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HolobiomicsLab

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1. ▌
   Metabolights Dataset Retrieval 2 · holobiomicslab

   Use when you have a MetaboLights dataset identifier (e.g., MTBLS1124) and need to download a specific mzML file (e.g., QC07.mzML) from the public repository for visualization, quality control assessment, or integration into a metabolomics workflow. The USI format mzspec:MTBLS1124:QC07.
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   Mnar Data Handling 2 · holobiomicslab

   Use when you have metabolomics data (targeted LC/MS or untargeted GC/MS) with left-censored missing values below the limit of quantification (LOQ) or limit of detection (LOD), and you need to impute these values while preserving the underlying distributional structure and avoiding bias from.
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   Mzml Mzxml Parsing 2 · holobiomicslab

   Use when you have raw LC-MS/MS data in mzML or mzXML format and need to isolate specific MS1/MS2 scan pairs for a targeted compound list or for building a local spectral library.
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   Usi String Parsing 2 · holobiomicslab

   Use when when you have a USI string (e.g., mzspec:GNPS:TASK-d93bdbb5cdda40e48975e6e18a45c3ce-f.mwang87/data/Yao_Streptomyces/roseosporus/0518_s_BuOH.
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5. ▌
   R Package API Usage 2 · holobiomicslab

   Use when you have tabular metabolomics data (tab-delimited or Sciex OS format) and need to apply a specialized R package's analysis pipeline—such as mzQuality—that requires sequential function calls (readData → buildExperiment → doAnalysis) to construct, validate, and transform experiment objects.
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6. ▌
   Evidence Linking 2 · holobiomicslab

   Use when when evaluating a scientific manuscript that describes a computational analysis (e.g., a normalization or transformation pipeline), use this skill to trace each claimed finding to its source code, data, or prior publication.
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7. ▌
   Peak List Formatting 2 · holobiomicslab

   Use when after successfully resolving a USI string to a specific mass spectrum scan, and before performing spectral matching, library search, or comparative analysis.
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8. ▌
   Status Value Parsing 2 · holobiomicslab

   Use when when a project README or documentation embeds badge endpoints that report real-time status (e.g., Travis CI build, Landscape.
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9. ▌
   JSON Response Parsing 2 · holobiomicslab

   Use when when querying a TensorFlow Serving metadata endpoint or similar REST API that returns JSON-formatted model metadata, and you need to programmatically extract and validate input/output layer names against known specifications (e.
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10. ▌
    Ion Mobility Calibration Curve Fitting · holobiomicslab

    Use when you have TWIM-MS experimental data with arrival times and m/z values, and access to calibrant reference standards with known CCS values (typically loaded from a calibration template).
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11. ▌
    Ion Mobility Machine Learning Training · holobiomicslab

    Use when you have a curated dataset of molecular structures (or molecular descriptors) paired with experimentally measured or reference collision cross section values, and you need to predict CCS values for a set of ≤10,000 novel molecules to filter or prioritize metabolomics identifications.
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12. ▌
    Isotope Pattern Matching And Filtering · holobiomicslab

    Use when you have high-resolution centroided mzML files from Orbitrap or similar instruments, a target compound list with known formulas and monoisotopic m/z values, and need to quantify isotopologue abundances (M+0, M+1, M+2, etc.) for stable isotope labeling experiments.
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13. ▌
    JSON Serialization Of Query Structures · holobiomicslab

    Use when you have parsed a MassQL query string into an AST representation and need to store, validate, transmit, or integrate the query structure with other tools or systems.
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14. ▌
    Latent Group Discovery From Omics Data · holobiomicslab

    Use when you have a preprocessed metabolomics feature matrix and sample metadata but suspect hidden substructures or unknown groupings that are not explained by known experimental conditions.
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15. ▌
    Lipid Abundance Statistical Comparison · holobiomicslab

    Use when after lipid matching is complete and you have a table of normalized lipid abundances aligned across samples, grouped into distinct experimental conditions or phenotypic categories (e.g., diseased vs. control, treated vs. untreated).
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16. ▌
    Lipid Expression Pattern Visualization · holobiomicslab

    Use when after statistical analysis of lipid abundance data has produced a table of lipid identities, expression measurements, p-values, fold-changes, and condition labels.
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17. ▌
    Local Sequence Availability Assessment · holobiomicslab

    Use when you need to determine which sequence files in a repository like MIBiG are unique to that resource and not mirrored in NCBI public databases.
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18. ▌
    M Z Value Clustering Mass Spectrometry · holobiomicslab

