Use when you have raw or unstructured MS2 spectral data (from untargeted tandem mass spectrometry experiments) and plan to run MS2MP inference for KEGG pathway prediction.
Use when after training a NeatMS CNN model on labeled MS1 peaks and generating predictions on a held-out test set, compute ROC-AUC to assess whether the model achieves the target…
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 when you have sampled the feasible flux solution space of constraint-based metabolic models (via optGpSampler or equivalent uniform sampling) and need to normalize flux…
Use when when you have extracted MS2 spectra from DDA chromatographic peaks and need to identify the originating compound by comparing against reference MS2 spectra (e.g., from…
Use when after performing assignment operations (assign_ri, assign_smiles) or combining multiple library objects (e.
Use when when building a graph-based molecular property prediction model that must process both molecular structures (as heterogeneous graphs) and tabular metadata…
Use when when you have labeled mass-spectrometry spectral data (precursor m/z and fragment m/z–intensity pairs) paired with known molecular structures (as InChIKeys or SMILES),…
Use when after identifying a set of differentially accessible peaks (via tl.diff_test or equivalent), when you need to infer which transcription factors may regulate the observed…
Use when you have a GNPS mass spectral molecular network (classical or feature-based) and MS2LDA-derived Mass2Motif data, and you need to annotate network nodes with both chemical…
Use when when you have loaded an LC-MS spectrum file (mzML, mzXML, or equivalent) into the GNPS LCMS Visualization Dashboard and need to annotate extracted ion chromatograms with…
Use when when you need to understand the modular composition of a multi-component research software project, particularly before onboarding, refactoring, or deploying it.
Use when you have transcript-level abundance estimates from salmon, sailfish, or kallisto quantification and need gene-level count matrices for differential expression analysis.
Use when you have raw 1D NMR spectral data (urine, worm, or other biological samples) that needs to be converted into peak tables for metabolite identification and quantification.
Use when when validating a new or reimplemented quantification tool against a reference implementation on the same dataset and index, or when investigating whether changes to seed…
Use when you need to evaluate whether a newly released or candidate library (e.g., spectrum_utils v0.4.
Use when you have loaded raw ATAC-seq fragment counts into a SummarizedExperiment object and are preparing to compute motif deviations.
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 converting or downloading a pre-trained Keras model to HDF5 TensorFlow 2.3.0 format, particularly when integrating the model into a fixed-interface pipeline (e.g.,…
Use when you have completed one or more LC-MS gradient runs, extracted separation efficiency metrics from the resulting MS1 and MS2 spectra, and need to incorporate those real…
Use when when you have a USI string (e.g., 'mzspec:GNPS:TASK-abc123:scan:1943') and need to retrieve the underlying spectrum data from its native repository without knowing a…
Use when a Python-based metabolomics analysis package has been relocated to a new GitHub organization (e.g., metabolomics-cloud) and you need to confirm that the migration…
Use when you have LC/MS feature data (m/z, retention time, intensity) and need to assign metabolite annotations with confidence scores rather than binary peak-to-compound matches.
Use when you need to understand how a complex MS/MS spectral search system routes query spectra through multiple parallel processing pipelines with different objectives (e.g.,…
Use when you have mass spectrometry spectral data (m/z and intensity pairs, precursor m/z, MS level, and metadata) in R memory or in a file format (mzML, mzXML, CDF, MGF, MSP),…
Use when you have LC-HRMS chromatograms in retention time × m/z matrix format and need to automatically localize chromatographic peak positions and extents prior to matching…
Use when you have performed lazy dask-backed feature extraction on an ImageContainer using im.calculate_image_features and need to persist the computed spatial features into the…
Use when when you have an untargeted metabolomics feature table (m/z values, retention times, intensities) and aim to predict functional pathway activity without explicit — from…
Use when after RAMClustR clustering and do.findmain molecular weight inference are complete, when you need to submit the same inferred spectra to multiple third-party annotation…
Use when after completing Cardinal-based preprocessing (feature summarization, TIC normalization, peak processing, spatial segmentation, and SSC annotation), use this conversion…
Use when you have already assigned samples to batches (inter-batch balance is fixed) and need to shuffle injection order within each batch to decorrelate sample properties from…
Use when you have raw arrival-time data from TWIM-MS and need to convert it to collision cross section (CCS) values for multi-omic analysis.
