Claude Skills by HolobiomicsLab

Use when you have two LC-MS feature tables (each with m/z, retention time, and intensity columns) from separate metabolomic experiments or replicates, and you need to establish which features in dataset A correspond to which features in dataset B to enable comparative or longitudinal analysis.

Use when after applying the CV_ratio() filtering function to a normalized metabolomic feature matrix (e.g., Urine_RP_NEG_norm.txt) in margheRita, generate retention statistics to report how many features passed the threshold (CV ratio > 1.0) and characterize the distribution of retained CV ratios.

Use when when you have a normalized and batch-corrected feature abundance matrix from non-targeted LC-MS/MS metabolomics (e.

Use when when you have LC-MS data (mzML or netCDF format) and a pre-defined list of target metabolites (m/z, retention time, and identifiers) that you wish to extract and quantify across multiple samples, rather than performing untargeted feature discovery.

Use when after feature detection (e.g., Asari processing of mzML files to feature tables) but before normalization, batch correction, or annotation.

Use when after feature detection and peak alignment have produced a feature table with zero or missing values across samples.

Use when after quality control, filtering, and normalization of an MS-DIAL-derived feature abundance matrix (e.g., Urine_RP_NEG_norm.txt or Urine_RP_POS_norm.txt), when you have samples assigned to discrete experimental classes (e.

Use when you have a metabolomics feature table (rows=features, columns=samples) generated from LC-MS or GC-MS preprocessing and need to identify which features contain systematic errors from peak integration or alignment.

Use when when you have preprocessed LC-MS/MS data (MGF file with MS1 and MS2 spectra and a feature abundance table from MZmine2 or similar peak detection tool) and need to perform chemical phylogeny-based diversity analyses or meta-analyses comparing metabolomic profiles across multiple samples or.

Use when after completing feature annotation and reaction assignment in an untargeted metabolomics workflow, specifically when you have a feature-by-sample intensity matrix aligned with metabolite identities and want to communicate cluster structure, reaction pathway groupings, and feature.

Use when when you have untargeted LC-MS/MS spectral data from microbial or environmental samples and aim to group related metabolites into molecular families for natural product discovery, especially when integrating with genomic biosynthetic gene cluster (BGC) annotations to link chemistry to.

Use when you have raw untargeted LC/MS data in open mzML or mzXML format and need to extract a quantified feature matrix (m/z and retention time coordinates with sample intensities) without prior knowledge of optimal signal detection parameters, batch effects, or quality control samples.

Use when when you have raw or GNPS-processed MS2 spectral data from microbial strains and need to organize spectra into molecular families (grouped by spectral similarity) while preserving strain provenance, as a prerequisite for linking metabolomic families to gene cluster families (GCFs) via.

Use when you need to create realistic, diverse chemical populations for simulating LC-MS/MS acquisition strategies in a virtual environment. It is essential when you lack real metabolomics data but want to prototype and compare fragmentation strategies (e.

Use when you have a preprocessed metabolomics feature matrix (expression matrix with metabolite abundances as columns and samples as rows) with corresponding binary or multi-class sample labels, and you need to train and compare classifier performance to select the -performing model for disease.

Use when you have raw LC-MS data in mzML or equivalent binary format from a public repository (MetaboLights, MassIVE) or instrument vendor output, and need to ingest it into MetaboAnalystR 4.0 for unified LC-MS1 feature detection and MS/MS spectra processing.

Use when you have a tab-delimited metabolomics data file (raw measurement output from xcms, Sciex OS, or similar acquisition pipelines) and need to load it into mzQuality before building a SummarizedExperiment.

Use when after running feature clustering (Gravity) or drift correction (Blueshift) on LCMS data, when you have a processed feature table and need to standardize its structure, validate metadata completeness, enforce missing-value thresholds, and generate a QC report documenting pass/fail status.

Use when when importing a tab-delimited or Sciex OS text export metabolomics dataset into mzQuality, before building the SummarizedExperiment object.

Use when you have measured intracellular metabolite concentrations (e.g., via LC–MS/MS) across multiple cell lines or samples and want to predict which metabolic reactions are substrate-limited versus transcriptionally regulated.

Use when you have untargeted LC-MS metabolomics data preprocessed with XCMS and need to filter out low-quality peak integrations that could introduce false positives or noise into metabolite quantification.

Use when after completing feature annotation with the annotateRC function on LC–MS All-ion fragmentation (AIF) datasets, when you need to persist ranked metabolite candidates, matched ion spectra, and global summary tables to disk for archival, manual review, or integration into downstream.

Use when you have raw LC/HRMS data files in mzXML, mzML, or netCDF format and need to identify individual and aggregated aligned peaks with their retention time and m/z values before applying spectral deconvolution or chemical annotation. This is the obligatory first step when using IDSL.

Use when after consolidating aligned LC-MS peaks into a quantitative feature table (with m/z, retention time, and intensity values across all samples), and before proceeding to statistical analysis or functional interpretation.

