Use when after LDA-based Mass2Motif discovery has generated a set of recurring fragmentation patterns (motifset_optimized.
Use when you have centroided mzML files from LC- or GC-HRMS instruments (acquired in data-dependent mode with ddMS2) and need to systematically identify chromatographic peaks,…
Use when you have a log2-transformed, standardized peak intensity matrix (rows = metabolite features, columns = samples) with compound annotations mapped to curated pathway…
Use when your TWIM-MS dataset contains ions with multiple charge states (e.g., +1, +2, +3 for the same molecular species) and you need CCS values that correctly account for the…
Use when after constructing a background set for ORA in metabolomics: you have loaded an experimental detection list and a metabolomics pathway database, applied background-set…
Use when you have PSI (percent-spliced-in) matrices for multiple samples grouped into two or more biological conditions, along with corresponding transcript expression…
Use when after generating candidate transformed structures from biotransformation rules and when you have MS/MS spectral feature data that you wish to organize into putative…
Use when you have completed pathway analysis using multiple competing methods (e.g., PALS, ORA, GSEA) on a metabolomics peak intensity dataset and need to verify that ranking…
Use when after a transformer-based de novo sequencing model (such as Casanovo) generates candidate peptide sequences from MS/MS spectra, before exporting results or using them in…
Use when when processing MS1 mass tracks from a single sample and you have already constructed per-bin mass tracks with consensus m/z and intensity vectors.
Use when you have raw mzML files and feature tables (CSV format from mzMine or XCMS) from untargeted LCMS experiments and need to distinguish true metabolite peaks from false…
Use when after completing peak picking, sample alignment, and before final MS2 spectrum extraction, when you have identified individual ion peaks across samples and need to link…
Use when implementing replacement methods ($<-, [<-, spectraData<-, mz<-, intensity<-, peaksData<-) for a writable MsBackend subclass, or when modifying peak data in an existing…
Use when apply peak-count capping when preprocessing tandem mass spectrometry (MS/MS) spectra for peptide identification or spectral library matching, particularly when working…
Use when after mass tracks have been aligned across all samples (either via pairwise alignment for ≤10 samples or nearest-neighbor clustering for larger cohorts), and you need to…
Use when after completing Part 4 (Identification of ISF Features) in the ISFrag workflow, when you have an analysis results object containing identified ISF features and need to…
Use when when you have paired MS2 spectra and BGCs with structural candidates (e.g., from MIBiG homology), and you want to rank which BGC likely produces which spectrum using a…
Use when after peak picking has generated a peaklist of experimental fragment m/z values (from Q-Exactive orbitrap, Agilent/Bruker/SCIEX Q-TOF UHPLC-HRMS/MS, or direct…
Use when processing LC-MS metabolomics studies with >10 samples where sample count and memory constraints make pairwise mass alignment infeasible.
Use when when processing raw or aggregated mass spectra datasets (from .mgf, .msp, .json, or .
Use when you have a raw GCxGC-MS chromatogram in NetCDF format (.cdf file) and need to import it into R as a 2D-TIC object for preprocessing (smoothing, baseline correction, peak…
Use when when you have quantitative lipidomics data (either from Skyline CSV export or numerical matrix format) with sample annotations and a biological grouping variable (e.
Use when when deploying containerized versions of a multi-variant application (e.g., CLI, development, Linux, and Windows flavors) and you need to verify that each built image…
Use when you have paired microbiome and metabolomic abundance tables (samples × features) with relative abundance or raw count values, and you are preparing data for downstream…
Use when after successfully reading and validating a tab-delimited metabolomics file (containing mandatory columns: aliquot, compound, area, type, injection_time, batch) using…
Use when you have an untargeted metabolomics feature table (m/z, retention time, p-value from statistical test) but lack comprehensive metabolite identifications or MS/MS…
Use when you need to create realistic, diverse chemical populations for simulating LC-MS/MS acquisition strategies in a virtual environment.