    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.
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19. ▌
    Machine Learning Regulatory Prediction · holobiomicslab

    Use when you have matched multiomics data (CNV, mutations, DNA methylation, histone PTMs, transcriptomics, miRNA, lncRNA, proteomics, phosphoproteomics) and metabolomics measurements across a cell line panel or cohort, and you want to infer which molecular features (genes, regulatory marks, splice.
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20. ▌
    Mass Spectrometry Image Reconstruction · holobiomicslab

    Use when you have paired .imzML (XML metadata) and .
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21. ▌
    Mass Track Correspondence Verification · holobiomicslab

    Use when after constructing a LOWESS regression function (rt_cal_dict) to align retention times between a reference sample and a current sample, validate that high-selectivity landmark peaks in the reference sample (mSelectivity > 0.
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22. ▌
    Matrix Algebra For Metabolite Networks · holobiomicslab

    Use when you have normalized and standardized metabolomics data (samples × metabolites matrix) and need to move beyond univariate or pairwise metabolite analysis to characterize the joint covariance structure or infer dynamic control relationships between metabolites under different growth.
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23. ▌
    Metabolic Model Constraint Application · holobiomicslab

    Use when when you have a generic constraint-based metabolic model, RNA-seq expression data with GPR associations for reactions, experimentally measured nutrient uptake/secretion rates (from YSI bioanalyzer or similar), and intracellular/extracellular metabolomics data, and you need to generate.
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24. ▌
    Metabolite Distance Metric Calculation · holobiomicslab

    Use when you have normalized peak intensity tables from FT-ICR MS data (or MetaboDirect pre-processed .csv output) with samples grouped by experimental treatments (e.
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25. ▌
    Metabolite Feature Matrix Manipulation · holobiomicslab

    Use when you have raw metabolomics peak intensity or concentration data in matrix form (samples as rows, metabolites as columns) and need to prepare it for normalization or statistical analysis.
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26. ▌
    Metabolite Intensity Matrix Extraction · holobiomicslab

    Use when when you have raw mass spectrometry peak intensity data (rows = peaks with IDs, columns = individual samples) and you need to align it with metabolite annotations (peak ID → KEGG or ChEBI compound ID mappings) before performing pathway-level analysis.
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27. ▌
    Metabolite Network Covariance Analysis · holobiomicslab

    Use when you have normalized metabolite abundance data from MetaboAnalyst or similar preprocessing and need to transition from univariate/pairwise correlation analysis to network-level inference that captures conditional dependencies (partial correlations) between metabolites under specific.
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28. ▌
    Metabolite Network Topology Extraction · holobiomicslab

    Use when after computing a Jacobian matrix from metabolomics covariance data, when you need to identify and visualize the structure of metabolite interactions (which metabolites regulate or influence which others) and their relative strengths.
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29. ▌
    Metabolite Pathway Association Mapping · holobiomicslab

    Use when you have a list of metabolite names or identifiers detected in your samples and need to assign them to known metabolic pathways before computing pathway dysregulation scores, performing pathway-level machine learning, or conducting metabolite-pathway regression analysis.
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30. ▌
    Metabolomics Feature Matrix Processing · holobiomicslab

    Use when you have a raw metabolomics intensity matrix with missing or zero values across samples in different experimental groups, and you need to input it into pathway activity scoring methods (PLAGE, ORA, GSEA) or metabolite set analysis pipelines that require normalized, zero-mean unit-variance.
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31. ▌
    Metabolomics Workbench API Integration · holobiomicslab

    Use when when you need to analyze a publicly archived lipidomics study (identified by study_id like ST001111) and want to bypass manual data download and format conversion.
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32. ▌
    Metadata Validation Rule Specification · holobiomicslab

    Use when mSMetaEnhancer retrieves metadata attributes (SMILES, InChI, CAS numbers, IUPAC names, formulas) from external services (CIR, CTS, PubChem, IDSM, BridgeDb) and you need to guarantee that only correctly formatted values are written back to .msp output files.
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33. ▌
    Microbe Metabolite Attribution Scoring · holobiomicslab

    Use when after training multi-layer perceptron neural networks via cross-validation to predict metabolite abundances from microbiome composition, when you need to interpret which microbes drive predictions of specific metabolites and to identify groups of microbes with coherent metabolite.
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34. ▌
    Microbial Biotransformation Prediction · holobiomicslab

