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HolobiomicsLab

@holobiomicslab source repo

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1. ▌
   Metabolite Spectral Data Merging · holobiomicslab

   Use when you have two or more mass spectral libraries in different formats (NIST binary exports converted to MSP, MoNA downloads, RIKEN public databases, GNPS MGF, or batches of in-house standards in separate MSP files) and need to combine them with consistent metadata (SMILES, InChIKey, molecular.
   0 installs
2. ▌
   Metabolomics Matrix Manipulation · holobiomicslab

   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.
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3. ▌
   Qc Sample Batch Drift Correction · holobiomicslab

   Use when you have a QC-annotated feature table (samples × features with QC sample identifiers) from LC-MS untargeted metabolomic profiling and observe systematic signal drift across the run sequence or between batch blocks.
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4. ▌
   Spectral Prediction Model Fusion · holobiomicslab

   Use when you have pre-trained MLP and GNN spectral prediction models evaluated on the same ESI/LC-MS test dataset, and you seek to improve average rank performance beyond either baseline model alone.
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5. ▌
   Annotation Complexity Comparison · holobiomicslab

   Use when you have MS imaging or LC-MS data with pre-annotated m/z values that include multiple isomer or metabolite names per m/z (stored as semicolon-delimited or multi-record strings), and you want to measure whether a refinement step (e.
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6. ▌
   Annotation Table Quality Control · holobiomicslab

   Use when after obtaining in silico annotations from SIRIUS (Zodiac/Cosmic scores) or ISDB (cosine/shared peaks metrics), before using the annotation table for Feature Component calculation, chemical class assignment, or metabolite discovery prioritization.
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7. ▌
   Batch Effect Correction Workflow · holobiomicslab

   Use when you have a feature table generated from LC-MS/MS non-targeted metabolomics data that spans multiple sample preparation batches, instrumental runs, or experimental conditions.
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8. ▌
   Batch Spectral Record Processing · holobiomicslab

   Use when you have acquired MS/MS spectra in .msp format (e.g., from MassBank or experimental acquisition) and need to transform them into a structured library format compatible with automated annotation tools.
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9. ▌
   Ccs Prediction Model Application · holobiomicslab

   Use when you have structural input data (SMILES or molecular geometry files) for N-Me derived unsaturated sterol lipids and need to generate a predicted CCS dataset indexed by lipid identifier and structural isomer class.
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10. ▌
    Centered Log Ratio Normalization · holobiomicslab

    Use when apply CLR normalization when you have count-based microbiome or metabolome compositional data (e.g., 16S rRNA gene abundances, LC-MS/MS metabolite abundances) that will be used as input to multivariate predictive models (neural networks, regression, correlation analysis).
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11. ▌
    Chemical Phylogenetic Comparison · holobiomicslab

    Use when you have LC-MS/MS data preprocessed with MZmine2 into an MGF file (containing MS1 and MS2 spectra) and a feature table (peak areas per sample), and you want to relate MS1 features to each other based on predicted molecular substructures and chemical properties rather than arbitrary.
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12. ▌
    Chemical Space Structure Ranking · holobiomicslab

    Use when you have an unknown compound's mass spectrum (m/z peaks and intensities in .
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13. ▌
    Chromatographic Alignment Tuning · holobiomicslab

    Use when when processing a cohort of centroided mzML LC-MS files with high sample-to-sample retention time and m/z drift, and you need reproducible alignment of detected peaks across all samples before gap-filling and feature consolidation.
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14. ▌
    Color Palette Application Greens · holobiomicslab

    Use when when rendering a treemap of qc_summary() output showing ion counts and percentages by filter status (passed/failed), and you need a perceptually uniform, colorblind-friendly palette that clearly distinguishes filter categories while using sequential intensity to reinforce the magnitude of.
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15. ▌
    Conditional Logic Implementation · holobiomicslab

    Use when you have a user-submitted spectrum with domain-context metadata (e.
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16. ▌
    Cross Sample Metabolite Matching · holobiomicslab

    Use when after feature extraction (MS1 peak picking, MS2 recognition, or targeted list extraction) from multiple individual samples and before annotation.
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17. ▌
    Deep Learning Feature Extraction · holobiomicslab

