Use when you have transcript-level abundance estimates and count matrices from tximport (derived from Salmon, Sailfish, or kallisto output) and need to prepare them for…
Use when you are evaluating or selecting FT-ICR MS software for a specific metabolomics workflow and need to assess which tools support your required analytical dimensions (e.g.,…
Use when you have access to a project README or repository documentation (Zenodo deposit, GitHub, or local clone) describing multiple domain-specific web applications, and you…
Use when when benchmarking or validating a pathway analysis method (such as PALS, ORA, or GSEA) on metabolomics data, you need quantitative evidence that the method's pathway…
Use when you have downloaded the LC-MS spectral peak dataset (DOI 10.25345/C5FD2F)
Use when you have 1D or 2D NMR spectra (1H and/or 13C) and need to predict unknown molecular structure (formula and connectivity) up to ~19 heavy atoms; or you have a set of…
Use when when you have preprocessed MS/MS spectra converted to a bag-of-fragments
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 after preprocessing a set of aligned 2D-TIC (two-dimensional Total Intensity Chromatogram) matrices from GCxGC-MS experiments—when you have multiple samples across…
Use when after generating a frequency count table (e.g., from count_fold_changes
Use when when you have raw LC-MS/MS DDA spectral data (positive and/or negative ionization modes) paired with sample metadata (originating taxon), and you need to detect molecular…
Use when you have multiple LC-MS runs with the same set of targets (compounds) and observe or expect retention time drift or jitter between runs.
Use when after you have detected LC-MS features, grouped them into empirical compounds via isotope and adduct clustering (using khipu), and have accurate m/z and retention time…
Use when you have pairs of augmented ion images from mass spectrometry imaging data and need to generate low-dimensional representation vectors that maximize similarity between…
Use when you have LC-MS/MS DDA data from one or more samples and need to organize fragmentation spectra by similarity relationships to support compound annotation, enable…
Use when a Python library exposes functionality that depends on external packages (like sqlalchemy, pandas, or lxml) that are not required for core operations.
Use when you have generated a feature table via mzrtsim() with simulated LC/GC-MS abundances, condition assignments, and batch labels, and you need to pass it to Bioconductor…
Use when after duplicate filtering of MZmine-exported MGF and CSV files, when you have combined spectra from multiple samples in a single MGF and need to segregate them by sample…
Use when after identifying statistically significant LC-MS features (e.g. via MB-VIP permutation testing) when you need to consolidate redundant measurements of the same…
Use when when you have a Thermo Fisher Scientific Orbitrap .raw file and need to confirm that a targeted acquisition method (e.g., PRM targeting a specific precursor m/z) is…
Use when when you have a Thermo Scientific .raw file from an Orbitrap instrument (e.
Use when you have preprocessed MS/MS spectra (noise-filtered, normalized) and need to compute pairwise similarity or distance scores for compound library matching, when your goal…
Use when when you have pre-computed spectral embeddings (vectors) for both query spectra and a reference library, and you need to measure retrieval performance by ranking…
Use when when you have extracted mass tracks (EICs) from individual LC-MS samples and need to establish reliable landmarks for subsequent pairwise or global alignment across a…
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 you have raw mass spectrometry data from direct-infusion (DI-MS) or ambient surface analysis probe (ASAP-MS) instruments and need to identify which m/z peaks are…
Use when you have completed XCMS grouping on LC-MS data and suspect misaligned features due to long acquisition periods (>1 week) or large sample cohorts (hundreds of samples).
Use when after LDA inference has produced a trained motifset (motifset.json or motifset_optimized.json) with Mass2Motif probability distributions over fragments and neutral losses.
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 when you have preprocessed 1H NMR spectral data from flavor mixtures or similar compound identification tasks, and you need to identify which compounds are present.
Use when you have an mzML file (e.g. Manuels_customs_ids.mzML) with non-standard
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 you have an untargeted metabolomics dataset with a two-layer network topology already constructed (one layer representing biochemical knowledge/pathways, the other…
Use when you have loaded mzPeak spectrum or chromatogram metadata and signal data into PyArrow Table structures (via the Python mzPeak reader or equivalent) and need to persist…
Use when you have executed batch searches of MS/MS spectra against multiple domain-specific MASST indices and need to synthesize results across domains (e.
Use when after completing the MS2LDA LDA modeling step when you have a JSON-serialized inferred motifset (Mass2Motifs with fragment and neutral-loss patterns) and need to annotate…
Use when a paired omics project JSON document contains genome identifiers (e.g. IMG IDs, NCBI accessions) but lacks corresponding organism names.
Use when when processing in-silico or experimental MS spectra records from databases with incomplete metadata, specifically when the adduct field is null or absent but the ionmode…
Use when you have raw or normalized microarray expression matrices stored in public repositories (GEO accessions) along with sample metadata, and you need to load both into R as…
Use when after generating a Chemical Feature Tree from q2-qemistree (or any tree artifact) and before using it for alpha-diversity or beta-diversity phylogenetic analyses.
Use when you have positive-mode tune mix reference data (e.g., example_tune_pos.h5)
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…
Use when you have a CSV or Excel file containing chemical structure descriptors for one or more molecules, and you want to obtain binary bitter/not-bitter predictions for each…
Use when after running do.findmain on a RAMClustR-clustered object to infer molecular weights and assign features to compound clusters, when you need to conduct structural…
Use when after drift correction and before imputation, when you have a MetaboSet object with LC-MS peak abundances and need to remove features with insufficient detection…
Use when you have measured execution times from multiple scripts that exercise different combinations of categorical variables (e.g., plot types: chromatogram, mobilogram,…
Use when you have LC-MS normalized intracellular metabolite abundance data from multiple cell lines (or samples) and need to compute reaction-level propensity scores that account…
Use when you need to quantify the degree of match between two MS/MS spectra—either
Use when you have a two-dimensional MS map (m/z vs retention time) from GC–MS or LC–MS data and need to discriminate analytes and identify marker features without false positives…
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 when you have structural clusters from multiple LC-MS assays (e.g., positive and negative ion modes, or reversed-phase and HILIC methods) and need to identify which…
Use when when a C++ program writes records to an output stream (e.g., SAM alignment file) and the final output file contains fewer records than expected based on upstream counts…
Use when after constructing a count matrix from transcript quantification files (via tximport, HTSeq, featureCounts, or direct alignment) and before running DESeq() differential…
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 raw Bruker NMR spectral files (from a Bruker instrument) in a directory and need to prepare them for automated metabolite identification and quantification using…
Use when you have a generic constraint-based metabolic model (SBML format) and cross-sectional omics data (RNA-seq, intracellular metabolomics, YSI or bioanalyzer extracellular…
Use when when you have ionized adduct structures (SMILES or MOL format) from a prior ionization-state determination step and need to create multiple low-energy 3D conformations…
Use when you need to represent, validate, and manipulate molecular compositions in MS analysis—specifically when annotating precursor or product ions with elemental formulas,…
Use when you have tunemix reference data acquired in both positive and negative ion modes and need to establish independent CCS calibration curves for each mode.
Use when you have acquired LC-MS/MS data in Data-Dependent Acquisition (DDA) mode for untargeted metabolomics and suspect contamination from chimeric (co-fragmented) MS/MS spectra.