Claude Skills by HolobiomicsLab

Use when you have an isotope-corrected or raw MSI dataset stored in HDF5 format following Cardinal::HDF5 conventions, a user-provided internal standard definition (sample identifier and/or feature name), and need to locate and extract the intensity row for that lipid before performing ratio-based.

Use when you have spatial metabolomics data with semicolon-delimited isomer name annotations (such as the 'all_IsomerNames' column in SpaMTP Seurat objects) and you need to reduce annotation complexity before pathway analysis, statistical testing, or visualization.

Use when you have spatial metabolomics data with semi-colon-delimited multi-isomer annotations (e.g., 'all_IsomerNames' column in SpaMTP Seurat objects) and you want to quantify the benefit of RefineLipids simplification with lipid_info='simple' parameter.

Use when when you have imported MSI data (imzML or vendor format) loaded into the napari plugin environment and need to organize raw spectral m/z and intensity arrays prior to mean intensity calculation, ROI analysis, or database annotation.

Use when when you have a spatial metabolomics or LC-MS dataset with detected m/z features (as a feature matrix or SpaMTP Seurat object) and need to assign metabolite identities. Specifically: you have observed m/z values, you know the ionization polarity and expected adduct form (e.

Use when processing extracted peak lists from MSI data and you need to annotate matrix-related signals but suspect that multiple ions with the same or very similar m/z values (isobaric ions) are present.

Use when after parsing an imzML XML metadata file and loading the corresponding .ibd binary intensity data, when you need to isolate and visualize the spatial distribution of specific isotopes, chemical species, or mass fragments.

Use when you have paired pre- and post-MALDI microscopy images with visible, non-overlapping ablation marks, MALDI raw data files (.RAW, .UDP, .imzML, .ibd) analyzed by METASPACE, and a segmented cell mask from CellProfiler.

Use when you have extracted latent low-dimensional peak features from imaging mass spectrometry (IMS) data using a graph-attention autoencoder and need to identify a ranked subset of marker ions that represent spatial metabolomic patterns.

Use when when you have a set of candidate molecular formulae for a measured m/z value and need to rank them by how closely their theoretical m/z matches the observed value.

Use when you have a list of detected masses (m/z peaks) from MALDI-MS imaging data and want to systematically search for adduct relationships. Apply this skill when you suspect that observed peaks include not just parent metabolites but also their adducts with matrix ions (e.

Use when after importing MSI data as an msimat object and having a list of detected peak masses, but before annotating which mass differences correspond to biologically plausible adducts.

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 this filter to balance annotation sensitivity against specificity: tighter ppm tolerances (e.

Use when converting mzML files to imzML format for imaging mass spectrometry data and you have a known internal standard (lock mass) whose exact m/z value is available. Use it specifically during the metadata annotation stage (iw_utils.

Use when you have m/z values from spatially-resolved mass spectrometry imaging (e.g., MALDI-MSI, DESI-MSI) and need to assign molecular formulae to thousands of features with higher precision than traditional LC-MS approaches.

Use when you have tabulated pairwise mass differences from a MALDI-MS imaging dataset (via massdiff()) and need to identify which observed mass differences correspond to known chemical adducts (e.g., [M+H]+, [M+Na]+, [M-H2O]+).

Use when you have raw spatial metabolomics imzML files (paired with .ibd binary data files) that need to be loaded into a unified AnnData format for integration with spatial transcriptomics data or for cross-modal spatial pattern identification in single or multiple sample datasets.

Use when you have vendor raw mass spectrometry data files (.raw) from a commercial instrument and need to convert them to open formats (mzML for spectral data, imzML for imaging mass spectrometry) for compatibility with third-party analysis software or to meet open-data standards.

Use when after loading a pixel array (NumPy format) and its associated metadata JSON file from MSIGen, when you need to account for pixel-to-pixel variations in total ion signal or when comparing relative abundances of multiple ions within or across samples.

Use when you have raw or preprocessed single-channel (2D array) or multi-channel (spectral) ion images from mass spectrometry imaging (MSI) data and need to train a contrastive deep learning model.

Use when you have paired .imzML (XML metadata) and .

Use when you have raw MS imaging data in imzML (continuous or processed) or Analyze 7.5 format and need to load it into R for spectral processing, normalization, peak-picking, or statistical analysis.

Use when when you have preprocessed MALDI-MSI data (in msimat format) and want to determine whether abundant peaks are actually molecular adducts of simpler parent ions rather than distinct metabolites.

Use when you have raw or preprocessed MS imaging data archived as an RDS file or from a Zenodo deposit that includes the full m/z feature set (e.g., 10,200 m/z values spanning 150–1000 m/z range) and spectrum count (e.

Use when you have raw or processed MSI data (in imzML or rMSIproc formats) and need to identify and annotate matrix-related peaks before statistical analysis or metabolite identification. Use it specifically when your MSI experiment employed a chemical matrix (e.

