Claude Skills by HolobiomicsLab

Use when you have deposited spatio-molecular matrices (e.g., MORPHnMOL.csv from SpaceM analysis) and need to reproduce or extend the data transformations, feature extractions, and figure generation reported in a metabolomics manuscript.

Use when you have acquired raw SIMS (secondary ion mass spectrometry) metabolite images aligned with tissue regions and segmented single-cell masks, and you need to extract normalized metabolite intensity values per cell before performing cell-type assignment, VAE embedding, or protein–metabolite.

Use when you have collected MS/MS spectra from a microbial sample (pure culture, environmental isolate, or mixed community) and need to assign chemical identities to observed m/z features while simultaneously resolving which microbial taxa are likely producers of each metabolite.

Use when after successfully training a spectrum prediction model (FFN encoder, GNN encoder, intensity predictor, or fragment generator) to completion or at intermediate milestones, and before using that model for inference on test sets, structural elucidation queries, or transfer learning.

Use when after training a MEISTER deep learning reconstruction model on mass spectrometry data, you must validate performance on independent test sets before applying the model to new experimental or clinical samples.

Use when you have mass spectrometry imaging data in multiple vendor or open-source formats and need to programmatically route each file to its correct processing handler based on file extension, avoiding hardcoded conditional chains and enabling extensible format support.

Use when when you have native metabolite structures (SMILES or mol format) and need to predict adduct ions for a mass spectrometry imaging experiment that uses a derivatizing matrix known to produce ions other than common [M+H]+ (positive mode) or [M-H]- (negative mode).

Use when when you have metabolite structures (as SMILES strings) and need to predict their observable m/z ions under non-standard ionization conditions imposed by a derivatizing matrix reagent (such as FMP-10).

Use when you have two or more spatial omics datasets (e.

Use when after GNPS spectral library search has returned matched chemical annotations (with m/z values and cosine similarity scores) for MS/MS spectra.

Use when you have imported raw imzML or Analyze 7.5 imaging data and need to represent it as a preprocessed spectral array before applying statistical methods (PCA, clustering, classification).

Use when your MSI data is stored in a Cardinal imaging experiment object (version 2.2+) that has already been peak-binned with peakBin(), and you want to run mass2adduct's massdiff() and adductMatch() pipeline without manually exporting to CSV;

Use when you have MSI intensity data exported from MSiReader or SCiLS software as a plain-text CSV file (with peaks as columns and pixels/spots as rows), and you need to import it into R to perform pairwise mass difference calculations and adduct identification.

Use when you have MSI intensity data exported from MSiReader, SCiLS, or Cardinal as plain-text CSV files or as Cardinal MSProcessedImagingExperiment/MSContinuousImagingExperiment objects, and need to load it into R as a validated msimat object for mass difference and adduct analysis.

Use when when you need to validate that MSI software (e.g., LipidQMap) achieves documented processing speeds on your target hardware, or when you need to establish a performance baseline before deploying the software for high-throughput imaging studies.

Use when after calculating mean intensity values across all spectra in an MSI dataset (or within a manually selected ROI), and you need to store the resulting m/z–intensity table in a portable format for downstream ROI analysis, database annotation, or external statistical pipelines.

Use when when you have imzML-format MSI data and need to identify biochemical species in specific tissue regions or anatomical structures.

Use when you are converting a processed Cardinal MSImagingExperiment object (containing normalized peaks, optional spatial shrunken centroids segmentation, and feature m/z annotations) into a Seurat object for downstream pathway analysis, differential metabolite expression, or integrative.

Use when when loading mass spectrometry imaging data from imzML or Analyze 7.

Use when you have loaded a feature-by-pixel intensity matrix (HDF5 format following Cardinal::HDF5 layout) from imzML MSI data in positive ion mode and you have identified paired [M+H]+ and [M+Na]+ features for the same neutral lipid species (differing by ~22 Da in m/z).

Use when after isotope correction when you have extracted intensity matrices from imzML or HDF5 MSI data and need to convert raw or isotope-corrected ion-image intensities into quantified values using a known internal standard lipid species.

Use when after generating a 3D pixel array (shape n+1, y, x) from raw line-scan MSI data via MSIGen's get_image_data() call, curate and validate the accompanying JSON metadata file before visualization or downstream analysis.

Use when you have processed and quantified MSI data from one or more imzML files in LipidQMap and need to export them as a unified, standards-compliant HDF5 container that preserves feature-by-pixel intensity matrices, per-feature lipid annotations (m/z, lipid class, adduct, neutral ID), per-pixel.

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 (AgLDI) experiments, and you need to computationally distinguish matrix ions from analyte ions before downstream feature.

Use when you have MSI intensity data exported from commercial software (MSiReader, SCiLS) or Cardinal processing pipelines as plain-text CSV files or Cardinal MSProcessedImagingExperiment/MSContinuousImagingExperiment objects, and you need to prepare it for mass-difference tabulation and adduct.

