Use when you have extracted tabular data into an intermediate JSON form and need to restructure records by mapping input fields to output dictionary keys, collating multiple…
Use when you have a feature table from LC- or GC-HRMS data (either detected via pyOpenMS or imported as a custom feature list) containing m/z, retention time, and intensity…
Use when after implementing or modifying the scoring module that computes average InChIKey scores and neighbourhood scores for candidate matches, or when integrating new scoring…
Use when you have defined one or more proton NMR spectral regions-of-interest (ROIs) with lower and upper chemical-shift bounds (in ppm) from an experimental NMR spectrum of a…
Use when after executing a reproducible simulation pipeline (particularly for Over-representation Analysis in metabolomics), compare the newly generated outputs against reference…
Use when after structural cluster assignment and correlation clustering are complete, and you need to represent the full set of structural relationships (isotopologues, adducts,…
Use when you have raw mass spectrometry data in mzML or Bruker .d format and need to ingest it into a tabular format (pandas DataFrame) for visualization, statistical analysis, or…
Use when you have a Thermo Fisher Orbitrap .raw file and need to programmatically
Use when when generating synthetic LC/GC-MS .mzML files from MoNA or HMDB spectral records where you need to compute absolute ground-truth maximum intensity (sim_ins) for each…
Use when you have raw mass spectra or processed feature matrices from liquid chromatography–mass spectrometry (LC-MS) or direct infusion MS that must be ingested by a deep…
Use when you have a feature table from nontargeted LC-MS peak detection (containing m/z, retention time, and intensity values) and need to disambiguate whether detected features…
Use when you have extracted a chemical mixture from a real mzML acquisition (e.g., Beer1pos), simulated the same chemicals through ViMMS using a chosen controller (e.
Use when you have peak-abundance data (after molecular formula assignment, peak filtering by m/z, isotope, ppm error, and sample presence thresholds) and you need to quantify and…
Use when you have detected features in LC- or GC-HRMS data (via pyOpenMS or custom feature tables) and need to systematically rank them for likelihood of being PFAS compounds.
Use when you have measured intracellular metabolite concentrations (e.g., via LC–MS/MS) across multiple cell lines or samples and want to predict which metabolic reactions are…
Use when after loading an MsmsSpectrum object but before intensity filtering or spectral annotation.
Use when you have trained a DNN retention time predictor and need to rank candidate metabolites for an unknown compound: the DNN outputs both point estimates and uncertainty…
Use when when you have preprocessed MS/MS spectral data (normalized peak intensities and m/z values) and need to convert individual spectra into fixed-dimensional vector — from…
Use when when generating augmented variants of single-channel or multi-channel ion images for contrastive learning in mass spectrometry imaging analysis.
Use when after initial peak detection on composite mass tracks via local maxima and smoothing, when you have unfiltered peak lists (JSON or structured format) containing…
Use when you have spatial molecular data (e.g., coordinates from microscopy or sequencing assays stored in an AnnData object), you need to compute a k-nearest-neighbor graph for…
Use when you have extracted peaks from multiple LC/HRMS batches (n > 1) with their m/z and RT values, and you need to identify and align peaks representing the same compound…
Use when you have a raw peak-picked untargeted LC-MS dataframe with columns containing mass-to-charge (m/z), retention time (rt), feature identifiers, adduct annotations, and…
Use when when you have access to a set of gallery or benchmark scripts executed across multiple plotting backends and need to quantify which backend delivers the fastest median…
Use when after running the msfeast_pipeline notebook to generate dashboard_data.json
Use when you have filtered ATAC-seq peak counts, matched motifs to those peaks, and want to measure which transcription factor motifs show elevated or reduced accessibility…
Use when after extracting ion chromatograms and ion mobilograms from raw diaPASEF or DIA data within specified m/z, retention time, and ion mobility windows.
Use when you have ATAC-seq BAM files and a set of genomic coordinates (e.g., transcription factor motif sites, peak regions) and need to quantify the spatial distribution of Tn5…
Use when you have discovered Mass2Motifs via LDA and need to (1) load a pre-computed motifset JSON file (e.g., motifset_optimized.
