Use when when an algorithm claims linear or sublinear time/space complexity (e.g., matrix-free spectral embedding) and you need to verify that claim holds for datasets at the…
Use when training neural networks on MS/MS spectra (or similar scientific data) where you need to preserve model states that improve validation performance.
Use when you have peak intensity data from metabolomics experiments with annotated metabolites assigned to known groupings (KEGG pathways, Reactome, GNPS Molecular Families, or…
Use when you have xcms-processed LC-MS data with detected feature groups (from xcms grouping), suspect retention time misalignment across samples due to long acquisition periods…
Use when after successfully reading and validating a tab-delimited metabolomics file (containing mandatory columns: aliquot, compound, area, type, injection_time, batch) using…
Use when you have genomic clusters (GCFs) and metabolomic features (MFs) from paired microbial datasets, each with strain membership information, and you need to score potential…
Use when you have raw or baseline-corrected metabolite abundance measurements from mass spectrometry and need to prepare them for batch effect correction (e.g., CordBat) or…
Use when after isotopic correction has been performed on MSI ion images and you need to convert normalized intensities into absolute quantitative values.
Use when when processing raw or centroid mass spectra (e.g., ESI-MS or FT-ICR data from Bruker .d or Thermo .raw formats) and you need to remove instrument noise and low-abundance…
Use when you have raw tabular experimental data (CSV or Excel) with column headers annotated using MESSES tag syntax (#<table_name>.id for record identifiers, #.
Use when after generating combined or alternative scores for a set of BGC-metabolite (GCF-MF) link candidates, you need to evaluate whether a scoring function preferentially ranks…
Use when you have a complete metabolomics data matrix (simulated or real abundance table) and need to create reproducible, controlled MNAR scenarios for evaluating imputation…
Use when you have .mzML or .abf LC-HRMS metabolomics raw data files and need to perform peak detection, feature identification, and chromatogram alignment reproducibly across…
Use when when you have LC-MS/MS acquisitions in DDA mode and need to train a customized DNMS2Purifier model to purify chimeric MS/MS spectra specific to your experimental…
Use when you have a set of RDKit-generated conformers ranked by ASE-ANI single-point energies, and you need to submit the lowest-energy subset to quantum software (e.g.
Use when when evaluating how well mass spectral similarity scores correlate with actual chemical structure for annotated spectral pairs (e.g., spectra with InChIKey metadata).
Use when you have centroided LC-MS/MS spectral data (in MGF, mzXML, mzML, or mzData format) and want to identify known or predicted natural product structures present in your…
Use when after batch correction of metabolomics data using pooled study quality control (SQC) samples and calculation of compound/internal standard ratios, when you need to decide…
Use when after computing PLAGE-derived activity scores for pathways or metabolite sets (Molecular Families, Mass2Motifs) from log2-standardized metabolomics intensity data.
Use when you need to enable optional modules in Pyteomics that depend on external libraries not bundled with the core package—such as h5py and hdf5plugin for mzMLb format access,…
Use when after organism names have been cleaned and standardized (e.g., via 1_cleaningOriginal.R and 4_cleaningTaxonomy.R) but before final integration of organism, structure, and…
Use when you have pre-trained Keras models from the NP-Classifier repository that must be deployed via TensorFlow Serving and need to expose standardized input/output layer names…
Use when you have per-sample metabolite abundance data and a metabolite-to-pathway
Use when you have loaded normalized methylation beta-value matrices from Illumina EPIC or 450k arrays and need to move beyond single-CpG differential methylation testing to…
Use when evaluating the reliability of non-targeted data pre-processing (NPP) tools (XCMS, MZmine 2, MS-DIAL, etc.) on known metabolite peaks.
Use when when you need to assess whether retention times measured on a given LC-MS run follow the expected linear relationship defined by iRT peptide standards (e.g., Pierce or…
Use when when preparing to create a release branch in a Maven-based multi-module
Use when when initializing an mWISE annotation pipeline with a new or custom KEGG database, or when you need to reconstruct the Cpd.Add matching table with modified…
Use when you have loaded raw ATAC-seq fragment counts into a SummarizedExperiment object and are preparing to compute motif deviations.