    Use when when you have chemical structures (SMILES or molecular structure format) and need to forecast what metabolites environmental microbes would produce; particularly when assessing pollutant persistence, environmental fate, or xenobiotic degradation pathways in aquatic and terrestrial settings.
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35. ▌
    Model Parameter Inspection And Logging · holobiomicslab

    Use when after instantiating a neural network model (such as TransG-Net) with multimodal inputs but before beginning training, to validate that the model architecture correctly accepts graph features and SMILES embeddings as separate modalities and produces expected output tensor shapes.
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36. ▌
    Module Trait Association Visualization · holobiomicslab

    Use when after identifying and naming metabolic correlation modules from WGCNA on normalized, imputed metabolomic data, and you need to assess whether specific modules associate significantly with a binary or categorical sample trait (e.g., disease status, treatment group).
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37. ▌
    Molecular Graph Feature Representation · holobiomicslab

    Use when when you have molecular structures (as SMILES, SDF, or graph adjacency) and need to predict continuous properties (e.
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38. ▌
    Molecular Structure To Vector Encoding · holobiomicslab

    Use when when you have a collection of molecular structures (SMILES, InChI, or SDF format) and need to train or compare neural network-based spectrum predictors (such as NEIMS, MassFormer, or ICEBERG variants) on the same benchmark dataset.
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39. ▌
    Multi Task Auxiliary Target Generation · holobiomicslab

    Use when training neural network models (MLP or GNN) for metabolite annotation on mass spectrometry data and you have access to a large unlabeled or weakly labeled spectral dataset.
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40. ▌
    Multiassay Data Structure Construction · holobiomicslab

    Use when you have three separate data components from a metabolomics assay: (1) a matrix of metabolite measurements (assay) with samples as columns and metabolites as rows, (2) a table of metabolite annotations (rowData, e.
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41. ▌
    Multimodal Spot Correspondence Mapping · holobiomicslab

    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.
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42. ▌
    Multivariate Lipid Metabolite Analysis · holobiomicslab

    Use when you have integrated, normalized lipidomic and metabolomic feature tables from the Multi-ABLE method or similar concurrent multiomics workflows, with matched sample phenotypes (e.
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43. ▌
    Mzml File Random Access By Spectrum Id · holobiomicslab

    Use when you have a compressed mzML file (mzML.gz or indexed gzip format) and need to extract a single spectrum or a small subset of spectra by their known numeric identifiers, rather than iterating through the entire file sequentially.
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44. ▌
    Node Coordinate Assignment Computation · holobiomicslab

    Use when you have constructed a network object (edges and nodes) in MetaNet and need to compute spatial coordinates for visualization. Use this skill when preparing networks for static plots (e.
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45. ▌
    Notebook Execution And Reproducibility · holobiomicslab

    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.
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46. ▌
    Object Oriented Class Hierarchy Design · holobiomicslab

    Use when when you need to create a plotting or visualization framework that must support multiple plot kinds (spectrum, chromatogram, mobilogram, peakmap) each backed by multiple rendering engines (matplotlib, bokeh, plotly), and you want to avoid combinatorial explosion of concrete classes while.
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47. ▌
    Open Modification Mass Shift Detection · holobiomicslab

    Use when query mass spectra do not confidently match unmodified peptides in the spectral library, or when you suspect the sample contains unknown or unexpected post-translational modifications.
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48. ▌
    Outlier Detection Removal Metabolomics · holobiomicslab

    Use when after kNN imputation of metabolite measurements but before variance-stabilizing normalization, when you have a MultiAssayExperiment object with potentially problematic samples.
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49. ▌
    Outlier Sample Removal Quality Control · holobiomicslab

    Use when visual inspection (PCA plots, TIC plots, or boxplots) reveals samples with aberrant lipid abundance profiles, or when domain knowledge suggests specific samples are technical replicates, biological outliers, or failed QC metrics.
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50. ▌
    Oxidized Lipid Nomenclature Conversion · holobiomicslab

    Use when you have lipid identifiers from multiple sources (e.
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51. ▌
    Package Version Pinning And Lock Files · holobiomicslab

    Use when when a multi-language analysis pipeline (R + Python) has been validated and you need to document the exact dependency tree so that other researchers or systems can recreate the same computational environment.
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52. ▌
    Pathway Metabolite Mapping Integration · holobiomicslab