Use when you have MS/MS spectra with high chemical noise (spurious ions arising from incomplete ionization, in-source fragmentation, or instrument artifacts) and you possess…
Use when when designing or optimizing an MsBackend implementation (or similar columnar data structure) you must decide whether to pre-allocate all known columns in the backing…
Use when you have observed metabolites (from LC-MS/MS, chromatography, or spectroscopy) whose identities are unknown, and you wish to constrain the candidate pool by leveraging…
Use when before executing a bioinformatics pipeline that depends on multiple R packages with strict version constraints (e.g., DaDIA, which requires R ≥4.0, XCMS ≥3.11.4, and…
Use when when performing m/z domain calibration on FT-ICR or high-resolution MS data and the initial calibration attempt finds fewer than 5 reference m/z matches within the…
Use when when you need to enable bracket notation (e.g., handler[5]) for random access to blocks within a large compressed or remote data source, or when you want to support both…
Use when after constructing a metabCombiner object by grouping features from two metabData objects by m/z, and before proceeding to anchor selection, RT mapping, or alignment…
Use when when training a fresh CNN model from scratch on labeled LCMS data (e.g., MS1 peak classification in NeatMS), particularly when aiming for a specific performance target…
Use when you have a list of identified or suspected chemical compound names (e.g., from GC-MS Match.
Use when after constructing a MetaboSet object with LC-MS peak abundances, sample metadata (pData with QC labels), and feature metadata (fData), and after marking missing values…
Use when when comparing two implementations of the same RNA-seq mapping algorithm on identical reference indices and read sets, if per-read mapping agreement is <99.8% or the…
Use when when you have raw GC–MS data in two-dimensional m/z × retention time format (NetCDF or proprietary binary) and need to identify marker features across aroma or breath…
Use when a mass spectrometry analysis pipeline must accept data from multiple sources with different identifier schemes (GNPS Task ID, Universal Spectrum Identifiers, or…
Use when apply TIC normalization when you have raw, unprocessed mass spectrometry data (Cardinal objects or imaging matrices with 10,000+ m/z features and 1,000+ spectra) where…
Use when you have mass spectrometry run data (spectra, chromatograms, instrument metadata) that must be stored in or recovered from the mzPeak format, or when you need to validate…
Use when after importing MSI data as an msimat object and having a list of detected peak masses, but before annotating which mass differences correspond to biologically plausible…
Use when after spreadOut() has converted raw CSV peak data into a structured list, when you have one or more Compound.Name entries from GC-MS that may be ambiguous, non-canonical,…
Use when when you need to deploy a Java web application packaged in a Tomcat Docker container to a specified HTTP endpoint, and must verify that the container starts successfully,…
Use when when you have raw strain correlation scores (or similar overlap-based metrics) computed across genomic cluster family (GCF) and molecular family (MF) pairs of varying…
Use when when you need to verify that a software package (such as MassQL) passes its periodic integration test suite as indicated by CI workflow badges in the project…
Use when immediately after acquiring raw GCxGC-MS data in NetCDF format (.cdf files) and before any signal enhancement (smoothing, baseline correction) or alignment steps.
Use when you have raw LC-MS data in vendor or mzML format and need to systematically discover and extract all detectable metabolite features across the full retention time range,…
Use when you have (1) peak-picked LC-MS AIF features in a feature table with m/z and retention time, (2) corresponding xcmsSet and RAMClustR pseudo-MS/MS spectral objects from…
Use when you are building or extending a multi-module Python library for scientific computation (e.
Use when you have an observed m/z value from mass spectrometry imaging and need to assign a chemical formula with high confidence.
Use when after EIC candidate generation from LC/HRMS data (mzXML, mzML, or netCDF formats), when you need to localize discrete peaks within chromatographic profiles and assign…
Use when when you have raw methylation array data (450K or EPIC format) in .idat files or as a beta-valued matrix and need to conduct a complete analysis pipeline including data…
Use when when processing sequential spectroscopic data (1H NMR spectra) where both local chemical shift patterns and global spectral dependencies are needed for compound…
Use when you have extracted a peak list from MSI data and need to annotate matrix-related signals, but overlapping peaks or isobaric ions (ions with identical or near-identical…
Use when when you have a validated ReDU sample-information metadata file (gnps_metadata.
Use when you have raw GC-MS data with overlapped peaks in a specific retention time region and need to resolve the individual pure mass spectra of all components present in that…
Use when you have raw IM-MS data in Agilent MassHunter (.d) or UIMF format from drift tube (DT) or SLIM instruments, and you intend to perform HRdm demultiplexing or peak — from…
Use when you have a collection of preprocessed MS/MS spectra with structural annotations (InChIKey, SMILES, or InChI) and need to identify pairs of compounds that are structurally…