Use when you have raw mass spectrometry imaging data (2D or 3D spatial coordinates with full mass-to-charge spectra) and want to train a probabilistic deep learning classifier for tumor delineation or tissue classification.

Use when you have raw line-scan MSI data from a vendor instrument (Agilent, Bruker, Thermo, or open-source .mzML format) and need to extract ion images for specified m/z targets with spatial binning and tolerance-based filtering.

Use when your input mass spectrometry imaging data is in a proprietary vendor format (Bruker .d/.baf, or other binary formats not natively supported by MSIGen) and you need to convert it to an open, readable format (mzML or processed binary) compatible with MSIGen's msigen() function.

Use when when you have a metabolite structure (SMILES or molecular graph) and need to predict its ionization behavior in a mass spectrometry experiment using a specific derivatizing matrix or ionization mode. Use this skill when the expected ions are non-standard (i.

Use when after normalization, smoothing, and baseline reduction have been performed on raw MSImagingArrays data.

Use when you have preprocessed MSI data (peaks already binned and normalized) and need to detect adduct formation patterns across the dataset.

Use when working with imaging mass spectrometry (IMS) datasets where you need to (1) automatically identify marker ions without manual annotation, (2) reduce peak intensity dimensionality while preserving spatial relationships between measurement points, or (3) apply iterative peak picking.

Use when you have preprocessed MSI data (as a CSV intensity matrix or Cardinal MSProcessedImagingExperiment object) and suspect that observed peaks include both parent ions and their adducts formed with matrix or salt species.

Use when after peak alignment across all spectra in an MSImagingExperiment using peakAlign(), when you need to reduce the feature set to high-confidence peaks by removing spurious or low-frequency detections.

Use when after computing all pairwise mass differences from a mass spectrometry imaging dataset, when you need to identify which mass differences correspond to real molecular adducts (e.g., metabolite–matrix or metabolite–salt ions) rather than noise.

Use when when you have raw MS/MS spectra in MGF or mzML/mzXML formats and need to feed them into Casanovo or similar transformer-based de novo sequencing models.

Use when when converting raw line-scan mass spectrometry imaging data (Agilent .d, Bruker .tsf/.baf/.tdf, Thermo .raw, or .mzML formats) and must decide which m/z values from a reference mass list correspond to peaks in the raw spectra.

Use when you have multiple CDF files containing mass spectrometry imaging data (spectra, m/z arrays, and spatial coordinates) that need to be ingested into MATLAB for the DIMPLE pipeline or similar linear-axis mass spectrometry imaging analysis.

Use when when you have mass spectrometry imaging root datasets paired with accompanying .mat workspace files (as in the B73 and Oaxacan Green genotypes from Sama et al.

Use when after rMSIcleanup has classified ions as matrix-related or non-matrix, and you need to audit, validate, or communicate the annotation decisions. Use it when overlapping or isobaric peaks are present in the dataset and you must document misclassification risks per annotation.

Use when after importing imzML or vendor-specific MSI data into napari and visualizing the raw spectral dataset.

Use when you have an MSI intensity matrix (msimat object) and an annotated mass-difference table (massdiff object with known or hypothesized parent–adduct ion pairs) where the number of peak pairs exceeds available RAM.

Use when when building a comprehensive chemical knowledge base for mass spectrometry formula assignment, particularly when you need to link chemical formulae across heterogeneous repositories (HMDB, ChEMBL, PubChem) and connect them through known metabolic transformations to improve annotation.

Use when you have annotated mass-difference peaks with known adduct identities (via mass-matching to reference adduct tables) and possess MSI intensity matrices where each peak's abundance is measured across multiple tissue pixels or voxels.

Use when you have extracted a large feature set of m/z values (hundreds to tens of thousands) from a Cardinal MSImagingExperiment object or similar MS dataset and need to assign putative metabolite identities using public structural databases.

Use when after loading spatial metabolomics data (from CSV, imzML, or merged positive/negative ionization modes) into an AnnData object, and before filtering metabolites or performing cross-modal integration.

Use when you have a measured m/z value from spatially-resolved metabolomics or mass spectrometry imaging and need to assign a molecular formula with high confidence. Use it specifically when you have access to a pre-constructed formula network (KnownSet database) linking 2.

Use when you have raw imzML and ibd (ion binary data) files from spatial mass spectrometry imaging and need to convert them into a standardized AnnData representation where m/z values are features (columns), spatial spots are observations (rows), and intensities form the feature matrix.

Use when you have extended a metabolite identification tool (such as Met-ID) to support a new derivatizing matrix beyond the default (e.

Use when after loading spatial metabolomics data (from CSV, imzML, or merged positive/negative mode files) into an AnnData object and before filtering or integrating with spatial transcriptomics.