    Use when you have preprocessed and normalized LC-MS metabolomics data from multiple disease groups (e.g., healthy, disease-A, disease-B) and need to identify which m/z features or their patterns discriminate between phenotypes.
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18. ▌
    Detection Network Output Parsing · holobiomicslab

    Use when you have raw LC-MS data in mzML format with regions of interest (ROI) already identified, and a trained detection model (e.g., checkpoint0029.pth) has produced bounding box predictions with confidence scores.
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19. ▌
    Dmodx Orthogonal Distance Metric · holobiomicslab

    Use when apply DModX when you have a normalized LC-MS feature matrix (post-normalization, Step 7 in OUKS) and need to identify samples whose metabolomic profiles are systematically displaced from the learned PCA subspace—indicating potential technical artifacts, extreme biological phenotypes, or.
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20. ▌
    Embedding Similarity Computation · holobiomicslab

    Use when when you have pre-computed embeddings (from MSBERT, Spec2Vec, or other deep learning models) for a query spectrum dataset and a reference library, and need to measure how well the embedding space ranks correct library matches.
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21. ▌
    Ensemble Variance Quantification · holobiomicslab

    Use when when you have retention order predictions from multiple independently trained models (e.g., ROASMI_1 through ROASMI_5) for the same set of compounds and need to estimate prediction confidence or identify compounds with high model disagreement.
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22. ▌
    False Positive Mitigation Tuning · holobiomicslab

    Use when after running Paramounter's peak-height optimization on XCMS CentWave-extracted metabolomic features, if the downstream analysis or feature validation reveals an unacceptable rate of false positives, or if the extraction workflow is experiencing software crashes or timeout failures due to.
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23. ▌
    Feature Annotation Consolidation · holobiomicslab

    Use when after chromatographic peak detection and feature detection in LC-MS preprocessing, when you have a set of detected features (m/z, retention time, intensity) and need to consolidate redundant or related ion signals into compound-level feature groups before downstream statistical or.
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24. ▌
    Feature Detection Rate Filtering · holobiomicslab

    Use when after constructing a MetaboSet object with LC-MS peak abundances, sample metadata (pData with QC labels), and feature metadata (fData), and after marking missing values as NA.
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25. ▌
    Feature Group Isotope Annotation · holobiomicslab

    Use when you have a feature table from nontargeted LC-MS peak detection (containing m/z, retention time, and intensity values) and need to disambiguate whether detected features represent the same molecular entity under different ionization/modification states or are true independent signals.
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26. ▌
    Feature Intensity Quantification · holobiomicslab

    Use when after features have been identified in LC-MS data via peak picking, MS2 recognition, or targeted-list matching, and you need to measure their signal magnitude (peak height or area) across samples for quantitative comparison, normalization, or statistical testing in metabolomics studies.
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27. ▌
    Feature Node Identifier Matching · holobiomicslab

    Use when you have created a feature-based GNPS molecular network and a corresponding MS2LDA experiment, and you need to propagate substructural motif annotations from the MS2LDA output back to the network nodes by matching feature IDs.
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28. ▌
    Feature Table Moniker Management · holobiomicslab

    Use when when processing a metabolomics feature table through multiple sequential transformations (e.g., imputation, normalization, batch correction, annotation) and you need to track which version of the table is being used at each step.
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29. ▌
    File System Audit And Validation · holobiomicslab

    Use when after invoking the saveAnnotations function on a MetaboAnnotatoR annotations object to confirm that all four expected output file types (global results file, ranked results file, per-feature ranked spectra PDFs, and pseudo-MS/MS MGF file) have been written to the output directory without.
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30. ▌
    Fragment Ion Difference Counting · holobiomicslab

    Use when when preparing tandem MS/MS data for spectral alignment and similarity comparison, particularly when you have loaded raw fragmentation spectra and need to extract and quantify mass difference patterns that capture the fragmentation process.
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31. ▌
    Fragment Ion Scoring And Ranking · holobiomicslab

    Use when when you have an experimental MS/MS spectrum (centroid mode) and need to convert it into a scored fragment library entry, or when you must rank candidate fragments by confidence before performing spectrum-to-spectrum matching in metabolite annotation workflows.
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32. ▌
    Fragment Ion Type Interpretation · holobiomicslab

    Use when you have an tandem MS spectrum with unidentified peaks and a known or hypothesized peptide sequence (in ProForma 2.0 format, including post-translational modifications).
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33. ▌
    Fragmentation Pattern Annotation · holobiomicslab