Use when when implementing or auditing a data replacement method (e.g., `mz<-`, `intensity<-`) in an MsBackend subclass that must enforce ordering or format constraints on peak…
Use when you have acquired EI or MS2 library files in MSP format (e.g., from NIST via Lib2NIST export, RIKEN, MoNA, SWGDRUG, or GNPS) and need to read them into R to assign…
Use when you have QCpool (pooled quality control) samples measured at regular intervals across one or more LC-MS/MS sequences and need to detect whether instrument performance…
Use when you have millions of MS/MS spectra to cluster and have already constructed nearest neighbor indexes (partitioned Voronoi diagrams of spectrum vectors bucketed by…
Use when you have loaded raw MSI spectral data (imzML format) in profile or centroid mode and need to remove background noise and baseline artifacts before intensity normalization…
Use when you have molecular structures (SMILES or graph representations) and need to predict or analyze infrared spectral properties using message passing neural networks.
Use when you have raw GCxGC-MS chromatogram data in NetCDF (CDF) format from an instrument and need to load it into R for preprocessing (smoothing, baseline correction, peak…
Use when when calling filter functions (e.g., filter_mispicked_ions(), filter_group(), filter_cv()) on R6-based metabolomics data objects in the mpactr package and you need to…
Use when you have a query spectrum (or set of query spectra) and need to rank candidate library spectra by their likelihood of sharing the same chemical structure.
Use when after feature detection and alignment on raw MS data, when you have a list of unknown feature m/z values and need to assign them to known xenobiotic metabolites or their…
Use when you have mass spectrometry data stored in a non-standard format (SQLite database, custom indexed gzip files, or other database backends) and want to enable pymzML's…
Use when you are implementing ORA for metabolomics pathway analysis and must decide which metabolites constitute the statistical background against which to test your experimental…
Use when performing untargeted metabolomics annotation (i.e., matching observed spectra to a compound database without a pre-defined target list) and you need to assign…
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 completed an initial ModiFinder analysis on a compound pair (known compound + modified analog with unknown structure), and you subsequently acquire or…
Use when when you have a C++ library (such as OpenMS) with nanobind binding specifications in a designated bindings directory and need to create a Python module that exposes C++…
Use when removing invalid or malformed entries (e.g., SMILES validation, format errors) from large spectral datasets (GNPS, MoNA, MTBLS1572, MassBank).
Use when when you are developing or contributing to a Python package (like cooltools) and need to test changes to utility functions, library integrations, or API implementations…
Use when when you have a mass spectral library (MSP format) that lacks SMILES annotations but is paired with a folder of MOL structure files (from Lib2NIST export or similar…
Use when you have a cooler file (.cool or .mcool) from a Hi-C experiment and need to generate a genome-wide track of per-bin sequencing depth to assess coverage uniformity,…
Use when you are setting up HiC-Pro or a similar multi-tool pipeline for the first time, or you need to validate that all required dependencies are installed and discoverable.
Use when you have developed a predictive model and need to compare its performance against established baselines (e.g., linear regression, Random Forest, Canonical Correlation…
Use when after computeDeviations has generated a SummarizedExperiment object with z-score assays reflecting bias-corrected deviations of observed vs.
Use when you have executed multiple database search pipelines (Dereplicator, VarQuest, and/or Dereplicator+) on centroided LC-MS/MS spectra (in MGF, mzXML, mzML, or mzData format)…
Use when after normalizing a metabolomic feature matrix when you have both non-QC (study) samples and QC (quality-control) replicates in the same experiment.
Use when when you have raw TWIM-MS arrival-time data and need to transform it into absolute CCS values for downstream biomolecular class assignment or comparative analysis.
Use when when you need to quantify and compare the effect of multiple filtering thresholds (e.g., Match.Factor ≥65, ≥80, ≥90) on the size of a retained compound set.
Use when when you have computed deviation and variability scores using chromVAR for two or more discrete parameter configurations (e.g., 6-mer vs 7-mer kmers, or different motif…
Use when when you have a pre-trained model and need to report stable, generalizable performance on a fixed training set with multiple held-out test splits.
Use when you have an unknown query spectrum suspected to carry a post-translational
Use when after fitting a linear model with lmFit on voom-transformed or log2-normalized RNA-seq or microarray expression matrices, apply eBayes moderation to moderate gene-wise…