Use when you have raw line-scan MSI data (from Bruker .d/.baf, converted to .mzML, or other supported formats) and need to configure MSIGen before calling msigen() to generate image arrays.

Use when use this skill at the start of any MSI analysis workflow when you have raw imzML files or vendor-specific MSI data that need to be loaded into napari-MSI-Explorer for visualization, mean intensity calculation, ROI analysis, or annotation.

Use when when you have loaded mass spectrometry imaging data into a MSImagingArrays object in Cardinal 3.6 and need to explore the effects of multiple preprocessing steps (e.

Use when after reading an imzML file (continuous or processed format) using readMSIData() and before proceeding to preprocessing or statistical analysis steps. Use this skill whenever you need to confirm that a parsed imaging dataset meets expected structural requirements—e.

Use when you have raw or downloaded MSMS spectra (from online resources via Universal Spectrum Identifier, or from local mzML/mzXML files) that require standardization, cleaning, and fragment assignment before visualization, statistical comparison, or machine-learning feature extraction.

Use when after performing spot_align_byknn to map SM spots to ST spots, validate that all SM spots have been successfully assigned and that the Euclidean distance distributions between SM spots and their assigned nearest ST neighbors are reasonable (no outliers or failed assignments that would.

Use when you have preprocessed and jointly normalized ST and SM data from the same sample(s) aligned to a common spatial resolution (via joint_adata_sm_st and normalize_total_joint_adata_sm_st), and you need to discover shared latent structure across modalities to identify co-localized spatial.

Use when when you have paired spatial transcriptome and metabolome datasets in h5ad format with spatial coordinate matrices (obsm['spatial']) and you need to establish spot-level correspondence across modalities for downstream integration or co-analysis.

Use when when you have an observed m/z value from mass spectrometry imaging and need to annotate it with a ranked list of candidate chemical formulae. Apply this skill when the KnownSet database (2.

Use when when you have computed raw p-values for multiple independent statistical tests (e.g., Pearson correlation tests across all pairwise ion combinations in MSI data) and need to report which results remain significant after accounting for multiple comparisons.

Use when you have mzML files generated from raw vendor mass spectrometry imaging data and need to create imzML output compatible with software like Cardinal MSI, METASPACE, M2aia, or SCiLS Lab.

Use when when processing mass spectrometry imaging (MSI) data in positive ion mode where both [M+H]+ and [M+Na]+ adducts are present for the same lipid species, and you observe intensity overlap in [M+H]+ ion images caused by the isotopic fine structure of [M+Na]+ adducts.

Use when when you have m/z values from spatially-resolved mass spectrometry imaging (MSI) and need to predict their molecular formulae with high precision.

Use when when you have preprocessed joint ST/SM AnnData objects (output from joint_adata_sm_st and normalize_total_joint_adata_sm_st) and need to fit a ConditionalVAESTSM model to unify spatial transcriptomics and spatial metabolomics data to a common resolution.

Use when when you have 512-dimensional (or other fixed-size) representation vectors output from paired encoders processing augmented versions of the same input (e.

Use when after normalizing, smoothing, and baseline-reducing mass spectra via normalize(), smooth(), and reduceBaseline(), when you need to distinguish true peaks from noise-induced artifacts.

Use when you have raw LC-MS data in vendor or mzML format and need to systematically discover and extract all detectable metabolite features across the full retention time range, without predefined target lists.

Use when when you have access to a peer-reviewed manuscript with an accompanying interactive notebook and public data repository, and you need to verify that the published figures can be regenerated from the original data through the documented processing pipeline, or when you want to reuse the.

Use when when working with Bruker .d/.baf mass spectrometry imaging data and needing to feed it into MSIGen or other open-source MSI processing pipelines.

Use when when annotating matrix-related ions in MSI datasets where two or more ions share identical or near-identical m/z values (isobaric ions) or exhibit overlapping spatial distributions across imaging pixels.

Use when annotating matrix-related signals in MSI datasets where chemical formulas or spatial distributions alone are ambiguous, or when multiple ions share nominal m/z values (isobaric peaks).

Use when after loading preprocessed MSI intensity data (via msimat from CSV export) or a simple numeric vector of mass peak values, when you need to discover which masses in your dataset co-vary as parent–adduct pairs.

Use when when processing paired augmented versions of the same input (e.g., two augmented ion images in COL or ISO mode) and you need to learn meaningful low-dimensional representations via contrastive loss.

Use when you have extracted a peak list from MSI data and need to annotate matrix-related signals, but overlapping peaks or isobaric ions (ions with identical or near-identical m/z values) risk being misclassified.

Use when after peak picking and alignment have been performed on MSImagingArrays data (via peakPick() and peakProcess()), apply this skill when you need to remove noise-driven or low-frequency peaks before summarizing reference peaks across the full imaging dataset.