Use when a webservice component (like MAGMa's joblauncher) lacks formal API documentation but the source code is accessible, and downstream consumers (web applications, external…
Use when you have trained MLPNN models on paired microbiome-metabolome data (via 10-fold cross-validation repeated across multiple iterations) and need to derive interpretable…
Use when you have extracted clustering or classification accuracy metrics (NMI, ARI, purity scores) for two or more competing methods evaluated on multiple datasets, and need to…
Use when when processing GC–MS or LC–MS data as m/z vs retention time chromatograms and you need to identify biomarker or chemical marker features without conventional peak…
Use when you have an unknown compound's mass spectrum (m/z peaks and intensities) in positive or negative ion mode and need to identify candidate metabolites from a struc — from…
Use when training embeddings from multi-modal spectral data (peak information + metadata) where you need to ensure both contrastive discriminability AND reconstruction fidelity.
Use when when processing centroided .mzML LC–MS runs with a multi-polarity target list (i.e., some targets ionize in positive mode, others in negative mode, or both) and you need…
Use when xCMS grouping has been performed on LC-MS data from studies with hundreds of samples or data acquisition periods longer than a week, where retention time drift structures…
Use when after a GitHub Actions CI workflow has executed static analysis (e.g., via Sonarcloud) and generated a quality report.
Use when you have extracted a centroided MS/MS spectrum from a Thermo Orbitrap raw file (via rawrr::readSpectrum or equivalent) and need to verify that the observed y-ion…
Use when you have raw LC-HRMS profile-mode data and need to identify candidate chromatographic peaks before classification or feature extraction.
Use when when working with raw or filtered MsmsSpectrum objects where peak intensities span a wide dynamic range and need to be normalized for downstream spectrum comparison,…
Use when after completing peak calling and cell annotation in an ArchR project, when you intend to perform trajectory analysis using STREAM rather than ArchR's native monocle3 or…
Use when you have raw lipidomic and metabolomic data files generated by the Multi-ABLE method (high-pressure liquid chromatography–mass spectrometry output) and need to p — from…
Use when you have a multi-cluster single-cell RNA-seq dataset with cell-type annotations and you want to test whether known biological pathways (e.g., KEGG or MSigDB gene sets)…
Use when you have predicted peptide sequences from a de novo sequencing tool (e.g., Casanovo) and want to understand the fine-grained accuracy of the predictions beyond…
Use when you have a trained shallow decision tree on ChemEcho feature vectors (sparse, high-dimensional representations of tandem mass spectra peaks and neutral losses) and need…
Use when apply TMM normalization when you have raw RNA-seq read counts from multiple samples and suspect differences in library composition (e.g., one sample over-represents a…
Use when when you have raw or minimally processed microarray expression data (e.g., from GEO) with intensity values that exhibit sample-to-sample distributional differences and…
Use when you have NMR metabolomics measurements paired with pre-analytical metadata (processing delay times, centrifugation timing, sample type such as plasma vs.
Use when you have an indexed gzip–compressed mzML file (mzML.gz with internal index structure) and need to retrieve and work with individual spectra or chromatograms by integer…
Use when you have raw or processed TWIM-MS data (arrival time and m/z pairs) from multiple lipid, protein, or metabolite classes and need to classify features by biomolecular type…
Use when you have a pretrained deep learning model, a reserved test set with ground-truth annotations, and need to evaluate prediction quality or generate embeddings for…
Use when you have received MSBERT-preprocessed spectral data from GNPS, MoNA, or MTBLS1572 and need to ensure data integrity before training a spectral embedding model.
Use when when you have intermediate JSON data that must be selectively transformed or enriched according to declarative conversion rules—for example, when extracting experimental…
Use when you have raw or processed HRMS/MS data from Q-Exactive, Agilent Q-TOF, Bruker Q-TOF, or SCIEX Q-TOF instruments in formats such as mzML, CSV peaklists, or vendor-specific…
Use when after merging methylation call files from multiple samples using unite() to create a methylBase object, apply PCA when you need to visualize sample-level relationships…
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 a Thermo Fisher Scientific .raw file from an LC-MS run containing a spiked iRT peptide standard mix (e.
