Claude Skills by HolobiomicsLab

Use when when you have performed many pairwise correlation tests between candidate parent and adduct ion intensity pairs in MSI data and need to identify statistically significant relationships while controlling for multiple-comparison bias.

Use when you have Bruker .d/.baf format mass spectrometry imaging data and need to ingest it into MSIGen for conversion to visualizable ion images. This skill applies when your raw data originates from Bruker TIMSTOF or similar instruments and you lack direct .

Use when you have an observed m/z value from mass spectrometry imaging and need to assign a chemical formula with high confidence.

Use when you have a peak list extracted from MSI data that includes candidate peaks with potential m/z overlap or spatial co-localization patterns across tissue images.

Use when you have mass spectrometry imaging data in Cardinal format (versions 2.

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 for conversion to other formats (e.g., Seurat), (2) verify that normalization or spatial segmentation (e.

Use when after completing Cardinal-based preprocessing (feature summarization, TIC normalization, peak processing, spatial segmentation, and SSC annotation), use this conversion when you need to leverage Seurat's downstream statistical methods—such as differential metabolite expression testing.

Use when when you have multiple CDF files from mass spectrometry imaging experiments (e.g., root tissue MALDI-MS data) that need to be ingested into Matlab for linear imaging analysis. Trigger: presence of .

Use when you have co-registered IMC (protein imaging mass cytometry) and SIMS (secondary ion mass spectrometry for metabolites) data from the same tissue regions, cell segmentation masks, and need to assign cell types based on protein expression patterns, then overlay those assignments onto.

Use when when you have received chemical annotations from GNPS spectral library matching workflow and need to assess their reliability before downstream analysis (e.g., chemical explorer visualization, sample filtering, or comparative metabolomics).

Use when when you have mass-to-charge (m/z) values from mass spectrometry imaging or other MS experiments and need to assign molecular formulae with high precision, especially in spatially-resolved metabolomics where traditional LC-MS annotation methods are insufficient.

Use when you have processed MSI peak data (in rMSIproc format) and need to distinguish matrix-related ions from analyte signals. Specifically: (1) you have a peak matrix with m/z values and spatial intensity maps; (2) you have a reference matrix identity (e.g., ''Ag1'' for silver);

Use when after computing all pairwise mass differences from MS imaging peaks and binning them into a histogram, use this skill when you need to prioritize which mass differences are most likely to represent real chemical adducts (rather than noise or measurement artifacts) by ranking them by.

Use when you have a histogram of mass differences (from pairwise comparisons of detected m/z values in MALDI-MS or MSI data) and need to annotate which differences correspond to known molecular adducts—particularly when investigating unexpected or ambiguous peaks in the mass spectrum, or when.

Use when you need to verify that a GitHub Actions workflow (such as dev_build_release.yml) successfully completes end-to-end, especially after code changes or to confirm that automated build infrastructure is functioning correctly.

Use when when you have preprocessed mass spectrometry imaging (MSI) ion images and need to generate augmented image pairs for contrastive learning in co-localized ion discovery tasks.

Use when you have two co-registered LA-ICP-MS element channel images and need to quantify whether their spatial distributions are statistically correlated or independent. Use it specifically when investigating whether two elements co-occur spatially (e.

Use when preparing ion image data for representation learning in mass spectrometry imaging, specifically when you need to augment raw ion images to generate pairs of diverse views for contrastive loss training.

Use when you need to install a Python package that is distributed via GitHub but not yet (or only occasionally) published to PyPI, such as pyBaf2Sql for Bruker .baf/.d mass spectrometry imaging data conversion.

Use when you have access to both raw data (deposited in a repository like Zenodo) and analysis scripts (in a GitHub repository), and you need to confirm that the published figures, tables, or quantitative findings are reproducible.

Use when preparing a fresh system or user account to run MSIGen for mass spectrometry imaging data processing, or when you need to isolate MSIGen installation from other Python projects to avoid dependency conflicts.

Use when when accepting a file path as input in an MSI data processing pipeline and you need to determine which data reader module to instantiate before calling get_image_data() or load_pixels(). This arises when building a multi-vendor instrument workflow (e.

Use when you have mass spectrometry imaging (MSI) data with ion images that need low-dimensional representation learning for downstream tasks like co-localized ion searching or isotope discovery.

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 paired augmented ion images processed through ResNet18 encoders producing 512-dimensional representation vectors, and you need to learn meaningful low-dimensional representations without labeled data by enforcing that augmentations of the same image remain similar while.