    Use when when you have metabolite-level summary statistics (p-values, log2 fold changes) from differential analysis and need to test whether specific metabolic pathways are significantly enriched in your dataset.
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53. ▌
    Percentile Threshold Determination Scc · holobiomicslab

    Use when when you have paired microbiome and metabolome data and need to identify which metabolites are significantly well-predicted by microbes, but the relationship between prediction accuracy and biological relevance is unknown or varies across datasets (e.g., IBD PRISM: 0.136;
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54. ▌
    Pixel Binning And Spatial Registration · holobiomicslab

    Use when you have raw line-scan mass spectrometry imaging data from nano-DESI or other line-scan acquisition modes and need to produce a georeferenced 3D pixel array.
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55. ▌
    Pytorch Distributed Training Execution · holobiomicslab

    Use when when you have a pre-trained GNN model checkpoint and need to apply transfer learning to a new chromatography or molecular property prediction dataset (e.g., Eawag_XBridgeC18_364.xlsx) by fine-tuning the model weights on domain-specific examples without retraining from scratch.
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56. ▌
    Pytorch Or Tensorflow Model Definition · holobiomicslab

    Use when when you have molecular structure inputs (SMILES strings, molecular graphs, or feature vectors) and need to predict a continuous molecular property (e.g., CCS values, retention time, ionization efficiency).
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57. ▌
    Quality Control Threshold Optimization · holobiomicslab

    Use when when you have extracted a peak feature table (CSV or tabular format) with mass-to-charge ratios, retention times, and intensity values across multiple samples, and you need to distinguish genuine differential metabolic signals from instrumental noise or low-abundance background before.
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58. ▌
    R Environment Dependency Specification · holobiomicslab

    Use when when developing or reproducing an R-based analysis that integrates Python dependencies (e.g., via reticulate), particularly in contexts where deep learning (keras) or complex machine-learning frameworks (caret) depend on specific numpy versions or Python minor releases.
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59. ▌
    Reference Feature Combination Strategy · holobiomicslab

    Use when when you have isolated, high-confidence reference chromatographic peaks (ground-truth) from reference LC-HRMS chromatograms that have been matched across multiple samples, and you need to train a CNN model to detect peaks in new chromatograms but lack sufficient labelled instances.
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60. ▌
    Round Trip Data Integrity Verification · holobiomicslab

    Use when you have implemented or are validating a reader/writer library for a mass spectrometry file format (such as mzPeak, mzML, or similar), and need to confirm that data parsed from disk can be written back without loss of information.
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61. ▌
    Ruv Iii Implementation In Metabolomics · holobiomicslab

    Use when you have metabolomics data distributed across multiple experimental batches, each batch contains sample replicates (the same sample measured multiple times within and across batches), PCA or visual inspection reveals systematic batch effects, and you need to distinguish unwanted variation.
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62. ▌
    Script Repository Workflow Integration · holobiomicslab

    Use when you have cloned or downloaded scripts from a development repository (e.g., github.com/InnovativeOmics/Core-Match) and need to incorporate edits into an installed distribution copy of LipidMatch-4.2 or FluoroMatch.
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63. ▌
    Signal Processing Prominence Filtering · holobiomicslab

    Use when when you have a 1D intensity array from a mass spectrum (m/z or retention time dimension) and need to identify and rank local maxima that are biochemically meaningful rather than noise-driven.
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64. ▌
    Spatial Coordinate Metadata Extraction · holobiomicslab

    Use when you have raw imzML files (paired with .ibd binary data) from imaging mass spectrometry experiments and need to construct an AnnData object for spatial metabolomics analysis.
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65. ▌
    Spatial Data Integration Preprocessing · holobiomicslab

    Use when when you have paired spatial transcriptome and metabolome datasets (in .h5ad or matrix format) with spatial location information, and you need to establish correspondence between features across modalities before performing spatial morphological alignment.
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66. ▌
    Spectral Data Preprocessing Lipidomics · holobiomicslab

    Use when you have raw lipidomic and metabolomic spectral data files from a Multi-ABLE barocycler-based concurrent multiomics experiment and need to normalize ion intensities, align retention times and m/z values across samples, and remove noise or low-signal features before statistical comparison.
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67. ▌
    Spectral Peak Picking Derivative Based · holobiomicslab

    Use when you have preprocessed MSImagingArrays objects (normalized via normalize(), smoothed via smooth(), and baseline-reduced via reduceBaseline()) and need to identify discrete peaks across all spectra in a mass spectrometry imaging dataset.
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68. ▌
    Spectral Search Performance Evaluation · holobiomicslab

