Use when you have a large compressed scientific data file (e.g., indexed gzip mzML) where you need to retrieve specific spectra, chapters, or records by integer ID without loading…
Use when when you have raw feature tables exported from a tandem LC-MS/MS preprocessing tool (e.g., Progenesis QI, MS-DIAL, Bruker Metaboscape) and need to combine them with…
Use when after MS2 fingerprints have been generated by counting MS2 peaks and neutral losses in each sample, and you have aligned them into a MemoMatrix (sample-by-fingerprint…
Use when when you have an existing real mzML file from a metabolomics LC-MS/MS acquisition (e.g., beer or urine samples) and need to populate a virtual mass spectrometer with the…
Use when after computing a correlation matrix (e.g., Pearson correlation across samples) on statistically significant LC-MS features, particularly when you need to inspect…
Use when when you have computed deviation and variability scores using chromVAR for two or more discrete parameter configurations (e.g., 6-mer vs 7-mer kmers, or different motif…
Use when you have preprocessed mass spectra (peak-filtered, metadata-cleaned) in supported formats (mzML, mzXML, msp, MGF, JSON) and need to compare all-pairs or many-to-many…
Use when use STOCSY when you have preprocessed 1H NMR spectral data with an unidentified peak of interest (driver signal at a specific δ ppm value) and need to determine its…
Use when after peak detection in a nontargeted LC-MS workflow when you have a feature table with detected peaks and need to filter low-quality features or understand why certain…
Use when you have mass spectrometry data loaded as a Pandas DataFrame with retention time and intensity columns, and you need to visualize the overall or mass-trace-specific…
Use when you have a feature table with intensity values across study (unknown) and blank control samples, and you need to remove features that may represent instrument artifacts,…
Use when when you have detected multiple features from non-targeted mass spectrometry and need to group them by putative compound origin.
Use when you have vendor-specific raw mass spectrometry data (ThermoFisher .raw, Agilent .
Use when after peak picking and sample alignment when you have an aligned feature table containing m/z and retention time coordinates.
Use when you have a trained regression model (e.g., a neural network or similar predictor) and a held-out test set with ground-truth continuous labels, and you need to measure…
Use when you have a published predictive model with known coefficients and feature requirements (e.g., MetaboAge from a peer-reviewed study), a target R package with an…
Use when after NMR or MS data acquisition and preprocessing (phasing, baseline correction) when you have a SummarizedExperiment object containing assay intensity matrix with QC…
Use when after abundance-correlation-based feature group refinement when you observe that larger feature groups (particularly those with 3+ features in the same m/z–retention-time…
Use when when you have downloaded a multi-file .csv library repository (e.g., LipidMatch) and need to verify that it meets minimum thresholds for species diversity (e.g., 500,000+…
Use when your research software comprises multiple independent subprojects or microservices (calculation engines, web services, data processors, websites) that must be deployed…
Use when when processing MS spectral data from multiple open mass spectra libraries (OMSLs) in mixed formats (MSP, MGF, JSON, CSV), especially when source data exhibits missing…
Use when you have a derivatizing matrix (e.g., TAHS or other publicly documented reagent) with known composition and ionization behavior that you want to use in Met-ID for…
Use when you have raw or curated mass spectrometry data (MS1, MS2, or MSMS) in mzML, mzXML, CDF, MGF, MSP formats, or from a MassBank/MetaboLights repository, and need to convert…
Use when after peak picking, sample alignment, and isotopologue/adduct grouping are complete, and you have DDA-MS2 scans associated with grouped feature ions.
Use when when compiling EI or MS/MS spectral libraries from multiple gigabyte-scale sources (e.
Use when after isotope correction when you have extracted intensity matrices from imzML or HDF5 MSI data and need to convert raw or isotope-corrected ion-image intensities into…
Use when after network partitioning, when you have identified connected subnetworks of features matched by isotope or adduct patterns and need to sanitize and categorize the…
Use when you have a feature table containing raw ion mobility arrival time measurements paired with experimentally assigned biomolecular class labels (e.g., lipid, protein,…
Use when when you have preprocessed LC-MS/MS data (MGF file with MS1 and MS2 spectra and a feature abundance table from MZmine2 or similar peak detection tool) and need to perform…
Use when you have extracted latent low-dimensional peak features from imaging mass spectrometry (IMS) data using a graph-attention autoencoder and need to identify a ranked subset…
Use when when you have a log2-normalized, zero-mean, unit-variance intensity matrix (rows=metabolites, columns=samples) and a curated metabolite set database (e.