Use when you have paired microbiome and metabolomic (or similar compositional) data with a new regression model and want to rigorously demonstrate its predictive advantage over…
Use when you have preprocessed single-cell ATAC-seq fragment files or count matrices and need to identify open chromatin regions (peaks) to support downstream differential…
Use when you have raw metabolomics count data (e.g., from mass spectrometry or NMR experiments) in tabular format and associated sample metadata (e.g., treatment groups,…
Use when after successfully matching at least 5 reference m/z points (from a .ref file) to spectrum peaks within a PPM error window (starting at ±1.0 ppm and widened iteratively…
Use when before invoking pp.make_fragment_file on a BAM file from alignment or external sources, especially when the BAM's sort order is unknown or when integrating BAM files from…
Use when you have uploaded a delimited data file (CSV, TSV, or semicolon-separated)
Use when you have measured CCS values from (LC-)IM-MS samples spiked with U¹³C labeled internal standards (e.
Use when ingesting mass spectrometry spectra from heterogeneous databases or libraries where adduct annotations may be incomplete, incorrectly formatted, or inconsistent with the…
Use when you have a matrix of Nightingale Health 1H-NMR metabolomics measurements (samples × features) and need to generate predicted metabolic scores published in peer-reviewed…
Use when your metabolomics experiment includes calibration line samples with known concentrations for spiked compounds, and you have computed batch-corrected…
Use when when you have baseline MS/MS peak annotations from a known compound but need to refine them using newly available structural information (e.
Use when you have a collection of preprocessed tandem mass spectra (binned into 10,000 equally-sized m/z bins, intensities square-root transformed, top 1,000 peaks retained), a…
Use when you have acquired untargeted MS data with orthogonal separations (LC, ion mobility) and/or data-independent acquisition (DIA) from Thermo, Agilent, or Bruker instruments,…
Use when you have a Thermo Fisher Scientific .raw file containing PRM data and need to verify that acquisition of a specific precursor ion (e.g., LGGNEQVTR++ at m/z 487.2567) is…
Use when you need to set up a LipoCLEAN analysis for MS-DIAL lipid identifications and do not yet have a configuration file, or you are switching between MS-DIAL versions 4 and 5…
Use when when you have processed LC-MS/MS data with precursor m/z, ionization mode, collision energy (if available), and fragment peak lists (m/z and intensity pairs), and need to…
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 a labeled peak quality dataset (development set with ground-truth pass/fail labels), a defined set of peak-quality metrics (e.
Use when you have completed batch spectral searches against multiple domain-specific MASST tools (via Fast Search API or individual domain searches) and need to combine and…
Use when after generating a complete lipid spectral library with adduct-specific
Use when you have executed multiple NPDtools database search pipelines (Dereplicator, VarQuest, Dereplicator+, or MetaMiner in different modes) on identical test spectra or RiPP…
Use when when setting up matchms for the first time in a new environment, after upgrading Python or conda, when switching between package managers (pip vs conda), or when…
Use when after extracting and grouping fragments from top x% TIC-filtered replicate spectra for a given feature, to quantify which fragments consistently appear across replicates.
Use when after running qc_summary() on a filtered mpactr object and aggregating ion counts by filter status category (passed/failed).
Use when you have a .msp format MS/MS spectrum library (e.g., from MassBank or similar public databases) and need to convert it into individual CSV entries indexed by positive or…
Use when you have raw untargeted LC/HRMS data (mzXML, mzML, or netCDF format) from population-scale studies (n > 500 samples) and need to extract a comprehensive peaklist with…
Use when after applying Scanpy preprocessing functions (e.g., pp.normalize_total, pp.pca) to a Dask-backed AnnData object, or when performing any operation that could alter matrix…
Use when you have an untargeted metabolomics feature table (m/z values, retention times, p-values from statistical testing) and need to infer which metabolic pathways are active…