    Use when when you have implemented or obtained a spectral library search tool (e.
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69. ▌
    Spectral Similarity Metric Computation · holobiomicslab

    Use when after clustering peak networks from INADEQUATE spectra and before filtering matches to retain high-confidence metabolite assignments.
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70. ▌
    Spectrum Object Instantiation From XML · holobiomicslab

    Use when when you have parsed XML elements from an mzML or mzML.gz file (via ElementTree or a similar XML parser) and need to convert those elements into pymzML Spectrum objects for spectrum-level operations such as random access, spectral comparison, or data extraction.
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71. ▌
    Stable Isotope Labeling Quantification · holobiomicslab

    Use when you have centroided high-resolution Orbitrap mzML files from stable isotope labeling experiments and need to measure isotopologue abundances (M+0, M+1, M+2, etc.) for a defined list of target compounds with 13C or other isotopic labels.
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72. ▌
    Statistical Hypothesis Testing Biology · holobiomicslab

    Use when you have a metabolite abundance table (rows=metabolites, columns=samples) from Metabolomics Workbench format and need to test whether specific metabolites or metabolite classes are significantly enriched in particular biological pathways or conditions, beyond what would be expected by.
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73. ▌
    Statistical Model Selection Assessment · holobiomicslab

    Use when after loading preprocessed metabolomics data (log-transformed feature abundance matrices with samples as rows, features as columns, and batch annotations) but before applying batch-effect correction.
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74. ▌
    Statistical Threshold Parameterization · holobiomicslab

    Use when when you have computed fold-change and p-value statistics from differential expression analysis and need to partition the results into significant and non-significant regions for visualization or downstream filtering.
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75. ▌
    Steady State Solution Uniform Sampling · holobiomicslab

    Use when when you have a constraint-based metabolic model with cell-specific flux boundaries (e.
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76. ▌
    Structural Distance Metric Computation · holobiomicslab

    Use when you have two or more lipid structures (in standardized lipid nomenclature or chemical format) and need to: (1) quantify structural dissimilarity for hierarchical clustering of lipidomes; (2) identify lipids responsible for shaping lipidome composition via distance-based feature selection;
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77. ▌
    Structure Database Candidate Retrieval · holobiomicslab

    Use when you have a mass spectrum of an unknown metabolite with a known or inferred precursor m/z, you have run a deep-learning semantic similarity model (e.
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78. ▌
    Structure Matching Via Smarts Patterns · holobiomicslab

    Use when when you need to identify compounds matching specific structural motifs or apply reaction transformation rules to a set of molecules.
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79. ▌
    Synthetic Training Instance Generation · holobiomicslab

    Use when when you have a small set of matched reference features (isolated, high-quality chromatographic peaks from reference chromatograms that have been aligned to a ground-truth reference list) and need to train a CNN model for peak detection in LC-HRMS profile mode data.
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80. ▌
    Tic Peak Identification Sliding Window · holobiomicslab

    Use when you have loaded raw mass spectrometry data (mzML, mzXML, or CDF format) into AutoTuner and need to identify peak regions in the TIC trace prior to extracted ion chromatogram (EIC) analysis.
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81. ▌
    Truncated Normal Distribution Sampling · holobiomicslab

    Use when imputing left-censored missing values in metabolomics data where missingness is caused by values falling below the limit of quantification (LOQ) or limit of detection (LOD). The input matrix should have missing values flagged as NA, with a defined upper bound (e.
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82. ▌
    Unit Test Validation For Preprocessing · holobiomicslab

    Use when after implementing or modifying basic peak filtering operations (e.g., low-intensity peak removal, intensity normalization) on mass spectrometry spectral data in supported formats (mzML, mzXML, msp, MGF, JSON).
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83. ▌
    Windows Desktop Application Deployment · holobiomicslab

    Use when when you have cloned a .NET Framework or .NET Core WPF project from a GitHub repository and need to compile it into an executable binary for Windows deployment. Specifically, when the project uses ReactiveExtensions/ReactiveProperty packages, declares a .NET Framework 4.7.2 or .
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84. ▌
    Paired Omics Bgc Metabolite Linking Workflow · holobiomicslab bundle