Use when after running tardisPeaks() with screening_mode=TRUE on centroided .mzML LC-MS data, when you need to visually inspect whether the 10 target compounds (internal standards…
Use when you have a user-supplied metabolite set file (CSV or JSON) defining custom groupings of metabolites (e.
Use when you have raw LC- or GC-HRMS data from vendor instruments (ESI or APCI ionization) that needs to be converted to a vendor-neutral format for non-target screening, or you…
Use when after training or loading a NeatMS neural network model, before applying it to filter false positive MS1 peaks in a new dataset.
Use when you have computed frequent fragmentation patterns from a collection of MS/MS spectra using mineMS2, and you want to focus pattern interpretation on subsets of spectra…
Use when you have raw LC-HRMS metabolomics data in .mzML or .abf format and need to perform peak detection, feature alignment, and metabolite annotation in a reproducible,…
Use when you have raw MS imaging data in imzML (continuous or processed) or Analyze 7.5 format and need to load it into R for spectral processing, normalization, peak-picking, or…
Use when when you have processed metabolomics LC-MS/MS data organized by batch and sample type (including pooled QC replicates), and you need to quantify whether batch-to-batch…
Use when you have raw or baseline-corrected metabolite abundance measurements from mass spectrometry and need to prepare them for batch effect correction (e.g., CordBat) or…
Use when you have aligned ChIP-Seq reads (in BED or BEDPE format) and need to convert them into quantitative genome-wide signal tracks (coverage, p-value, or q-value scores) for…
Use when your input consists of multiple large MSP files (hundreds of megabytes) with associated structure folders containing hundreds of thousands of MOL or SDF files that…
Use when when you have (1) transcriptomics data and a metabolic network model with GPR rules to compute RAS scores; (2) constraint-based model predictions (RPS from optGpSampler…
Use when after XCMS feature detection and retention time correction, when you have a feature table (CSV or XCMS object) with m/z and retention time values aligned across samples.
Use when after generating probability predictions for potential modification sites (via ModiFinder.
Use when when building a comprehensive reference spectral library for metabolomics or chemical identification, you have multiple source libraries in different formats (msp, mgf,…
Use when when you have centroid mzML files from LC-MS metabolomics acquisition and need to construct sample-level mass tracks before cross-sample alignment.
Use when when designing injection plate layouts in InjectionDesign and needing to display sample positions with clear, domain-appropriate labels on the y-axis (e.g., row…
Use when you need to verify whether a GitHub Actions workflow badge (e.g., main.yml) accurately reports the CI pipeline's true pass/fail status.
Use when you are extending the MsBackend virtual class to create a new backend for storing MS spectra data and need to define the internal data structure.
Use when you have intracellular metabolomics abundance data (absolute or relative concentrations) for multiple cell lines or conditions, a constraint-based stoichiometric…
Use when you have a collection of MS/MS spectra (stored as Spectrum2 objects in an ms2Lib class) and need to identify which spectra share identical fragmentation…
Use when you have a table of detected chromatographic peaks (e.g., from CentWave peak detection in xcms) and need to isolate a single target m/z (e.g., m/z 304.1131 for a…
Use when after completing metabolite annotation of LC-MS AIF features using the annotateRC function, when you need to persist ranked candidate matches, inspect multiple candidate…
Use when working with imaging mass spectrometry (IMS) datasets where you need to (1) automatically identify marker ions without manual annotation, (2) reduce peak intensity…
Use when after calling peaks and annotating cells in an ArchR project, when you need to perform trajectory analysis using STREAM or other external tools that require a…
Use when when clustering large-scale mass spectrometry datasets (millions of MS/MS spectra in MGF format) where runtime is a bottleneck and you have access to NVIDIA GPU resources…
Use when you have NMR peak data (1H and 13C chemical shift values) and need to obtain SMART 3 classification predictions from the DeepSAT service.
Use when you have one or more small-molecule chemical structures (as SMILES, MOL, or SDF) and need to systematically explore their fate across mammalian biotransformation, human…
Use when you have constructed a NetworkX graph with LC-MS features as nodes and need to annotate each node with metadata derived from the MamsiStructSearch output (assay source,…