Introduction | Prerequisites | Connecting to Sirius | Checking Connection Status | Managing Projects | Creating/Opening a Project | Project Information | Working with Features | Logging In | Using the GUI | Shutting Down | Utility Functions | Next Steps | Session Info

Introduction | Prepping Spectra object | Open Sirius and project set up | Submit job to Sirius - For structure DB search | Retrieve Results | De novo structure description | Importing MS2-only or MSn-only data | Session information

Introduction | Preprocessing | MS1 and MS2 Extraction | Open Sirius and project set up | Data import | Searchable database | Submit job to Sirius - For structure DB search | Retrieve Results | Formula identification results: | Structure DBs search results | Compound class prediction results | Spectral library matching results | Fragmentation tree results | Submit job to Sirius - For De Novo structure annotation. | Retrieve results | Setting a Known Molecular Formula | CleanUp | Session information

Introduction | Connecting to a Project | Quick Summary with summary() | Formula Identification Summary | Structure Database Summary | De Novo Structure Summary | Spectral Library Match Summary | Detailed Results with results() | Formula Candidates | Structure Database Results | Compound Class Predictions (CANOPUS) | De Novo Structures (MSNovelist) | Spectral Library Matches | Fragmentation Trees | Filtering by Feature | Return Types | Mapping to Original IDs | Session Info

Introduction | Managing Databases | Listing Available Databases | Database Information | Creating a Custom Database | From a Compound List (TSV/CSV) | From a Spectral Library (MGF) | Comparing Results: Default vs Custom Database | Setup: Import Sample Data | Run with Default Database (BIO) | Run with Custom Database Added | Compare Results | Removing a Database | Best Practices | Clean Up | Session information

Introduction | Installation | General information on the mzTab-M format | R mzTab-M API | High-level, user faced functions | Get the Metabonaut result object | Define the data set's metadata (MTD) | Add feature abundance matrix (SMF) | Completing the metadata content | Export to an MTD+SMF mzTab-M file | Importing an MTD+SMF mzTab-M file | Validation of the mzTab-M file | Low-level functions | Formatting and exporting | Metadata | Small Molecule Feature (SMF) Table | Small Molecule (SML) Table | Small Evidence (SME) Table | General utility functions | Session information | References

Introduction | Installation | General information on the mzTab-M format | R mzTab-M API | High-level, user faced functions | Get the Metabonaut result object | Define the data set's metadata (MTD) | Add feature abundance matrix (SMF) | Completing the metadata content | Export to an MTD+SMF mzTab-M file | Importing an MTD+SMF mzTab-M file | Validation of the mzTab-M file | Low-level functions | Formatting and exporting | Metadata | Small Molecule Feature (SMF) Table | Small Molecule (SML) Table | Small Evidence (SME) Table | General utility functions | Session information | References

Create a QFeatures object | Table imports | QFeatures conversion | QFeatures object export | Session Information

Start the app | Workflow configuration | Sample/Feature Filtering | Pre-Filtering Metrics section | Filtering section | Post-filtering Metrics section | Filtering missing values by samples/features | Normalisation | Zero to NA | Log Transformation | Imputation | Aggregation | Join | Summary | Session Information

The QFeatures and scp Packages | The QFeatures Package | The scp Package | Before You Start | Installation and Launch | Installation | Application launch | Applications overview | Citation | References | Session Information

Create a QFeatures object | Table imports | QFeatures conversion | QFeatures object export | Session Information

Start the app | Workflow configuration | Sample/Feature Filtering | Pre-Filtering Metrics section | Filtering section | Post-filtering Metrics section | Filtering missing values by samples/features | Normalisation | Zero to NA | Log Transformation | Imputation | Aggregation | Join | Summary | Session Information

The QFeatures and scp Packages | The QFeatures Package | The scp Package | Before You Start | Installation and Launch | Installation | Application launch | Applications overview | Citation | References | Session Information

