Use when when you have MS/MS spectra (LC-MS or equivalent positive ionization mode data) that you intend to embed using Word2Vec or similar distributional semantic models, or when…
Use when after executing a spatial statistics function (e.g., squidpy.gr.sepal) on a spatial transcriptomics dataset in AnnData format, and before using the computed rankings or…
Use when before initiating raw file conversion or feature extraction, when you have a heterogeneous collection of raw LC-MS files (.raw or .mzML) and sample information scattered…
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 when designing or optimizing backends that handle large MS datasets (mzML, mzXML, CDF files via MsBackendMzR), to verify that claimed memory advantages of on-disk or…
Use when when you have .mzML or .abf LC-HRMS raw data files that require MS-DIAL-based feature detection, chromatogram alignment, and metabolite identification, and you need to…
Use when when you have a containerized scientific tool available on Docker Hub (e.
Use when when you have Spectra objects in R and need to apply Python MS library functionality (spectral similarity scoring, filtering, normalization) without leaving the R…
Use when you have retention time predictions from a source chromatographic method and need to predict retention times for a target chromatographic method, but have limited…
Use when you have FTICR-MS direct injection (mzML) data with identified chromatographic peaks and need to correct systematic m/z bias.
Use when when you have mass spectrometry data (mzML, Bruker .d, or CSV) loaded into a Pandas DataFrame with columns for m/z, retention time, ion mobility, or intensity values, and…
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 after generating an ensemble of 3D conformers via RDKit conformation sampling, when you need to reduce the conformer set size before expensive quantum-chemical…
Use when when applying a pre-trained Spec2Vec Word2Vec model to new mass spectra (particularly those outside the model's training distribution), you need to assess whether peaks…
Use when after peak picking (e.g., via MS-DIAL) and quality control filtering, when you have a raw feature abundance matrix with intensity values across multiple samples and need…
Use when when you have computed per-bin insulation scores from a Hi-C cooler file using cooltools.insulation and need to identify discrete genomic boundaries that separate…
Use when you have retrieved chemical formulae and metadata from two or more of HMDB, ChEMBL, or PubChem and need to merge them into a single searchable database without formula…
Use when a Python package has been relocated to a new repository location, reorganized to conform to new organizational standards (e.g., metabolomics-cloud conventions), or its…
Use when you have MSBERT-preprocessed spectral datasets (GNPS, MoNA, or MTBLS1572 format) with SMILES annotations before training a spectral embedding or compound identification…
Use when you need to store or retrieve mass spectrometry spectra (m/z and intensity pairs) from a novel data source or storage medium (e.
Use when when you have run a spectral networking job on GNPS (e.g. ProteoSAFe-METABOLOMICS-SNETS-V2) and need to reuse the network output files locally with MetaMiner or another…
Use when when you need to understand how a multi-instrument mass spectrometry platform (such as mzmine) selectively routes data to different processing pipelines based on — from…
Use when you have acquired tunemix data (positive or negative ion mode, in .h5 format) with known CCS reference values and need to construct a calibration function that will later…
Use when when you have microarray or RNA-seq expression data paired with phenotype/sample metadata describing experimental conditions, treatments, or group assignments, and you…
Use when after matching mass-to-charge ratios to a compound database (e.g., KEGG) and assigning adduct/fragment types, when you have an annotated feature table with retention…
Use when you have an LC-MS peak-intensity matrix (rows = peaks with m/z and intensity; columns = samples) and need to assign KEGG compound identifiers to observed peaks.
Use when you have a mass spectrometry visualization library that claims to support multiple plotting backends and need to verify that: (1) all backends produce functionally…
Use when after reading in per-sample methylation call files with methRead() and obtaining methylRawList objects, but before calculating differential methylation or performing…
Use when when you have a metabolite SMILES structure and need to predict which adduct ions will appear in a mass spectrum acquired with a chemical derivatizing matrix.
