Use when you have extracted quantitative genomic features (e.g., insulation scores, boundary annotations) as a pandas DataFrame with bin coordinates and boolean or numeric…
Use when after applying batch correction (e.g., via pycombat) to a multi-batch feature table, to validate whether the correction has reduced systematic intensity differences…
Use when you have an untargeted metabolomics feature table with m/z values, retention times, and intensity measurements, a metabolic network representation with compound — from…
Use when when you need to reproduce a computational workflow described in a GitHub repository, validate CI/CD pipeline definitions (e.g., GitHub Actions workflows), inspect source…
Use when when you have implemented a binary file format encoder (such as igzip header construction) and need to verify that the binary output is correct before deploying it to…
Use when after a GitHub Actions CI workflow has executed static analysis (e.g., via Sonarcloud) and generated a quality report.
Use when working with large GCIMS matrices where computational speed or memory constraints are a concern, after filtering retention time (e.g., 0–1100 s) and drift time (e.g.,…
Use when after completing an Environment simulation or replay with scan-level MS2 acquisition control, and evaluation data has been collected in memory.
Use when you have labeled MS/MS spectra from replicate measurements and need to determine a frequency threshold for denoising that balances competing objectives: retaining true…
Use when you have two or more complementary scoring functions (e.g., strain correlation and IOKR scores) that you wish to combine, and you need to determine which combination…
Use when when you have loaded a raw or processed Cardinal MSImagingExperiment object from MS imaging data and need to (1) extract spectral intensities and m/z feature information…
Use when you have trained a multitask NMR-to-structure model and need to quantify its predictive accuracy on held-out test molecules.
Use when your peak intensity matrix exhibits batch-to-batch variation (retention time drift, signal intensity fluctuation across injection sequences), you have QC samples injected…
Use when you have raw UPLC-HRMS data from ThermoFisher or Agilent instruments and need to feed it into MSThunder for nontargeted pollutant identification.
Use when you have a normalized gene expression matrix (log2-quantile normalized, filtered to high-variance genes) and a collection of annotated gene sets (e.g., Reactome, MSigDB…
Use when when you have trained a regression model on experimental retention times or similar continuous molecular property predictions and need to quantify its generalization…
Use when you have tandem MS/MS spectra annotated by at least two of GNPS (FBMN), ISDB-LOTUS (CFM-ID 4.0 spectral matching), and Sirius 6, and you need to rank features by…
Use when you have SMILES strings of molecules at specific ionization states (e.g., protonated or deprotonated adducts) and need to predict collision cross section values for mass…
Use when when you have Thermo Fisher RAW mass spectrometry files and need to extract mass-to-charge ratios, intensities, scan metadata, and peak lists within a Python script or…
Use when your MetaboSet object contains missing values (marked as NA) in the expression matrix after quality flagging, but you need complete data for multivariate analysis.
Use when after generating a count matrix from fragment data using pp.add_tile_matrix, pp.make_peak_matrix, or pp.make_gene_matrix in SnapATAC2.
Use when you have computed or received a precomputed expected contact frequency table (e.
Use when after running tardisPeaks() in screening mode or peak detection mode, when you need to visually confirm that target compounds are visible in the expected m/z and…
Use when you have a metabolomics count table (rows=metabolites, columns=samples)
Use when after peak picking, sample alignment, and isotopologue/adduct grouping steps have been completed in an untargeted LC-MS workflow.
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 you have a Spectra object in R and need to apply a specialized Python MS algorithm (e.
Use when when you have mass-spectrometry data (m/z and intensity pairs or spectral matrices) paired with ground-truth molecular fingerprints or InChIKeys, and you need to learn a…
Use when you have a large single-cell count matrix (≥10 million cells) in CSR format and need to verify whether the matrix-free spectral embedding in SnapATAC2 achieves its…
Use when when you have preprocessed mass spectra (peak-filtered, metadata-cleaned) in supported formats (mzML, mzXML, msp, MGF, JSON) and need to compare all or many pairs of…
Use when after generating a Chemical Feature Tree from q2-qemistree (or any tree artifact) and before using it for alpha-diversity or beta-diversity phylogenetic analyses.
