Use when when preparing to run Over-representation Analysis (ORA) on a metabolomics study, after constructing the background set but before running the enrichment test.
Use when when ingesting raw LC-MS/MS output from a mass spectrometry instrument and you need to prepare it for metabolite identification, fragmentation tree computation, or…
Use when when preparing augmented training data for a Siamese rescore model that must learn to rank correct molecular formulas above incorrect ones;
Use when you have extracted tabular data into an intermediate JSON form and need to restructure records by mapping input fields to output dictionary keys, collating multiple…
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 (1) spatial omics data loaded in AnnData format with a pre-built spatial neighbor graph (from squidpy.gr.spatial_neighbors() or similar), (2) a categorical…
Use when you have centroided MS2 spectra from data-dependent LC- or GC-HRMS measurements and need to rapidly prioritize potential PFAS features within a larger feature set.
Use when apply this filter after loading raw .idat files or beta-valued matrices from HumanMethylation450 or EPIC methylation arrays when you need to remove probes with…
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 peak clustering has been performed on aligned GCIMS samples and a peak table matrix has been constructed, but the matrix contains NA values because some samples did…
Use when you have raw Bruker NMR spectral data files (1D 1H format) stored in a directory structure and need to prepare them for automated metabolite identification and…
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 a Python module declares optional/conditional dependencies (e.g., sqlalchemy for database access) and you need to confirm that the module can be imported and instantiated…
Use when you have preprocessed MS/MS spectra binned into 10,000 equally-sized m/z bins (10–1000 m/z range) with square-root-transformed intensities, and you need to generate…
Use when you have isotope-corrected or raw ion-image intensity matrices from LipidQMap or similar MSI software and need to: (1) export them as persistent HDF5 containers for…
Use when after implementing or modifying an mzML parser module that converts mzML files into MS-DIAL's internal data model, and before integrating the parser into the production…
Use when after a mass spectrum has been matched against a reference m/z file (e.g., SRFA.ref) and a sufficient number of calibration points (≥5) have been identified within a…
Use when when processing a batch of LC-MS samples in mzML or mzXML format where at least one file has been designated as a quality control (QC) file, extract its TIC or BPC before…
Use when when ingesting or updating MassBank records in plain-text or structured format, and you need to verify that metadata fields (accession, name, formula, mass, spectrum…
Use when after deploying a TensorFlow Serving container (especially within a Dockerized stack like NP-Classifier), before running classification or inference pipelines, to confirm…
Use when after spreadOut() has converted raw CSV peak data into a structured list, when you have one or more Compound.Name entries from GC-MS that may be ambiguous, non-canonical,…
Use when after bias-corrected ATAC-seq signal tracks (bigWig files) have been generated and you need to quantify transcription factor binding strength within open chromatin…
Use when apply log transformation when peak intensity distributions are right-skewed with heteroscedastic variance (intensity-dependent noise), particularly in QC-based batch…
Use when you have a feature table from LC-MS data alongside blank (solvent-only) sample runs, and you want to remove features whose intensity in study samples is not substantially…
Use when you need to determine the complete set of validated instrument/vendor and acquisition mode combinations for a mass spectrometry analysis tool, when assessing whether your…
Use when when processing a metabolomics feature table through multiple sequential transformations (e.g., imputation, normalization, batch correction, annotation) and you need to…
Use when after drift correction has been applied to a MetaboSet object, and before imputation and batch correction.
Use when you have downloaded raw spectroscopic data files (NMR, HSQC, COSY, IR modalities) from the Zenodo repositories and need to convert them into the standardized multi-modal…
Use when after loading a feature table into memory when the table contains zero or missing values that represent true signal loss (not genuine absence), and you need to impute…
Use when you have a SummarizedExperiment object containing pooled quality control samples with measured compound and internal standard peak areas.
Use when when executing peak integration on preprocessed GC-IMS data (after alignment and baseline correction) and you need to decide whether to include or exclude peaks that…
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 deploying a multi-container application stack using docker-compose
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 aligned peak data from molecular networking (with m/z, intensity, retention time, and alignment quality metrics across multiple spectra) and need to…
Use when you have centroided mzML files from LC- or GC-HRMS instruments (acquired in data-dependent mode with ddMS2) and need to systematically identify chromatographic peaks,…
Use when you have a pre-trained GNN CCS prediction model and need to assess its predictive performance and cross-dataset generalizability.
Use when after applying any sequence of spectrum preprocessing operations (set_mz_range, remove_precursor_peak, filter_intensity, scale_intensity) to an MsmsSpectrum object, to…
Use when you have a mature C++ library (like OpenMS) with stable APIs that you want to make accessible from Python environments, and you need to preserve performance-critical C++…
Use when after a CNN model has generated predicted molecular embeddings from mass spectrometry data, and you need to identify the most likely candidate molecules from a r — from…
Use when when you have mass spectral libraries from multiple sources (NIST, MoNA, RIKEN, GNPS) in disparate formats (MSP, MGF, MOL folder structures) or with misaligned metadata…
Use when you have labeled training data (e.g., pqm_development with 500 peaks and 89 samples) and need to select which of multiple classification algorithms (e.g., AdaBoost,…
Use when you have a log2-normalized, zero-mean and unit-variance standardized intensity matrix of metabolite features (rows=metabolites, columns=samples) and need to compute a…
Use when you have a domain-specific language (DSL) grammar specification and raw query strings that must be converted into structured intermediate representations for validation,…
Use when after completing all per-sample annotation steps (molecular networking, ISDB/spectral matching, SIRIUS/CSI:FingerID, and compounds metadata enhancement with Wikidata IDs…
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 after integratePeaks has been executed with a chosen integration method (e.g., fixed_size with RIP saturation threshold of 0.1) on a clustered, baseline-corrected GC-IMS…
Use when when you have a small set of matched reference features (isolated, high-quality chromatographic peaks from reference chromatograms that have been aligned to a…
Use when you need to verify that visualization functions produce graphically correct output that matches previously validated baseline images.
Use when you have raw or minimally processed single-cell RNA-seq expression data loaded into an AnnData object (dense, sparse, or Dask-backed array as X), and you need to apply…
Use when when you have loaded aligned peak data (from a preceding molecular networking alignment task) as a structured table with peak intensity, m/z, retention time, and…
Use when after mark_nas() has replaced non-NA missing-value codes (e.g., 0, 1) with R's NA in the exprs matrix of a MetaboSet object, and you need to decide whether to apply…
Use when when you have a table of execution times or performance metrics indexed by two or more categorical dimensions (e.g., plot_type × backend, or sample × condition), and you…
Use when when you have a parsed mass spectrum (precursor m/z, ionization mode, collision energy, and fragment peak list as m/z–intensity pairs) and need to obtain molecular…
Use when you have a pre-trained deep learning encoder (e.g., TCN spectrum encoder trained on a large corpus) and want to adapt it to a new task (e.
Use when working with untransformed metabolomics count data (e.g., c57_nos2KO_mouse_countDF)
Use when you have a list of candidate metabolites for an unknown compound (from mass-to-structure search or library matching), experimental retention time(s) from one or more…
Use when a mass spectrometry analysis pipeline must accept data from multiple sources with different identifier schemes (GNPS Task ID, Universal Spectrum Identifiers, or…
Use when when you have implemented conditional routing logic in the GNPS_MASST codebase and need to verify that spectrum submissions with explicit domain-context selections (e.
Use when when you have applied multiple scoring functions (e.g., strain correlation and IOKR) to rank genomic-metabolomic (GCF-MF or BGC-spectrum) links and need to verify that:…