoothing

correct_drift_gam(): Drift Correction by Generalized Additive Model (GAM) Smoothing

correct_batch_centering(): Batch Centering Correction

Quality Control and Filtering

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

calc_qc_metrics(): Calculate Quality Control (QC) Metrics for Features

filter_features_qc(): Feature Filtering Based on QC Criteria

detect_outlier(): Get list of analyses classified as technical outliers

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(): RunSequence Plot

plot_runscatter(): RunScatter Plot

plot_rla_boxplot(): Relative Log Abundance (RLA) Plot

plot_pca(): PCA Plot for Quality Control

plot_normalization_qc(): Compare %CV values before and after normalization

plot_qcmetrics_comparison(): Comparison of two feature QC metrics variables

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(): Write Data Processing Report (EXCEL)

save_dataset_csv(): Export Data to CSV file

save_feature_qc_metrics(): Save Feature QC Metrics to CSV

Lipidomics

Functions specific to lipidomics data processing and analysis.

get_lipid_class_names(): Get lipid class, species and transition names

Datasets

Example datasets for testing and demonstration.

lipidomics_dataset: Plasma Lipidomics Dataset with Metadata

quant_lcms_dataset: LC-MS Dataset with External Calibration Curve and Metadata

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(): Percent coefficient of variation (%CV)

cv_log(): Percent coefficient of variation (%CV) based on log-transformation

calc_average_molweight(): Calculate Average Molecular Weight from Chemical Formulas

save_dataset_csv(): Export Data to CSV file

fun_correct_drift(): Drift Correction by Custom Function

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

fun_loess(): Loess smoothing helper function

fun_cspline(): Cubic spline smoothing helper function

fun_gam_smooth(): Generalized Additive Model (GAM) smoothing helper function

get_mad_tails(): Get MAD-based tails

order_chained_columns_tbl(): Reorder Data Frame based on a chain of linked values in two columns.