    Use when when you have an experimental MS/MS spectrum (query spectrum as m/z–intensity pairs) and need to identify the compound by comparing its fragmentation pattern to a spectral library.
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34. ▌
    Genomic Metabolomic Link Ranking · holobiomicslab

    Use when you have paired genomic (BGCs clustered into GCFs via BiG-SCAPE) and metabolomic data (MS2 spectra grouped into MFs), with strain/sample co-occurrence patterns and predicted BGC–spectrum IOKR scores, and you need to prioritise which GCF–MF pairs are most likely to represent true natural.
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35. ▌
    Ion Count Percentage Calculation · holobiomicslab

    Use when after applying one or more mpactr filters (mispicked, group, cv, insource) to an mpactr object and generating a qc_summary() data.
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36. ▌
    Ion Filter Status Categorization · holobiomicslab

    Use when after applying one or more mpactr filters (filter_mispicked_ions, filter_group, filter_cv, filter_insource_ions) to a peak table, when you need to quantify how many ions passed or failed each filter and summarize the overall filtering impact by status distribution.
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37. ▌
    Ion Type Assignment Verification · holobiomicslab

    Use when after calling MsmsSpectrum.annotate_proforma() to assign fragment ions to a mass spectrum, verify that each annotated peak has the correct ion_type ('b' or 'y'), charge state, and m/z deviation from the theoretical mass computed for that peptidoform.
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38. ▌
    Isotopologue Signature Detection · holobiomicslab

    Use when you have preprocessed, statistically significant LC-MS features (from multiple assays or a single assay) and need to group features that represent the same metabolite in different isotopic labeling states.
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39. ▌
    JSON Parsing Motifset Extraction · holobiomicslab

    Use when you have completed the MS2LDA LDA modeling phase and possess motifset.json or motifset_optimized.json files containing inferred Mass2Motifs.
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40. ▌
    Kendrick Mass Defect Calculation · holobiomicslab

    Use when you have a feature list from LC- or GC-HRMS analysis (with m/z, retention time, and exact mass columns) and you want to detect homologous series of PFAS compounds that repeat by CF₂ mass increments (typically ≈34 Da).
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41. ▌
    Lantibiotic Structure Annotation · holobiomicslab

    Use when you have (1) genomic data from a Streptomyces or other RiPP-producing organism in raw FASTA format or annotated GenBank format, (2) high-resolution LC-MS/MS spectra in centroided MGF, mzML, mzXML, or mzData format, and (3) a known or predicted lantibiotic core peptide sequence you wish to.
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42. ▌
    Lc Ms Data Pipeline Architecture · holobiomicslab

    Use when you have vendor-format LC-MS acquisition files (.raw, .d, .ms) from instrument runs and need to set up an end-to-end data quality control system that converts proprietary formats into open mzML, processes spectral data, and surfaces QC failures in real time.
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43. ▌
    Lc Ms Feature Quality Assessment · holobiomicslab

    Use when you have generated feature tables from LC-MS data using different parameter combinations (e.g., varying Centwave, FeatureFinderMetabo, or ADAP peak picking settings) and need to objectively compare their outputs to select the -performing configuration for your dataset.
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44. ▌
    Lc Ms Quality Metric Computation · holobiomicslab

    Use when after performing peak detection on centroided .mzML LC-MS data with screening_mode=FALSE in TARDIS.
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45. ▌
    Lcms Feature Probability Scoring · holobiomicslab

    Use when you have an untargeted LC/MS feature table (m/z, retention time, intensity columns) and need to move beyond single-hit matching to probabilistic annotation.
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46. ▌
    Lcms Feature Relationship Export · holobiomicslab

    Use when after ISFrag has completed identification of in-source fragment features (Part 4 output), when you need to serialize and inspect the hierarchical fragmentation relationships among identified ISF features, or when preparing data for visualization or external analysis of fragment lineage and.
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47. ▌
    Lcms Target Visibility Screening · holobiomicslab

    Use when after loading centroided .mzML LC–MS runs and before executing full peak detection and integration.
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48. ▌
    Lipid Library Annotation From Mz · holobiomicslab