Introduction | Installation | Available data | TripleTOF | sciex | PXD000001 | CPTAC | Saghatelian et al. (2004) FAAH knockout LC/MS data | DIA-NN software outputs | DIA-NN single-cell proteomics reports | Proteomics contaminant databases | FTICR-MS direct injection MS data | MRM data file | CE-MS data | TMT MS3 SPS data | Adding data to MsDataHub | Session information

Introduction | Installation | Available data | TripleTOF | sciex | PXD000001 | CPTAC | Saghatelian et al. (2004) FAAH knockout LC/MS data | DIA-NN software outputs | DIA-NN single-cell proteomics reports | Proteomics contaminant databases | FTICR-MS direct injection MS data | MRM data file | CE-MS data | TMT MS3 SPS data | Adding data to MsDataHub | Session information

Introduction | Installation | Accessing data from Bruker TimsTOF files | Session information

Introduction | Installation | Importing MS Data from MetaboLights | General information for a MetaboLights data set | Session information

Introduction | Installation | Importing MS Data from MetaboLights | General information for a MetaboLights data set | Session information

Introduction | Installation | A stash for MsExperiment objects | Creating self-contained stashes | Retrieve MS experiments from MetaboLights | Session information

Introduction | Installation | A stash for MsExperiment objects | Creating self-contained stashes | Retrieve MS experiments from MetaboLights | Session information

Introduction | Installation | A stash for Spectra objects | Creating self-contained stashes | Stashes for Spectra with in-memory backends | Session information

Introduction | Installation | A stash for Spectra objects | Creating self-contained stashes | Stashes for Spectra with in-memory backends | Session information

Introduction | Installation | Importing MS Data from MassIVE | General use and information retrieval from MassIVE | Session information

Introduction | Installation | Importing MS Data from MassIVE | General use and information retrieval from MassIVE | Session information

Introduction | Installation | The Chromatograms object | Available backends | Chromatographic peaks data | Chromatograms metadata | Creating Chromatograms objects | Access data from a Chromatograms object | peaksData | chromData | Lazy Processing and Parallelization | Processing queue | Parallelization | Changing backend type | Choosing the right backend | Plotting chromatograms from a Spectra object | Understanding Factorization | Re-factorizing after metadata changes | Extracting chromatographic regions of interest | Basic extraction by retention time | Extraction with m/z filtering (ChromBackendSpectra only) | Imputing missing values in chromatograms | Available imputation methods | Extrapolation vs. Interpolation | Example: Imputing an extracted ion chromatogram (EIC) | Selecting the right imputation method | Imputation in lazy evaluation pipelines | Comparing chromatograms | Comparing chromatograms within a single object | Comparing two Chromatograms objects | Comparing groups of chromatograms | Session information

Introduction | Installation | The Chromatograms object | Available backends | Chromatographic peaks data | Chromatograms metadata | Creating Chromatograms objects | Access data from a Chromatograms object | peaksData | chromData | Lazy Processing and Parallelization | Processing queue | Parallelization | Changing backend type | Choosing the right backend | Plotting chromatograms from a Spectra object | Understanding Factorization | Re-factorizing after metadata changes | Extracting chromatographic regions of interest | Basic extraction by retention time | Extraction with m/z filtering (ChromBackendSpectra only) | Imputing missing values in chromatograms | Available imputation methods | Extrapolation vs. Interpolation | Example: Imputing an extracted ion chromatogram (EIC) | Selecting the right imputation method | Imputation in lazy evaluation pipelines | Comparing chromatograms | Comparing chromatograms within a single object | Comparing two Chromatograms objects | Comparing groups of chromatograms | Session information

Introduction | Installation | Importing MS Data from Metabolomics Workbench | General use and information retrieval from Metabolomics Workbench | Session information

Introduction | Installation | Importing MS Data from Metabolomics Workbench | General use and information retrieval from Metabolomics Workbench | Session information

Introduction | Installation | Example implementations | Suggested properties of implemented methods | PlainTextParam | AlabasterParam | Session information

Introduction | Installation | Example implementations | Suggested properties of implemented methods | PlainTextParam | AlabasterParam | Session information