Use when you have 1D or 2D NMR spectra (1H and/or 13C) and need to predict unknown molecular structure (formula and connectivity) up to ~19 heavy atoms; or you have a set of…
Use when when you have downloaded fragment records from separate experimental and predicted online databases and need to verify that each fragment can be traced back to a valid…
Use when you have acquired LC-MS/MS data in Data-Dependent Acquisition (DDA) mode for untargeted metabolomics and suspect contamination from chimeric (co-fragmented) MS/MS spectra.
Use when when you have mass spectrometry ion image data and need to learn meaningful low-dimensional representations through self-supervised contrastive learning.
Use when when you have fitted one or more regression models (linear or polynomial) to external calibration standards in MS data and need to verify model adequacy before applying…
Use when after running qiime qemistree make-hierarchy and obtaining a tree artifact (qemistree.
Use when you need to generate a comprehensive, non-redundant inventory of lipid species that span a defined lipid class (e.g., phosphatidylcholine, triacylglycerol) and a range of…
Use when you have completed peak calling and cell annotation in ArchR and want to perform trajectory inference or visualization in STREAM.
Use when after peak annotation when you have: (1) a peak intensity matrix (rows=peaks with KEGG/ChEBI/UniProt IDs, columns=samples) with group labels; (2) a pathway database…
Use when you observe discrepancies in mapping rate or per-transcript quantification between two salmon implementations, or when the default chain-pruning thresholds…
Use when after peak detection in GC-IMS preprocessing, when you need to assess whether detected peaks from multiple samples align to the same chemical entities (clusters) using…
Use when when you have aligned ChIP-Seq reads (BED or BEDPE format) and a corresponding control sample, and you need explicit control over peak-calling parameters—including…
Use when when you have genomic sequences (assembled contigs or antiSMASH/BOA mining results) and want to match experimental tandem mass spectra against predicted RiPP structures.
Use when when you have loaded a dataset of molecular fingerprint vectors (such as biosynfoni fingerprints from a Zenodo deposit) and need to quantify how sparse the…
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 you have raw LC-MS/MS data in mzML or mzXML format and need to: (1) identify the top-abundance MS1 signals in an LC run, (2) compute a single scalar metric (separation…
Use when after extracting and grouping fragments from top x% TIC-filtered replicate spectra for a given feature, to quantify which fragments consistently appear across replicates.
Use when you have intensity measurements (peak features, protein intensities, or gene expression values) with compound or gene annotations (KEGG IDs, ChEBI IDs, UniProt IDs, or…
Use when after loading and normalizing a beta-valued methylation matrix (450K or EPIC array), apply SVD interpretation when you need to assess whether observed variation is driven…
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 when designing a targeted lipidomics experiment and you have lipid species definitions (including chain composition and adducts) but need to configure precursor–fragment…
Use when after MSConvert has converted vendor-specific raw mass spectrometry data (ThermoFisher, Agilent, or equivalent formats) on a Linux system and before initiating analysis…
Use when before invoking any Python module in a multi-step Hi-C processing pipeline, or when a dependency has been freshly installed or reinstalled.
Use when you have (1) genomic data from a Streptomyces or other RiPP-producing organism in raw FASTA format or annotated GenBank format, (2) high-resolution LC-MS/MS spectra in…
Use when you have raw IMC and SIMS image data from the same tissue region(s) and need to: (1) register the two modalities spatially, (2) segment individual cells across both…
Use when when annotating full-scan MS or MS imaging data against a metabolite database (e.g., LipidMaps, HMDB) and you need to control the stringency of m/z matching.
Use when after marker identification or metabolite annotation has produced a curated list of compound IDs (e.g., KEGG IDs or CAS numbers) and you need to determine which metabolic…
Use when you have downloaded the LC-MS spectral peak dataset (DOI 10.25345/C5FD2F)
Use when you have prototyped a novel data-dependent acquisition strategy that uses dynamic exclusion with intensity or ROI weighting, and you need to quantitatively compare its…