1
Introduction
2
Installation
2.1
patRoon Bundle
2.1.1
Updating the bundle
2.1.2
Details
2.2
Docker image
2.3
Regular R installation
2.3.1
Automatic installation
2.3.2
Manual installation
2.3.3
Verifying the installation
2.4
Managing legacy installations
3
Workflow concepts
4
Generating workflow data
4.1
Introduction
4.1.1
Workflow functions
4.1.2
Workflow output
4.1.3
Overview of all functions and their output
4.2
Preparations
4.2.1
Data pre-treatment
4.2.2
Analysis information
4.2.3
Automatic project generation with newProject()
4.3
Features
4.3.1
Finding and grouping features
4.3.2
Suspect screening
4.4
Componentization
4.4.1
Features with similar chromatographic behaviour
4.4.2
Homologues series
4.4.3
Intensity and MS/MS similarity
4.5
Incorporating adduct and isotopic data
4.5.1
Selecting features with preferential adducts/isotopes
4.5.2
Setting adduct annotations for feature groups
4.5.3
Using adduct annotations in the workflow
4.6
Annotation
4.6.1
MS peak lists
4.6.2
Formulae
4.6.3
Compounds
4.6.4
Suspect annotation
5
Processing workflow data
5.1
Inspecting results
5.2
Filtering
5.2.1
Features
5.2.2
Suspect screening
5.2.3
Annotation
5.2.4
Components
5.2.5
Negation
5.3
Subsetting
5.3.1
Prioritization workflow
5.4
Deleting data
5.5
Unique and overlapping features
5.6
MS similarity
5.7
Visualization
5.7.1
Features and annatation data
5.7.2
Overlapping and unique data
5.7.3
MS similarity
5.7.4
Hierarchical clustering results
5.7.5
Generating EICs in DataAnalysis
5.8
Interactively explore and review data
5.9
Reporting
5.9.1
Legacy interface
6
Sets workflows
6.1
Initiating a sets workflow
6.2
Generating sets workflow data
6.2.1
Componentization
6.2.2
Formula and compound annotation
6.3
Selecting adducts to improve grouping
6.4
Processing data
6.5
Advanced
6.5.1
Initiating a sets workflow from feature groups
6.5.2
Inspecting and converting set objects
7
Transformation product screening
7.1
Obtaining transformation product data
7.1.1
Parent input
7.1.2
Processing data
7.1.3
(Custom) Libraries and transformations
7.2
Linking parent and transformation product features
7.2.1
Componentization
7.2.2
Processing data
7.2.3
Omitting transformation product input
7.2.4
Reporting TP components
7.3
Example workflows
7.3.1
Screen predicted TPs for targets
7.3.2
Screening TPs from a library for suspects
7.3.3
Non-target screening of predicted TPs
7.3.4
Non-target screening of TPs by annotation similarities
8
Advanced usage
8.1
Adducts
8.2
Feature intensity normalization
8.2.1
Feature normalization
8.2.2
Group normalization
8.2.3
Using normalized intensities
8.2.4
Default normalization
8.3
Feature parameter optimization
8.3.1
Parameter sets
8.3.2
Processing optmization results
8.4
Chromatographic peak qualities
8.4.1
Applying machine learning with MetaClean
8.5
Exporting and converting feature data
8.6
Algorithm consensus
8.7
MS libraries
8.8
Compound clustering
8.9
Feature regression analysis
8.10
Predicting toxicities and concentrations (MS2Tox and MS2Quant integration)
8.10.1
Predicting toxicities
8.10.2
Predicting concentrations
8.10.3
Toxicity and concentration units
8.10.4
Inspecting predicted values
8.10.5
Using predicted values to prioritize data
8.11
Fold changes
8.12
Caching
8.13
Parallelization
8.13.1
Parellization of R functions
8.13.2
Multiprocessing
8.13.3
Notes when using parallelization with futures
9
References
patRoon handbook
8
Advanced usage