Introduction | Installation | General usage | Creating Spectra objects | Accessing spectrum data | Filtering, aggregating and merging spectra data | Filter spectra data | Filter or aggregate mass peak data | Merging, aggregating and splitting | Examples and use cases for filter operations | Data manipulations | Visualizing Spectra | Aggregating spectra data | Comparing spectra | Exporting spectra | Changing backends | Backends | Handling very large data sets | Serializing (saving), moving and loading serialized Spectra objects | Session information | References

Introduction | Installation | General usage | Creating Spectra objects | Accessing spectrum data | Filtering, aggregating and merging spectra data | Filter spectra data | Filter or aggregate mass peak data | Merging, aggregating and splitting | Examples and use cases for filter operations | Data manipulations | Visualizing Spectra | Aggregating spectra data | Comparing spectra | Exporting spectra | Changing backends | Backends | Handling very large data sets | Serializing (saving), moving and loading serialized Spectra objects | Session information | References

Introduction | Calculating fragment ions | Visualising fragment ions | Customising charge states | Session information

Introduction | Installation | Code of Conduct | Contributions | Commit messages | Credit where credit is due | Coding style | Contact | Session info

Introduction | Example data | Simple imputation | Passing paramters to the imputation function | The MARGIN argument | Mixed imputation | Different margins | Passing paramters to the imputation functions | Using the whole matrix to compute imputated values | Session information | License | References

Methods | Datasets | Existing search outputs | New search outputs | Introduction | MaxQuant | Label-free | TMT | DIA-NN | plexDIA | sage | FragPipe | Session information | License

Introduction | Example data | Simple imputation | Passing paramters to the imputation function | The MARGIN argument | Mixed imputation | Different margins | Passing paramters to the imputation functions | Using the whole matrix to compute imputated values | Session information | License | References

Methods | Datasets | Existing search outputs | New search outputs | Introduction | MaxQuant | Label-free | TMT | DIA-NN | plexDIA | sage | FragPipe | Session information | License

Reading data as QFeatures | Encoding the experimental design | Filtering out contaminants and reverse hits | Removing up unneeded feature variables | Managing missing values | Counting unique features | Imputation | Data transformation | Normalisation | Feature aggregation | See also | Session information | License | References

Reading data as QFeatures | Encoding the experimental design | Filtering out contaminants and reverse hits | Removing up unneeded feature variables | Managing missing values | Counting unique features | Imputation | Data transformation | Normalisation | Feature aggregation | See also | Session information | License | References

The QFeatures class | Converting tabular data | The single-set case | The multi-set case | Including sample annotations | Additional information | Sample names | Special case: empty samples | Reducing verbose | Under the hood | License | Reference

The QFeatures class | Converting tabular data | The single-set case | The multi-set case | Including sample annotations | Additional information | Sample names | Special case: empty samples | Reducing verbose | Under the hood | License | Reference

Introduction | System requirements | Installation | Translating data structures between R and Python | Library loading and system setup | Converting MS data from R to Python | Converting MS data from Python to R | Using a dedicated MS data backend for MS data in Python | Replacing data and ensuring data consistency | Conversion of spectra variables | Combined MS data analysis | Summary | Appendix | General comments | Session information

Introduction | System requirements | Installation | Translating data structures between R and Python | Library loading and system setup | Converting MS data from R to Python | Converting MS data from Python to R | Using a dedicated MS data backend for MS data in Python | Replacing data and ensuring data consistency | Conversion of spectra variables | Combined MS data analysis | Summary | Appendix | General comments | Session information

Introduction | Calculating fragment ions | Visualising fragment ions | Session information

Installation instructions | Introduction | Handling and processing identification data | Loading PSM data | Keeping all matches | Filtering data | Remove decoy hits | Keep first rank matches | Remove shared peptides | All filters in one function | The mzR and mzID parsers | Session information

Installation instructions | Introduction | Handling and processing identification data | Loading PSM data | Keeping all matches | Filtering data | Remove decoy hits | Keep first rank matches | Remove shared peptides | All filters in one function | The mzR and mzID parsers | Session information