    Use when you have experimental peaklist data (CSV or mzML-derived tables) from UHPLC-HRMS/MS instruments (Q-Exactive, Agilent/Bruker/SCIEX Q-TOF) with fragment m/z values and want to annotate them to known lipid identities.
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49. ▌
    Machine Learning Model Inference · holobiomicslab

    Use when you have molecular descriptors or fingerprints for a set of compounds (e.g., from LC-MS metabolomics) and need to predict a continuous property—such as HPLC retention time—to support compound identification or filter out false positive annotations.
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50. ▌
    Mahalanobis Distance Calculation · holobiomicslab

    Use when after data normalization (Box-Cox transformation) and before hypothesis testing in Step 9 of untargeted metabolomic workflows.
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51. ▌
    Mass Difference Pattern Matching · holobiomicslab

    Use when after peak picking and sample alignment when you have an aligned feature table containing m/z and retention time coordinates. Use it when your untargeted LC-MS workflow needs to reduce feature redundancy caused by naturally occurring stable isotope patterns and common adduct formation.
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52. ▌
    Mass Spectral Feature Annotation · holobiomicslab

    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.
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53. ▌
    Mass Spectral Network Annotation · holobiomicslab

    Use when you have a GNPS molecular network (classical or feature-based) and MS2LDA LDA experiment output (Mass2Motif assignments with probability and overlap scores) from the same experiment, and you want to annotate network nodes with structural motifs and chemical classes to infer molecular.
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54. ▌
    Mass Spectrum Similarity Scoring · holobiomicslab

    Use when when you have a query MS/MS spectrum (m/z and intensity pairs) that you need to match against a library of reference spectra, and you want to identify the -matching library entry while accounting for unmatched peaks that may indicate spectral contamination or chimerism.
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55. ▌
    Mass Tolerance Optimization Hrms · holobiomicslab

    Use when you have experimental peak lists (m/z, retention time, intensity) from peak-picking software (MZmine, XCMS, MS-DIAL, or Compound Discoverer) and need to match them against a simulated lipid fragment library (500,000+ lipid species).
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56. ▌
    Mass Track Consensus Computation · holobiomicslab

    Use when after mass tracks have been aligned across all samples (either via pairwise alignment for ≤10 samples or nearest-neighbor clustering for larger cohorts), and you need to generate a single representative m/z per aligned bin for downstream feature extraction and annotation.
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57. ▌
    Metabolite Database Construction · holobiomicslab

    Use when when performing untargeted metabolomics annotation at scale and you need to estimate false discovery rates for candidate metabolite identifications.
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58. ▌
    Metabolite Feature Normalization · holobiomicslab

    Use when you have loaded two or more nontargeted LCMS feature tables from the same analytical method that contain m/z, retention time, and intensity values, and these datasets exhibit differences in metadata scale, distribution, or format that could confound cross-dataset feature matching or.
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59. ▌
    Metabolite Identifier Conversion · holobiomicslab

    Use when your metabolomics dataset contains metabolite identifiers in multiple formats (e.
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60. ▌
    Metabolomics Classifier Training · holobiomicslab

    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.
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61. ▌
    Molecular Fingerprint Generation · holobiomicslab

    Use when when you have annotated chemical structures (SMILES or InChI strings) from a curated MS/MS dataset and need to compute pairwise structural similarity scores (Tanimoto or other metrics) as training labels, or when preparing molecular representations for comparison against mass spectral data.
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62. ▌
    Molecular Fingerprint Prediction · holobiomicslab

    Use when you have tandem MS/MS spectra paired with known molecular structures (for training) or unknown spectra requiring structure identification, and you want to predict dense molecular fingerprint vectors that encode structural similarity.
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63. ▌
    Ms Peak Blank Signal Subtraction · holobiomicslab

    Use when you have an MS-DIAL feature table from DDA or DIA LC-MS analysis that includes blank injection samples (at least 3 recommended), and you need to eliminate features that are artifactual contamination rather than genuine metabolites.
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64. ▌
    Ms Spectra Dataset Preprocessing · holobiomicslab

    Use when when you have a raw or partially processed MS/MS spectra collection (e.g., GNPS-sourced Orbitrap or Q-TOF spectra in MGF format) and need to (1) restrict to a specific instrument type (e.
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65. ▌
    Ms2 Diagnostic Fragment Matching · holobiomicslab