Use when you have raw MS data files from one or more instrument vendors (Agilent, Bruker, Thermo Fisher, or mzML-formatted) and need to convert them to a vendor-agnostic…
Use when after loading centroided .mzML LC-MS data and creating a target list with compound ID, name, theoretical or measured m/z, expected RT (in minutes), and polarity…
Use when after anchor selection and RT mapping spline construction, when you have a fitted metabCombiner object with pre-aligned feature pair candidates and need to tune the…
Use when when you have MS/MS fragment spectra (in .mgf format) acquired from unknown metabolite features and need to annotate them against known compounds.
Use when you have statistically significant LC-MS features grouped into structural clusters (isotopologue groups, adduct groups, cross-assay links) and correlation cluster…
Use when you have mzML/mzXML chromatogram files from Thermo, Waters, or Bruker instruments and need to extract MS1 and MS2 scans matching both a target m/z value AND a known or…
Use when you have implemented or are evaluating an algorithmic or system optimization (e.g., MASST+) that claims to reduce execution time, and you need to quantify and…
Use when you have a pre-trained deep learning encoder (e.g., TCN spectrum encoder trained on a large corpus) and want to adapt it to a new task (e.
Use when you have a normalized gene expression matrix (bulk RNA-seq or microarray) from a time-course or multi-condition experiment and need to quantify whether known gene sets…
Use when when a trained Siamese neural network model makes predictions on new spectrum pairs and you need to identify and exclude high-uncertainty predictions to improve RMSE.
Use when after marker identification or feature selection has produced a list of discriminatory m/z features, and before pathway enrichment analysis (e.g., KEGG).
Use when you have a GC-MS results table with a Match.Factor column (representing identification confidence) and you need to understand how many distinct compounds survive at…
Use when after computeDeviations has generated a SummarizedExperiment object with z-score assays reflecting bias-corrected deviations of observed vs.
Use when when you have raw mzML or mzXML mass spectrometry files with uncompressed numeric arrays (not pre-compressed with zlib or msnumpress) and need to reduce file size for…
Use when when deploying a multi-service microarchitecture (such as MAGMa''s four distinct subprojects: magmaweb, joblauncher, job, and pubchem) via Docker Compose and you need to…
Use when when you have high-resolution tandem MS/MS spectra in mzML, mzXML, or MGF format and need to cluster or search millions of spectra efficiently.
Use when you have Sciex Multiquant txt exports containing signal intensities from QCpool samples injected at regular intervals (e.g., every 10–20 samples) during one or more…
Use when when you have run the same mass spectrum through molecular formula assignment under different parameter settings (e.
Use when after normalizing total UMI counts per cell using normalize_total, and before PCA or feature selection.
Use when when you have a query electron ionization mass spectrum (as m/z and intensity pairs) and need to identify it against a spectral library stored in msp format.
Use when you are building a visualization library that must support multiple plot kinds (chromatogram, spectrum, mobilogram, peakmap) across multiple rendering backends…
Use when training neural networks on MS/MS spectra (or similar scientific data) where you need to preserve model states that improve validation performance.
Use when you have statistically significant features from multiple LC-MS assays with different ionization modes (e.g., positive and negative ESI) and need to collapse redundant…
Use when you have a peak-picked feature table (HDF5 format) from high-dimensional MS data (m/z, drift_time, retention_time, intensity) and need to identify and label isotopic…
Use when when applying iterative peak detection (local-maximum or Gaussian-fit methods) to 1D extracted ion chromatograms (XICs), arrival time distributions (ATDs), or MS1 spectra…
Use when you have a collection of MS/MS spectra (≥2 spectra) and wish to identify fragmentation signatures common to subsets of those spectra.
Use when you have raw RNA-seq count data (from HTSeq, featureCounts, Salmon, or similar quantification tools) organized in a count matrix with samples as columns and genes as…
Use when when you have retention times measured on one chromatographic method and need to predict or map them to another method with minimal or no overlap in measured molecules.
Use when you have MS2 product-ion spectra in open formats (.mzML or .mzXML) from public mass spectrometry datasets (e.g., from MassIVE with a valid accession) and need to identify…
Use when when you have chemical annotations (GNPS matches) distributed across multiple sample groups (e.g., by sample type, extraction method, ionization source) with unequal…
Use when when a user uploads a JSON project document to the Pairing Omics Data Platform and you need to determine whether it satisfies the platform's data structure requi — from…