Introduction | Peptide-protein relation | Visualising adjacency matrices | Colouring the graph nodes | Colouring protein nodes | Colouring peptide nodes | Using quantitative data | Prioritising connected components | Session information

Introduction | Peptide-protein relation | Visualising adjacency matrices | Colouring the graph nodes | Colouring protein nodes | Colouring peptide nodes | Using quantitative data | Prioritising connected components | Session information

Introduction | Installation | The Unimod database | Different types of PTM annotations | Add modifications to a sequence | Session information

Introduction | Examples | Contributions | Session information | References

Introduction | Examples | Contributions | Session information | References

Introduction | Installation | Extracting data from Spectra objects with MassQL | MassQL definition | Type of data | Condition | Filter | Differences of the SpectraQL implementation to the MassQL definition | Examples | Filtering and subsetting | Choosing which data to return | Filtering peaks within spectra | Session information

Introduction | Installation | Extracting data from Spectra objects with MassQL | MassQL definition | Type of data | Condition | Filter | Differences of the SpectraQL implementation to the MassQL definition | Examples | Filtering and subsetting | Choosing which data to return | Filtering peaks within spectra | Session information

Introduction | Memory requirements of different data representations | Chunk-wise and parallel processing | Notes and suggestions for parallel or chunk-wise processing | Spectra functions supporting or using parallel processing | Session information

Introduction | Memory requirements of different data representations | Chunk-wise and parallel processing | Notes and suggestions for parallel or chunk-wise processing | Spectra functions supporting or using parallel processing | Session information

Introduction | Installation | Creating and using MsBackendSql SQL databases | Performance considerations | Database systems and data storage modes | Database systems | MsBackendSql database layouts/storage modes | Performance comparison with other backends | Considerations for database systems/servers | Other properties of the MsBackendSql | Summary | Session information

Introduction | Installation | Creating and using MsBackendSql SQL databases | Performance considerations | Database systems and data storage modes | Database systems | MsBackendSql database layouts/storage modes | Performance comparison with other backends | Considerations for database systems/servers | Other properties of the MsBackendSql | Summary | Session information

Introduction | Installation | General description | Example use cases | Matching of m/z values | Matching of m/z and retention time values | Matching of SummarizedExperiment or QFeatures objects | Matching of MS/MS spectra | Using alternative spectra similarity functions | Query against multiple reference databases | Finding MS2 spectra for selected m/z and retention times | Performance and parallel processing | Utility functions | Creating mixes of standard compounds | Input format | Using the function | Session information | References

Introduction | Installation | General description | Example use cases | Matching of m/z values | Matching of m/z and retention time values | Matching of SummarizedExperiment or QFeatures objects | Matching of MS/MS spectra | Using alternative spectra similarity functions | Query against multiple reference databases | Finding MS2 spectra for selected m/z and retention times | Performance and parallel processing | Utility functions | Creating mixes of standard compounds | Input format | Using the function | Session information | References

Introduction | Installation | Importing MS/MS data from MassBank files | Accessing the MassBank MySQL database | Pre-requisites | Direct access to the MassBank database | Session information

Introduction | Installation | Importing MS/MS data from MassBank files | Accessing the MassBank MySQL database | Pre-requisites | Direct access to the MassBank database | Session information

Introduction | Creating CompDb databases | CompDb from HMDB data | CompDb from custom data | CompDb from MoNA data | CompDb by sequentially filling with data | Extending CompDb databases | Session information

Introduction | Creating CompDb databases | CompDb from HMDB data | CompDb from custom data | CompDb from MoNA data | CompDb by sequentially filling with data | Extending CompDb databases | Session information