    Use when you have paired genomic and metabolomic data from the same microbial strains and want to link biosynthetic gene clusters (BGCs) to the metabolite features they plausibly encode — mine BGCs from assembled genomes with antiSMASH, tokenize BGC domains with iPRESTO/Pfam and cluster them into gene cluster families (GCFs) with BiG-SCAPE, build an MS/MS molecular network from paired LC-MS/MS data with GNPS to obtain molecular families (MFs), then score GCF-MF co-occurrence across strains (NPLinker-style Metcalf/hypergeometric scoring) to produce a ranked table of candidate BGC-metabolite links for natural-product discovery.
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85. ▌
    Sirius Denovo Structure Elucidation Workflow · holobiomicslab bundle

    Use when you have MS/MS for unknown features (a SIRIUS-flavour mgf / .ms) and want de novo annotation without a spectral match — molecular formula (SIRIUS+ZODIAC), structure (CSI:FingerID + COSMIC), compound class (CANOPUS), optionally against a custom database, filtered by confidence. Library-FREE by design.
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86. ▌
    Anndata Object Structure Validation · holobiomicslab

    Use when after applying Scanpy preprocessing functions (e.g., pp.normalize_total, pp.pca) to a Dask-backed AnnData object, or when performing any operation that could alter matrix dimensions, data types, or backing storage (dense, sparse, or lazy).
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87. ▌
    Empirical Bayes Variance Estimation · holobiomicslab

    Use when you have a fitted linear model (lmFit object) from microarray or RNA-seq count data and need to compute stable variance estimates and differential expression statistics despite having few biological replicates or small numbers of arrays.
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88. ▌
    Gene Expression Ranking Preparation · holobiomicslab

    Use when you have a gene expression matrix (RNA-seq counts, microarray intensities, or normalized expression values) and need to perform gene set enrichment analysis on preranked gene lists. Use this skill when you want to rank genes by a univariate statistic (e.
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89. ▌
    Gene Expression Statistical Testing · holobiomicslab

    Use when you have fit a linear model to normalized gene expression data (microarray intensities or RNA-seq counts) and need to test for differential expression across experimental conditions or contrasts.
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90. ▌
    Genomic Database Identifier Mapping · holobiomicslab

    Use when your gene expression matrix or pathway collection uses identifier formats incompatible with your enrichment analysis tool (e.g., gene symbols vs. Entrez IDs), or you need to reconcile gene sets from multiple sources (Reactome, KEGG) that employ different naming conventions.
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91. ▌
    Neighborhood Enrichment Computation · holobiomicslab

    Use when when you have spatial transcriptomics or imaging data (e.
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92. ▌
    Spatial Enrichment Score Validation · holobiomicslab

    Use when after executing a spatial statistics function (e.g., squidpy.gr.sepal) on a spatial transcriptomics dataset in AnnData format, and before using the computed rankings or enrichment scores in downstream analysis.
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93. ▌
    Spatial Neighbor Graph Construction · holobiomicslab

    Use when when you have spatial molecular data (e.g., Visium, imaging-based cytometry) stored in an AnnData object with coordinate information in .
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94. ▌
    Chemical Annotation Confidence Assessment · holobiomicslab

    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).
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95. ▌
    Extracted Ion Electropherogram Generation · holobiomicslab

    Use when you have CE-MS raw data (mzML or netCDF format) containing a target compound of known m/z ratio and you need to resolve it as a distinct peak on the effective mobility scale rather than migration time scale.
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96. ▌
    Metabolomics Peak Detection Configuration · holobiomicslab

    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.
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97. ▌
    Pca Dimensionality Reduction Unsupervised · holobiomicslab

    Use when you have a feature-by-sample matrix (rows = annotated chemical features such as m/z, retention time, GNPS spectral library matches;
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98. ▌
    Vendor File Format Specification Handling · holobiomicslab

    Use when you have a raw MSI data file from an unknown or mixed set of vendors and need to apply format-specific data extraction, spectral parsing, or image reconstruction. The file extension alone must determine which parsing module (MSIGen.raw, MSIGen.D, MSIGen.baf, MSIGen.tdf, MSIGen.
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99. ▌
    Chemical Structure Fingerprint Comparison · holobiomicslab

    Use when when you have MS/MS spectra with known chemical structures (InChIKeys or SMILES) and want to validate whether a novel or existing spectral similarity scoring method actually reflects true chemical structural similarity.
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100. ▌
     Injection Order Assignment And Scheduling · holobiomicslab

     Use when designing multi-batch LC/GC-MS experiments where you need to control for batch effects (e.g., instrument drift, reagent lot variation) and have identified both a balance dimension (e.g., sample group, treatment condition) and a randomization dimension (e.
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