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
2.5
Developers: devtools / pkgload
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 workflow functions and their output
4.2
Data processing projects
4.3
Sample analyses
4.3.1
Analysis file types and formats and the
msdata
interface
4.3.2
Analysis information
4.3.3
Data conversion and pre-treatment
4.4
Example data
4.5
Features
4.5.1
Finding features
4.5.2
Feature grouping
4.5.3
Suspect screening
4.5.4
Importing feature data
4.6
Componentization
4.6.1
Features with similar chromatographic behaviour
4.6.2
Homologues series
4.6.3
Intensity and MS/MS similarity
4.7
Incorporating adduct and isotopic data
4.7.1
Selecting features with preferential adducts/isotopes
4.7.2
Setting adduct annotations for feature groups
4.7.3
Using adduct annotations in the workflow
4.8
Annotation
4.8.1
MS peak lists
4.8.2
Formulae
4.8.3
Compounds
4.8.4
Estimation of identification confidence
4.8.5
Account login for SIRIUS
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
Interactively explore and review data
5.6
Updating feature group data
5.7
Unique and overlapping features
5.8
MS similarity
5.9
Visualization
5.9.1
Features and annatation data
5.9.2
Overlapping and unique data
5.9.3
MS similarity
5.9.4
Hierarchical clustering results
5.9.5
Generating EICs in DataAnalysis
5.10
Use and modify sample analysis metadata
5.10.1
Grouping and aggregating data
5.10.2
Modifying analysis information
5.11
Reporting
5.11.1
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
(Custom) Libraries and transformations
7.1.2
TPs from feature annotation candidates
7.1.3
Processing data
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
Ion mobility spectrometry (IMS-HRMS) workflows
8.1
Introduction
8.1.1
LC-MS workflows with IMS data
8.1.2
Direct mobility assignment
8.1.3
Post mobility assignment
8.1.4
Summary
8.2
Performing IMS workflows
8.2.1
Parameter defaults
8.2.2
Raw data
8.2.3
Direct mobility assignment (DMA) workflows
8.2.4
Post mobility assignment (PMA) workflows
8.2.5
Mobility and CCS conversion
8.2.6
Suspect screening
8.2.7
Componentization
8.2.8
Annotation
8.2.9
Sets workflows
8.3
Processing data
8.3.1
Updating feature group properties
8.3.2
Inspecting and plotting data
8.3.3
Subsetting and filtering data
8.4
Example workflow
9
Advanced usage
9.1
Adducts
9.2
Default numeric limits and tolerances
9.3
Feature intensity normalization
9.3.1
Feature normalization
9.3.2
Group normalization
9.3.3
Using normalized intensities
9.3.4
Default normalization
9.3.5
IMS workflows
9.4
Feature parameter optimization
9.4.1
Parameter sets
9.4.2
Processing optmization results
9.5
Chromatographic peak qualities
9.5.1
Applying machine learning with MetaClean
9.6
Exporting and converting feature data
9.7
Algorithm consensus
9.8
Background removal in MS/MS data
9.9
MS libraries
9.10
Compound clustering
9.11
Feature regression analysis
9.12
Predicting toxicities and concentrations (MS2Tox and MS2Quant integration)
9.12.1
Predicting toxicities
9.12.2
Predicting concentrations
9.12.3
Toxicity and concentration units
9.12.4
Inspecting predicted values
9.12.5
Using predicted values to prioritize data
9.13
Fold changes
9.14
Caching
9.15
Parallelization
9.15.1
Multithreading
9.15.2
Parellization of R functions
9.15.3
Multiprocessing
9.15.4
Notes when using parallelization with futures
10
References
patRoon handbook
9
Advanced usage