Introduction | What is a MsBackend? | Conventions and definitions | Notes on parallel processing | API | Required methods | spectraData() | spectraVariables() | backendInitialize() | peaksData() | extractByIndex() and [ | backendMerge() | intensity() | mz() | spectraNames() | Data replacement methods | $<- | spectraData<- | intensity<- | mz<- | peaksData<- | selectSpectraVariables() | dataStorage<- | spectraNames<- | Optional methods | $ | acquisitionNum() | backendBpparam() | backendParallelFactor() | backendRequiredSpectraVariables() | centroided() | centroided<- | collisionEnergy() | collisionEnergy<- | dataOrigin() | dataOrigin<- | dataStorage() | dropNaSpectraVariables() | isEmpty() | isolationWindowLowerMz() | isolationWindowLowerMz<- | isolationWindowTargetMz() | isolationWindowTargetMz<- | isolationWindowUpperMz() | isolationWindowUpperMz<- | isReadOnly() | length() | lengths() | msLevel() | msLevel<- | peaksVariables() | polarity() | polarity<- | precScanNum() | precursorCharge() | precursorIntensity() | precursorMz() | precursorMz<- | ionCount() | isCentroided() | longForm() | reset() | export() | rtime() | rtime<- | scanIndex() | smoothed() | smoothed<- | split() | supportsSetBackend() | tic() | uniqueMsLevels() | filterDataOrigin() | filterDataStorage() | filterEmptySpectra() | filterIsolationWindow() | filterMsLevel() | filterPolarity() | filterPrecursorMzRange() | filterPrecursorMzValues() | filterPrecursorCharge() | filterPrecursorScan() | filterRt() | Implementation notes | Testing the validity of the backend | Session information | References

Introduction | What is a MsBackend? | Conventions and definitions | Notes on parallel processing | API | Required methods | spectraData() | spectraVariables() | backendInitialize() | peaksData() | extractByIndex() and [ | backendMerge() | intensity() | mz() | spectraNames() | Data replacement methods | $<- | spectraData<- | intensity<- | mz<- | peaksData<- | selectSpectraVariables() | dataStorage<- | spectraNames<- | Optional methods | $ | acquisitionNum() | backendBpparam() | backendParallelFactor() | backendRequiredSpectraVariables() | centroided() | centroided<- | collisionEnergy() | collisionEnergy<- | dataOrigin() | dataOrigin<- | dataStorage() | dropNaSpectraVariables() | isEmpty() | isolationWindowLowerMz() | isolationWindowLowerMz<- | isolationWindowTargetMz() | isolationWindowTargetMz<- | isolationWindowUpperMz() | isolationWindowUpperMz<- | isReadOnly() | length() | lengths() | msLevel() | msLevel<- | peaksVariables() | polarity() | polarity<- | precScanNum() | precursorCharge() | precursorIntensity() | precursorMz() | precursorMz<- | ionCount() | isCentroided() | longForm() | reset() | export() | rtime() | rtime<- | scanIndex() | smoothed() | smoothed<- | split() | supportsSetBackend() | tic() | uniqueMsLevels() | filterDataOrigin() | filterDataStorage() | filterEmptySpectra() | filterIsolationWindow() | filterMsLevel() | filterPolarity() | filterPrecursorMzRange() | filterPrecursorMzValues() | filterPrecursorCharge() | filterPrecursorScan() | filterRt() | Implementation notes | Testing the validity of the backend | Session information | References

Introduction | Installation | Configure reticulate with host system Python environment (optional) | SpectriPy pre-requisites and installation instructions | Installing Bioconductor | Installing SpectriPy | Check installation completed | Appendix | Installation instructions for first-time R users | Installing R and RStudio | How to Download and Install R | Windows | Mac | Linux | Using R | RStudio | Opening R | Startup and Python configuration | Fixing package installation or loading problems | Session information

Introduction | Installation | Configure reticulate with host system Python environment (optional) | SpectriPy pre-requisites and installation instructions | Installing Bioconductor | Installing SpectriPy | Check installation completed | Appendix | Installation instructions for first-time R users | Installing R and RStudio | How to Download and Install R | Windows | Mac | Linux | Using R | RStudio | Opening R | Startup and Python configuration | Fixing package installation or loading problems | Session information