Use when after calling MsmsSpectrum.annotate_proforma() to assign fragment ions to a mass spectrum, verify that each annotated peak has the correct ion_type ('b' or 'y'), charge…
Use when you have high-resolution LC-MS data processed through both XCMS feature detection and RAMClustR clustering, and you need to verify the reliability of molecular weight…
Use when you have a raw metabolomics abundance table (e.g., LC/MS or GC/MS peak intensities or concentrations) with non-normal distributions and missing values, and you need to…
Use when after biomolecular class labels have been assigned to features in a TWIM-MS dataset and you have raw ion mobility arrival time measurements.
Use when you have a large gene expression matrix (e.g., thousands of genes) from normalized microarray or RNA-seq data and need to reduce computational burden before running…
Use when when predicting one data modality (e.g., metabolite abundances) from another (e.g., microbiome composition) and you need to distinguish genuine microbe–metabolite…
Use when when you have simulated DDA (data-dependent acquisition) scans from a ViMMS Environment and need to (1) quantify how well the simulated acquisition matched real or…
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 you have ensemble predictions (e.g., from Monte-Carlo Dropout inference with N ≥ 10 forward passes per input) and need to distinguish high-confidence from uncertain…
Use when augmenting mass spectrometry ion images for contrastive learning, specifically when you need to simulate the natural Poisson noise that arises from photon-counting…
Use when when training a deep neural network on mass spectrometry spectral data where overfitting is a risk (especially with data augmentation applied), and when you need both…
Use when when you have authored a custom .csv lipid library and need to confirm it adheres to LipidMatch's documented schema before placing it in the designated library directory…
Use when you have a mass spectral library in MSP format (e.g., from NIST, SWGDRUG, or other sources) exported alongside a folder of MOL files, and you need to populate the SMILES…
Use when when you need to generate synthetic LC-MS/MS data to test fragmentation
Use when you have a Sphinx-based documentation project with multiple gallery scripts (e.
Use when you have run ORA on simulated metabolite sets with known null conditions (no true pathway enrichment) and need to measure how detection coverage, pathway database size,…
Use when when installing HiC-Pro on a shared HPC cluster or multi-node computing environment where job submission must be routed through a scheduler rather than running locally.
Use when after completing outlier detection, batch correction, and quality metric calculation on a SummarizedExperiment object using mzQuality's doAnalysis function, and after…
Use when after sending HTTP requests to API endpoints (such as /classify or /model/metadata on an NP-Classifier server) to verify that the response is parseable JSON and contains…
Use when when you have DDA LC-MS/MS data (mzML format) with identified chromatographic peaks at a specific m/z (e.g., 304.1131) and multiple MS2 spectra fragmented from that…
Use when you have loaded fragment data from single-cell ATAC-seq experiments into a backed AnnData object (with fragments stored in .obsm['fragment_paired'] or .
Use when you have a metabolomics dataset and want to perform pathway enrichment analysis using ORA, but need to first understand its behavior, limitations, and correct application…
Use when you have a query mass spectrum (or a metabolite reference spectrum from public data) and need to search it against a large-scale spectral repository (≥billions of…
Use when after generating a feature table from mzML data (via Asari) and before performing MS1 or MS2 annotation.
Use when when you have an unknown compound's mass spectrum (m/z peaks and intensities in .mgf or equivalent format) and need to identify structurally related metabolites — from…
Use when when comparing mapping outputs between two selective-alignment implementations (e.g., C++ vs.
Use when when you have tandem mass spectra (mz/intensity pairs with precursor m/z) and need to train interpretable machine learning models—particularly decision trees or — from…
Use when you have 1D ¹H and/or ¹³C NMR spectra (as preprocessed numerical arrays or peak lists) from an unknown organic molecule with ≤19 heavy atoms, and you need to recover its…
Use when when preparing raw mass spectrometry imaging (MSI) ion images for deep learning–based representation learning, especially when you need to generate augmented image pairs…
Use when when reproducing or validating a tandem mass spectrometry denoising pipeline on mzML files with known feature precursor m/z and RT coordinates, compare pre- and…
Use when after executing forward inference on preprocessed mass spectrometry spectra with a deep learning model (e.g., PS²MS), when you have per-spectrum predictions with…