Use when you have paired experimental and computational predictions for the same biological property (e.
Use when you have chemical annotations (GNPS spectral library matches) assigned to MS/MS samples and a validated ReDU sample-information template (TSV) with categorical metadata…
Use when you have an unknown sample spectrum (m/z peaks and intensities from DI-MS, ASAP-MS, or other high-throughput mass spectrometry modalities) and a reference species…
Use when you have extracted peaks from multiple LC/HRMS batches (n > 500 samples across different analytical runs or days) and observe systematic retention time drift or offset…
Use when when you have high-dimensional replicate experimental data (e.g., metabolomics, proteomics, genomics assays) where technical or biological variability threatens…
Use when you have loaded a feature-by-pixel intensity matrix (HDF5 format following Cardinal::HDF5 layout) from imzML MSI data in positive ion mode and you have identified paired…
Use when you have a metabolomics dataset with left-censored missing values (e.g., below limit of quantification in LC/MS or GC/MS) and need to evaluate multiple imputation…
Use when a paired omics project JSON document contains genome identifiers (e.g. IMG IDs, NCBI accessions) but lacks corresponding organism names.
Use when you have a cleaned organism table (with validated, deduplicated organism names from sources like NCBI, manual curation, or previous cleaning steps) and need to annotate…
Use when you have MS/MS spectra in .msp format and need to retrieve similar compounds or compute spectral similarities for compound identification.
Use when you have a ranked candidate list (e.g., BGCs sorted by IOKR or strain-correlation score) for each test spectrum, a known ground-truth BGC for each spectrum, and you want…
Use when after applying frequency domain calibration (Ledford, linear, or quadratic equation) to a raw FT-ICR mass spectrum, validate the calibration quality by measuring residual…
Use when your input is a spatial dataset (AnnData object with coordinate metadata) paired with a large tissue image, and you need to extract spatial features (via…
Use when when you have transcript-level quantification files (e.g., Salmon quant.sf.gz, kallisto abundance.h5, or RSEM .results) and need to construct a gene-level count matrix…
Use when you have acquired raw mass spectrometry data in vendor-proprietary formats (ThermoFisher, Agilent, or equivalent) and need to analyze it using MSThunder for unknown…
Use when you have parsed metabolite identities with known spin-system coupling constants (J-values) and chemical shifts, and need to generate the theoretical multiplet patterns…
Use when when you need to verify that a specific data transformation (e.g., precursor m/z zeroing, feature scaling, or field masking) is applied consistently across multiple…
Use when when you have preprocessed MS/MS spectral data (normalized peak intensities and m/z values) and need to convert each spectrum into a learned molecular embedding — from…
Use when you have raw NMR metabolomics measurements paired with pre-analytical metadata (e.g., processing delay times, sample type designations [plasma vs.
Use when when you need to reproduce or validate benchmark comparisons between clustering methods on single-cell chromatin accessibility data, particularly when the source…
Use when you have a connected subnetwork of LC-MS features that matched isotope or adduct patterns, and you need to establish a canonical tree representation with a single neutral…
Use when after running saturation repair or multidimensional smoothing on IM-MS data when you need to validate whether corrected peaks are reliable or whether overlapping…
Use when when you have computed Reaction Activity Scores (RAS) from transcriptomics and GPR rules, Reaction Presence Scores (RPS) from RAS normalized flux predictions, and Flux…
Use when when you need to constrain a large metabolite database to a specific instrumental range (e.g., m/z 100–1000) before generating virtual chemical mixtures for LC-MS/MS…
Use when your input is a single-cell gene expression matrix too large to fit in RAM, or you are working in a resource-constrained environment (e.g., shared compute cluster, laptop…
Use when you have a large gzip-compressed file (e.g., mzML.gz) with an embedded index structure in the gzip header comment field, and you need to retrieve specific blocks (e.g.,…
Use when when preprocessing a public MS/MS spectral library (e.g., GNPS) for machine learning and you discover discrepancies between expected and observed compound counts after…
Use when you have trained multiple machine learning classifiers (e.g., AdaBoost, SVM, Random Forest) on the same metabolomics peak-quality training set using k-fold…
Use when you have constraint-based metabolic models with integrated transcriptomics (gene expression), intracellular metabolomics (substrate concentrations), and extracellular…
Use when you have untargeted metabolomics data (e.g., LC-MS/MS spectra) and need to organize compounds by structural relatedness to enable structure discovery for unknown…
Use when after filtering retention time and drift time ranges on raw GCIMS samples but before decimation and alignment.