Use when you have a Sciex Multiquant (≥v3.0.3) txt export containing QCpool sample measurements at multiple timepoints within a sequence, and you need to flag compounds with high technical variability or signal degradation before proceeding to statistical analysis or interpretation of.

Use when you have a raw MGF file containing fragmented LC-MS-MS metabolomics spectra and want to apply Latent Dirichlet Allocation (LDA) to discover hidden topics (molecular families, biochemical patterns) across your sample set.

Use when after you have detected LC-MS features, grouped them into empirical compounds via isotope and adduct clustering (using khipu), and have accurate m/z and retention time values.

Use when you are beginning a non-targeted metabolomics analysis and need to source raw LC-MS/MS data files (in mzML or NetCDF format) that have been vetted for quality and are known to support FBMN and statistical analysis.

Use when when converting raw metabolomics data from external formats (tab-delimited text, Sciex OS exports) into a unified R analysis environment, or when you have an existing SummarizedExperiment from another pipeline (e.

Use when you have raw LC-MS data (mzXML format or pre-computed feature tables from external software) and need to: (1) detect both Gaussian and non-Gaussian shaped metabolic features across multiple samples, (2) align these features across samples, and (3) export EIC chromatograms with m/z.

Use when after XCMS peak picking and fillPeaks() when you have xcmsEIC and filled xcmsSet objects and need to systematically flag low-quality or unreliable peak integrations prior to statistical modeling or machine learning classification.

Use when after peak detection and feature table construction (rows = features, columns = samples with intensity values) and before applying intensity-based filters (e.g., fold-change, phenotype score) or when preparing data for dashboard visualization.

Use when you have an aligned MemoMatrix (sample-by-feature occurrence matrix where features are MS2 peaks and neutral losses) and need to remove background noise before applying visualization or clustering techniques (MDS/PCoA, TMAP, Heatmap).

Use when when you have raw profile LC-MS data in .mzML format and need to prepare regions of interest (ROI) as input for a CNN-Transformer peak detection network.

Use when after drift correction and before missing value imputation when your LC-MS peak table contains features with variable detection rates across samples.

Use when you have fitted an MB-PLS model on multi-assay LC-MS intensity data (e.g., HPOS, LPOS, LNEG), computed MB-VIP scores for all features, and need to identify which features are statistically significant for your phenotypic outcome.

Use when you have a raw feature table (TSV/CSV) derived from LC-MS peak detection (e.

Use when when you have a raw LC-MS peak table imported from vendor software (e.

Use when when you have a feature intensity table (samples × compounds) from targeted or non-targeted metabolomics and need to prepare it for statistical modeling or multivariate analysis.

Use when a Python-based metabolomics analysis package has been relocated to a new GitHub organization (e.g., metabolomics-cloud) and you need to confirm that the migration preserved package integrity, installation, and runtime correctness.

Use when your metabolomics dataset (LC/MS or GC/MS) contains missing values encoded as NA or zero that represent compounds below the instrument's limit of detection (LOD) or limit of quantification (LOQ), rather than values missing completely at random.

Use when your input is a raw metabolomics intensity matrix (compounds × samples) with known batch assignment and QC sample labels, and you observe signal drift across the analytical sequence or batch-to-batch variation.

Use when when you have raw LC-MS metabolomics data from multiple disease groups (e.g., .mzML or .npy format files) that must be converted into a uniform, normalized feature representation before training a deep learning classifier to distinguish disease states.

Use when you have a raw metabolomics abundance table (e.g., LC/MS or GC/MS peak intensities or concentrations) with non-normal distributions and missing values, and you need to prepare it for Gibbs sampler or other model-based imputation.

Use when you have trained multiple machine learning classifiers (e.g., AdaBoost, SVM, Random Forest) on the same metabolomics peak-quality training set using k-fold cross-validation with repeated runs (e.

Use when when benchmarking or validating a pathway analysis method (such as PALS, ORA, or GSEA) on metabolomics data, you need quantitative evidence that the method's pathway rankings remain stable despite noise and missing peaks—conditions prevalent in real LC-MS/MS datasets.

Use when you have completed NPP runs from one or more metabolomics tools on a set of LC-HRMS mzML files AND you have reference information (target molecule list with molecular formula, main adduct, and RT boundaries) available for a subset of expected compounds in those files.

Use when when you have raw peak intensity matrices from metabolomics LC-MS/MS experiments with zero values (missing peaks or undetected compounds) and need to prepare data for pathway-level analysis using PLAGE, ORA, or GSEA.

Use when when preparing to process raw LC-HRMS metabolomics data (.mzML or .abf files) with MS-DIAL within a Nextflow pipeline, before executing peak detection and chromatogram alignment.

Use when you have raw peak tables exported from a tandem mass spectrometry preprocessing tool (e.g. Progenesis, MS-DIAL, or Bruker Metaboscape) and need to integrate them with sample metadata for reproducibility filtering, mispicked-ion removal, or group-based feature exclusion.