Use when you have raw ion images from MSI data and need to train a contrastive encoder to learn stable, mode-specific representations.

Use when you have pairs of augmented ion images from mass spectrometry imaging data and need to generate low-dimensional representation vectors that maximize similarity between augmentations of the same image while avoiding representation collapse.

Use when when exporting quantified ion images and pixel metadata from LipidQMap to HDF5 format for use in downstream Cardinal or other MSI analysis workflows.

Use when you have chemical entity records scattered across two or more public repositories (e.g., HMDB, ChEMBL, PubChem, KEGG) and need a single authoritative, deduplicated knowledge base indexed by a queryable identifier (e.g., m/z value or chemical formula).

Use when when you have conducted batch MS/MS searches across one or more domain-specific MASST tools and need to combine their hit scores, metadata annotations, and taxonomic lineages into a single coherent result set for comparative analysis or publication.

Use when after converting raw Bruker .d/.baf or other proprietary mass spectrometry imaging formats using pyBaf2Sql or ProteoWizard MSConvert, or after running MSIGen's get_image_data() function.

Use when you have two augmented versions of the same ion image (from mass spectrometry imaging data) and need to extract learnable 512-dimensional feature representations using a shared-weight encoder for contrastive loss optimization.

Use when when you need to construct a dual-branch neural network encoder that processes two augmented versions of the same input (e.g., ion images in COL or ISO mode) and must enforce weight sharing between branches to reduce parameters while maintaining separate output representations.

Use when you have raw or preprocessed mass spectrometry feature matrices (e.g., from low mass resolution or sparse acquisition) and want to enhance signal quality and spatial resolution across tissue or single-cell samples.

Use when when performing metabolite identification in mass spectrometry imaging and the metabolites have been chemically derivatized with a known derivatizing matrix (such as FMP-10) that produces ions other than the standard [M+H]+ in positive mode or [M-H]− in negative mode.

Use when you have a derivatizing matrix (e.g., TAHS or other publicly documented reagent) with known composition and ionization behavior that you want to use in Met-ID for metabolite annotation, and the matrix is not yet configured in your Met-ID installation.

Use when when working with mass spectrometry imaging data from metabolites treated with derivatizing matrices (e.

Use when when exporting quantified MSI data as HDF5 containers following the Cardinal::HDF5 layout convention, and you need to establish bidirectional indexing between intensity data (feature-by-pixel matrix) and metadata groups (featureData, pixelData).

Use when when you have deposited mass spectrometry imaging data in NetCDF (CDF) format paired with MATLAB workspace files (.

Use when when a user submits one or more MS/MS spectra and has declared or implied a domain context (microbial, plant, tissue, microbiome, food, or metadata aggregation), and the search must be executed against the appropriate domain-curated spectral library.

Use when your research involves searching MS/MS spectra against multiple curated taxonomic or domain-specific databases (microbial, plant, tissue, microbiome, or food origin) and you need to aggregate, compare, and visualize matching results across all domains in a single interface.

Use when you have 512-dimensional representation vectors output from paired ResNet18 encoders processing augmented ion images, and you need to: (1) introduce an intermediate projection space to enable contrastive loss optimization without trivial/collapsed solutions, (2) further compress learned.

Use when when deploying Galaxy-M or similar multi-component metabolomics platforms that depend on heterogeneous runtime environments (Python, R, MATLAB, WINE) across multiple operating systems (Ubuntu 14.

Use when building a file format dispatcher or initialization routine that must accept user-provided file paths and map them to format-specific processing modules.

Use when when you have preprocessed ST and SM AnnData objects with spatial coordinates and need to establish one-to-one spot correspondence between the two modalities prior to joint analysis.

Use when when you have LC-MS raw data and need to process it through a feature detection and quantification pipeline in KNIME, but lack a structured mapping between sample identifiers, experimental conditions, and the raw LC-MS runs.

Use when after peak alignment across all spectra in an imaging dataset, use this skill when you have detected many peaks but need to reduce false positives and sparse features.

Use when you have loaded a feature-by-pixel intensity matrix from an MSI HDF5 container and need to perform dimension-preserving corrections (e.

Use when after LC-MS feature detection, alignment, and quantification are complete and you have a feature table with m/z and retention time attributes. Use this skill when you have access to a reference list of molecules of interest (e.

Use when after completing feature detection, alignment, and optional filtering (blank subtraction, QC reproducibility, feature occurrence thresholds) in MZmine2 or Optimus, and you need to prepare the feature table and MS/MS spectra for GNPS-based molecular networking, bioassay integration, or.