Use when you have labeled MS/MS spectra from replicate measurements and need to determine a frequency threshold for denoising that balances competing objectives: retaining true…
Use when when testing associations between metabolic features (from NMR or MS) and a phenotype of interest (e.g., BMI, disease status) in a cohort where age, gender, or clinical…
Use when you have loaded an MS2 library (from NIST, GNPS, or other sources via read_lib()) that contains both positive and negative ionization modes mixed in a single file, and…
Use when when you have generated a set of candidate metabolites for a given experimental MS/MS spectrum and need to determine which candidate is most likely to be the true…
Use when when you have a raw or minimally processed scRNA-seq dataset (e.g., a Seurat object loaded from GEO) and need to prepare it for pathway enrichment or coregulation…
Use when you have a ranked list of library candidates (top 2000 by MS2Deepscore)
Use when when comparing mapped read counts between two RNA-seq quantification implementations (e.
Use when you need to generate a comprehensive, non-redundant inventory of lipid species that span a defined lipid class (e.g., phosphatidylcholine, triacylglycerol) and a range of…
Use when after generating a complete lipid spectral library with adduct-specific
Use when when you have multiple candidate spectrum prediction models (e.g., FFN vs. GNN encoders, NEIMS vs. MassFormer vs.
Use when you have consensus metabolic reconstructions for all members of a microbial or plant community (e.
Use when you have a preprocessed feature table from non-targeted LC-MS/MS metabolomics data (after data merging, cleanup, blank removal, and batch correction) and need to test…
Use when you have raw IM-MS data in Agilent MassHunter (.d) or UIMF format from drift tube (DT) or SLIM instruments, and you need to reduce data volume while preserving s — from…
Use when after chromatographic peak detection on preprocessed LC-MS data, when you have detected features (peaks) in multiple samples and need to establish which peaks ac — from…
Use when after initializing and executing a forward pass through a dual-branch RT-Transformer model (combining fingerprint and molecular graph inputs) on a batch of molecular…
Use when you have LC–MS all-ion fragmentation chromatograms already processed by xcms and clustered by RamClustR, a feature table (targetTable.csv format) listing features to…
Use when you have raw mass spectrometry intensity data from targeted analytes and a set of calibration standard measurements with known concentrations.
Use when you have 1H NMR spectral data from complex mixtures and need to identify component compounds, but a single architecture (CNN or Transformer alone) fails to capture both…
Use when you have completed a GNPS1 (METABOLOMICS-SNETS, METABOLOMICS-SNETS-V2, FEATURE-BASED-MOLECULAR-NETWORKING) or GNPS2 (classical_networking_workflow,…
Use when after composite map peak detection has generated a full unfiltered peak list with SNR values computed for each candidate peak.
Use when you have loaded an FT-ICR raw spectrum (e.g., ESI_NEG_SRFA.d in Bruker or ThermoFisher .raw format) and need to identify the m/z positions and intensities of individual…
Use when you have GNPS molecular networking output (from GNPS1 at https://gnps.ucsd.edu
Use when processing a mass spectrometry dataset (in FragHub JSON format or similar) where duplicate spectral records are suspected or known to exist.
Use when when standard conversion directives (headers, collate, fields_to_headers, exclusion_headers, values_to_str, sort_by, test) cannot express the required transformation…
Use when after implementing or modifying a cross-language integration layer that wraps Python mass spectrometry functions (e.g., spectral matching, peak detection, normalization…
Use when you have acquired LC-MS peak tables from both unlabeled (12C) and isotope-labeled (13C) samples from a stable isotope tracing experiment, paired with sample metadata…
Use when after running pycombat batch correction on multi-batch metabolomics feature tables when you need to validate that batch correction has successfully attenuated inter-batch…
Use when when you have SWATH-MS raw data (mzML or vendor format) containing multiplexed MS/MS spectra from multiple co-eluting precursor ions and need to separate these spectra…
Use when when you have replicate MS/MS spectra for the same feature (precursor m/z and retention time) and need to distinguish genuine fragment ions from noise.