Introduction | What is a ChromBackend? | Conventions and definitions | Notes on parallel and chunk-wise processing | API | Required methods | dataStorage() | length() | backendInitialize() | chromVariables() | chromData() | peaksVariables() | peaksData() | [ | $ | backendMerge() | Data replacement methods | chromData<- | $<- | peaksData<- | Methods with available default implementations | backendParallelFactor() | chromIndex() | collisionEnergy() | dataOrigin(), dataOrigin<- | intensity(), intensity<- | isEmpty() | isReadOnly() | lengths() | msLevel(), msLevel<- | mz(), mz<- | mzMax(), mzMax<- | mzMin(), mzMin<-` | precursorMz(), precursorMz<- | precursorMzMax(), precursorMzMax<- | precursorMzMin(), precursorMzMin<- | productMz(), productMz<- | productMzMax(), productMzMax<- | productMzMin(), productMzMin<- | rtime(), rtime<- | split() | Session information

Introduction | What is a ChromBackend? | Conventions and definitions | Notes on parallel and chunk-wise processing | API | Required methods | dataStorage() | length() | backendInitialize() | chromVariables() | chromData() | peaksVariables() | peaksData() | [ | $ | backendMerge() | Data replacement methods | chromData<- | $<- | peaksData<- | Methods with available default implementations | backendParallelFactor() | chromIndex() | collisionEnergy() | dataOrigin(), dataOrigin<- | intensity(), intensity<- | isEmpty() | isReadOnly() | lengths() | msLevel(), msLevel<- | mz(), mz<- | mzMax(), mzMax<- | mzMin(), mzMin<-` | precursorMz(), precursorMz<- | precursorMzMax(), precursorMzMax<- | precursorMzMin(), precursorMzMin<- | productMz(), productMz<- | productMzMax(), productMzMax<- | productMzMin(), productMzMin<- | rtime(), rtime<- | split() | Session information

Preparing the data | Exploring the QFeatures hierarchy | Basic data exploration | Using ggplot2 | Advanced data exploration | Interactive data exploration | License | References

Introduction | Creating QFeatures object | Manipulating feature metadata | Subsetting | Filtering | Session information | License | References

Preparing the data | Exploring the QFeatures hierarchy | Basic data exploration | Using ggplot2 | Advanced data exploration | Interactive data exploration | License | References

Introduction | Creating QFeatures object | Manipulating feature metadata | Subsetting | Filtering | Session information | License | References

Introduction | Installation | Importing MS/MS data from MGF files | Annotated MGF files | Parallel processing | Session information

Introduction | Installation | Importing MS/MS data from MGF files | Annotated MGF files | Parallel processing | Session information

Introduction | Installation | General usage | Querying compound annotations | Additional functionality for CompDb databases | Accessing and using MS/MS data | Ion databases | Session information | References

Introduction | Installation | General usage | Querying compound annotations | Additional functionality for CompDb databases | Accessing and using MS/MS data | Ion databases | Session information | References

Introduction | Installation | Examples | Conversion between ion m/z and compound masses | Working with chemical formulas | Kendrick mass defect calculation | Retention time indexing | Linear model-based adjustment of LC-MS feature abundances | Basic quality assessment and pre-filtering of metabolomics data | Contributions | Session information | References

Introduction | Installation | Examples | Conversion between ion m/z and compound masses | Working with chemical formulas | Kendrick mass defect calculation | Retention time indexing | Linear model-based adjustment of LC-MS feature abundances | Basic quality assessment and pre-filtering of metabolomics data | Contributions | Session information | References

Introduction | Installation | MSP file format | Importing MS/MS data from MSP files | Session information | References

Introduction | Installation | MSP file format | Importing MS/MS data from MSP files | Session information | References

Introduction | Installation | Mass Spectrometry Feature Grouping | Grouping of features by similar retention time | Grouping of features by abundance correlation across samples | Performing feature grouping on a subset of features | Session information

Introduction | Installation | Mass Spectrometry Feature Grouping | Grouping of features by similar retention time | Grouping of features by abundance correlation across samples | Performing feature grouping on a subset of features | Session information