Use when you have computed k-nearest neighbors for spatial coordinates (e.g., via pynndescent or another NN backend) and need to store the resulting adjacency and distance…
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 you have transcript-level abundance estimates from salmon, sailfish, or kallisto quantification and need gene-level count matrices for differential expression analysis.
Use when when you have completed feature detection in MZmine3 or similar tools and produced a feature quantification table (rows = features, columns = samples with intensity…
Use when you have intracellular metabolomics abundance data (measured metabolite concentrations) from multiple biological replicates collected from two or more cell lines or…
Use when you have a normalized count matrix (from Salmon or similar quantification
Use when when implementing a custom MsBackend subclass and need to verify that spectra variables (e.g., precursor m/z, retention time, MS level) conform to expected data types…
Use when you need to validate that a repository's automated build, test, or publish pipeline is functioning correctly on a target branch (e.g., release branch); when you want to…
Use when you have millions of MS/MS spectra represented as low-dimensional vectors (via feature hashing) and need to compute pairwise distances only between similar spectra rather…
Use when you are developing or comparing new data-dependent acquisition (DDA) strategies in ViMMS and need to evaluate how well each strategy fragments sampled compounds from the…
Use when you have a peak-abundance matrix from FT-ICR MS (peaks as rows, samples as columns with raw peak intensities) and need to compute abundance-based diversity indices or…
Use when your R-based Spectra analysis workflow requires a specific mass spectrometry algorithm (e.g., CosineGreedy similarity scoring, spectral normalization, or advanced…
Use when when you need to validate that a development build release workflow (such as dev_build_release.yml for a mass spectrometry data processing project) executes without…
Use when when processing multiple LC-MS samples with varying scan numbers or retention-time drift, before constructing composite mass tracks for peak detection.
Use when after peak picking has generated a peaklist of experimental fragment m/z values (from Q-Exactive orbitrap, Agilent/Bruker/SCIEX Q-TOF UHPLC-HRMS/MS, or direct…
Use when you have a preranked gene list (e.g., genes sorted by log2 fold-change, t-statistic, or other continuous metric) and a collection of gene sets or biological pathways, and…
Use when you have computed a histogram of pairwise mass differences from MS peaks and want to determine which observed mass differences correspond to known chemical species such…
Use when when you have a set of molecular structures (N-Me derived unsaturated sterol lipids or structurally similar organic molecules with C=C bonds) represented as SMILES or…
Use when when you have MS/MS spectra with known chemical structures (InChIKeys or SMILES) and want to validate whether a novel or existing spectral similarity scoring method…
Use when after EIC candidate generation and peak detection have been completed on LC/HRMS data, when you need to extract the retention time and intensity values at peak maxima for…
Use when when you have mass spectrometry data stored in non-standard formats (SQLite, HDF5, custom binary) that pymzML does not natively support, and you want to enable…
Use when after you have identified statistically significant LC-MS features and run MamsiStructSearch to generate structural clusters (isotopologue groups, adduct groups,…
Use when you have computed eigenvector values from a prior eigs_cis calculation on a cooler Hi-C matrix and need to classify genomic regions into discrete A/B compartment…
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 when you have access to Rust source code in a repository with a Cargo manifest (Cargo.toml) and need to verify that a library's read and write APIs produce…
Use when after generating a q-value bedgraph track from ChIP-Seq pileup versus local lambda comparison, and you need to identify statistically significant narrow peaks with…
Use when you have a trained GNN model for molecular property prediction (such as CCS) and need to understand which atomic and bond features are most influential in driving…
Use when you have spatial metabolomics data with semicolon-delimited isomer name annotations (such as the 'all_IsomerNames' column in SpaMTP Seurat objects) and you need to reduce…
Use when when ingesting mass spectral libraries (EI or MS2) where SMILES information is embedded in the Comment field rather than in a dedicated SMILES metadata field—particularly…
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 converting MS/MS spectra into Spec2Vec embeddings using a pre-trained Word2Vec model that was trained on reference data (e.g., a subset of GNPS or MassBank).