Use when after training multiple MLPNN models (via cross-validation) on paired microbiome and metabolome data when you need to extract interpretable feature importance scores from…
Use when when you have two or more implementations of a spectral search tool (e.g., MASST vs. MASST+) and need to quantify whether claimed performance improvements (e.g.,…
Use when when reproducing a prior software release (especially one generated by automated versioning tools like Semantic Release), you need to confirm that the artifacts produced…
Use when you have raw .idat files or beta-valued matrices from HumanMethylation450 (450k) arrays and need to remove low-quality probes, correct for technical artifacts (batch…
Use when you have raw MS/MS spectral data in one or more standard mass spectrometry file formats (.mgf, .msp, or .mzML) and need to convert them into a standardized…
Use when after batch correction of metabolomics QC samples using pooled study quality control (SQC) samples, when you have multiple candidate internal standards and need to…
Use when when you have a feature intensity table (samples × compounds) from targeted or non-targeted metabolomics and need to prepare it for statistical modeling or multivariate…
Use when you have processed MSI data (peak matrix and spatial coordinates) from matrix-assisted laser desorption/ionization (MALDI) or silver-assisted laser desorption/ionization…
Use when you are designing or optimizing an MsBackend implementation and need to decide whether to pre-populate the @spectraVars slot with all core spectra variable columns (mz,…
Use when a Shiny application or R package is confirmed to work on one OS (e.g., Windows only) but fails to initialize or run on others due to unresolved file path conventions,…
Use when after computing gene set enrichment scores (e.g., via GESECA on reverse PCA feature loadings) on a single-cell or bulk dataset with an existing dimensionality reduction…
Use when you have metabolomics data (targeted LC/MS or untargeted GC/MS) with left-censored missing values below the limit of quantification (LOQ) or limit of detection (LOD), and…
Use when when you need to aggregate values from multiple records in a JSON input document into a single concatenated string field (e.
Use when after running saturation repair or multidimensional smoothing on IM-MS data when you need to validate whether corrected peaks are reliable or whether overlapping…
Use when when preparing NMR datasets for processing in NMRFx and the Dataset.createDataFile() method must choose among competing storage backends.
Use when building a metadata enrichment system that must support multiple pluggable converter backends and you need to automatically discover all available converters at runtime,…
Use when after computing per-bin coverage depth using cooltools.coverage() on a loaded cooler object, when you need to (1) share the coverage track with non-Python tools, (2)…
Use when after running omu_summary (t-test) or omu_anova (ANOVA) on metabolomics
Use when after making code modifications (bug fixes, new features, or refactoring) to the MS2Query codebase, or when contributing changes via pull request.
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 after executing Formation formatting on processed feature tables (output from Blueshift or Gravity modules).
Use when when constructing a composite loss function for contrastive learning on structured data (e.
Use when you need to map computed per-bin metrics (insulation scores, boundary calls, contact frequencies) back to genomic coordinates for export to BED/GFF format,…
Use when you have claims in a paper or tool documentation that one peak picking method outperforms others (e.g., 'IDSL.IPA outperforms MZmine 2 and xcms'), but the specific…
Use when you have a Thermo Fisher Scientific .raw file and need to (1) enumerate all scans and their metadata, (2) identify which scans are MS1 vs.
Use when you have raw SMILES strings from a chemical database (e.g., CCSBase, METLIN, or custom sources) and need to feed them into a graph neural network model.
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 generating synthetic LC/GC-MS .mzML files with companion ground-truth peak tables for method validation, you need to calculate the absolute maximum intensity that…
Use when designing a contrastive learning pipeline for ion images or other data modalities where you need to process multiple augmented versions of the same input through an…
Use when you have generated a .hic contact map from Hi-C raw sequencing data and need to identify topologically associating domains (TADs) or other chromatin structural boundaries.
Use when you have a pair of MS/MS spectra—one from a known compound and one from a structurally related modified (unknown) compound—and need to identify which atom(s) in — from…
Use when you have a txt or tabular export file from a liquid chromatography–mass
Use when when you have validated SMILES strings or RDKit molecule objects representing chemical structures and need to feed them into a pre-trained deep learning model (such as…
Use when you have raw LC-MS/MS data files (mzML/mzXML format from Thermo, Waters, or Bruker instruments) and a list of target compounds defined by precursor m/z values (and…
Use when after rewriting or modifying a Python module (such as calculate_feature_overlap.py
Use when after running pycombat batch correction on multi-batch metabolomics feature tables when you need to validate that batch correction has successfully attenuated inter-batch…
Use when when you have published LOTUS flat files (TSV or compressed TSV.GZ) containing structure-organism pairs and need to enumerate unique structures, group by organism…
Use when after applying one or more mpactr filters (mispicked, group, cv, insource) to an mpactr object and generating a qc_summary() data.
Use when when performing gene-level differential expression analysis on RNA-seq data where samples may express different isoforms of the same gene at different relative…
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 have multiple mass spectral libraries in different formats (NIST MSP + MOL folder, MoNA MSP, RIKEN MSP, SWGDRUG MSP) and need to merge them into a single,…
Use when you have raw or processed arrival-time data from a traveling-wave ion mobility mass spectrometry (TWIM-MS) platform and need to convert it into standardized collision…
Use when you have retrieved a complete set of project JSON documents from a data platform and need to verify that each document's structure, field types, and required properties…
Use when you have independently generated or received both scATAC-seq peak count matrices and scRNA-seq gene expression matrices from the same set of cells (multiome experiment),…
Use when you have raw GCxGC-MS data in NetCDF format that contains instrumental and chemical noise (baseline drift, high-frequency signal artifacts) and you need to prepare…
Use when you have an annotated LC-MS feature table with KEGG candidate matches and adduct assignments (output from the matching stage), and you need to disambiguate which features…
Use when after NPFimg's automated detection algorithm has identified marker features from a two-dimensional MS map (m/z vs retention time), especially when you need to validate…
Use when you have baseline-corrected and smoothed 2D-TIC chromatogram objects from individual GCxGC-MS samples that exhibit retention-time variations relative to a reference…
Use when you have an MS/MS spectrum and a ProForma 2.0 peptidoform specification,
Use when you have mzML mass spectrometry files that need both compression and rapid random access by spectrum ID (e.g., direct retrieval of spectrum 2540 without sequential…
Use when when preparing metabolomics abundance tables with left-censored missingness (values below instrument detection limit or quantification limit) for imputation.
Use when you have a baseline GNN model for predicting a continuous molecular property (e.
Use when when designing a library that needs to support multiple plotting backends (e.g., matplotlib, bokeh, plotly) and you want to avoid reimplementing parameter validation,…
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 you have configured a GitHub Actions workflow that executes build, test, and quality checks, and you want to embed a machine-readable, auto-updating badge in your…
Use when you have acquired raw mass spectrometry data from ThermoFisher, Agilent, or compatible vendors in their native formats (.raw, .d, or equivalent) and need to prepare it…
Use when when you have implemented or modified a data ingestion module (e.g., mzML parser) and need to verify that file deserialization produces correct internal representations.
Use when when you have access to a curated dataset (such as LOTUS) with published headline statistics in a peer-reviewed article or enriched index, and you need to validate data…
Use when when you have millions of high-dimensional objects (e.g., MS/MS spectra converted to feature-hashed vectors) and need to compute pairwise similarities or retrieve nearest…
Use when you have an untargeted metabolomics feature table (with m/z, retention time, and statistical significance values) and want to predict which metabolic pathways and…