    Use when you have centroided MS2 spectra from data-dependent LC- or GC-HRMS measurements and need to rapidly prioritize potential PFAS features within a larger feature set.
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66. ▌
    Ms2 Fragment Pattern Recognition · holobiomicslab

    Use when you have data-dependent acquisition (DDA) MS2 spectra from HRMS measurements (ESI or APCI ionization) and need to prioritize potential PFAS features from a large pool of detected ions.
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67. ▌
    Msms Spectral Database Retrieval · holobiomicslab

    Use when when you have a target compound (modified or unmodified) and need to obtain its experimental MS/MS spectrum and metadata to serve as a known reference for ModiFinder analysis, or when benchmarking evaluation methods like average_distance scoring.
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68. ▌
    Multiclass Metabolite Comparison · holobiomicslab

    Use when when you have a normalized metabolite abundance matrix with sample metadata assigning each sample to one of three or more distinct biological classes (e.
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69. ▌
    Network Diffusion Prioritization · holobiomicslab

    Use when after clustering and filtering KEGG candidates for LC-MS features, when you have a ranked set of candidate metabolites per feature and access to a metabolite interaction network (e.g., from FELLA).
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70. ▌
    Neural Network Input Preparation · holobiomicslab

    Use when when you have annotated representative LCMS samples (raw mzML files + labeled feature tables in mzmine CSV format) and need to convert them into balanced or unbalanced peak matrix batches with fixed dimensions for neural network training.
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71. ▌
    Neutral Loss Formula Computation · holobiomicslab

    Use when you have tandem mass spectra with precursor m/z and observed fragment peak m/z values (as mz/intensity pairs), and you need to construct interpretable feature vectors where each axis corresponds to a real chemical entity (peak or neutral loss) rather than a latent dimension.
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72. ▌
    Neutral Loss Peak Interpretation · holobiomicslab

    Use when you have a tandem mass spectrum (MSMS) loaded via USI and wish to maximize the interpretability of observed peaks.
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73. ▌
    Numerical Method Scheme Analysis · holobiomicslab

    Use when when correcting LC-MS isotope labeling data and existing numerical schemes (e.
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74. ▌
    Peak Deconvolution Preprocessing · holobiomicslab

    Use when you have raw IM-MS data in Agilent MassHunter (.d) or UIMF format from drift tube (DT) or SLIM instruments, and you intend to perform HRdm demultiplexing or peak deconvolution to resolve co-eluting or structurally similar ions.
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75. ▌
    Peak Detection Mass Spectrometry · holobiomicslab

    Use when you have raw mass-spectrometry data files (mzML, mzXML, or vendor formats) from untargeted metabolomics experiments and need to extract differential metabolic ion peaks for downstream statistical or annotation analysis.
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76. ▌
    Peak Detection Optimization Free · holobiomicslab

    Use when when processing raw untargeted LC/MS data in mzML or mzXML format and you need to detect peaks across mass-to-charge (m/z) and retention time (rt) dimensions without prior knowledge of optimal signal detection parameters, QC samples, or domain expertise in LC/MS preprocessing.
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77. ▌
    Peak Detection Signal Processing · holobiomicslab

    Use when after feature extraction from mzML/mzXML breath analysis data when you have a numerical array or dataframe of feature intensities across retention time or m/z dimensions and need to identify which features represent genuine volatile organic compound (VOC) signals rather than noise or.
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78. ▌
    Peak List Filtering And Cleaning · holobiomicslab

    Use when you have acquired MS/MS spectra containing suspect noise ions—either electronic noise (ions with identical intensities occurring >4 times in a single peak list, a signature of detector artifacts) or chemical noise (fragment ions chemically implausible given the precursor molecule's.
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79. ▌
    Peak List Parsing And Validation · holobiomicslab

    Use when when importing raw mass spectrometry data from vendor or open formats (mzML, mzXML, msp, MGF, JSON, metabolomics-USI) and you need to extract peak m/z and intensity pairs into a standardized representation.
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80. ▌
    Peak Matching And Mass Alignment · holobiomicslab

    Use when when you have raw MS2 spectra (m/z and intensity pairs) and a curated reference peak list from a large training dataset (e.
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81. ▌
    Peak Picking Algorithm Selection · holobiomicslab

    Use when at the entry point of SLAW processing when you have centroided mzML or netCDF LC-MS files and need to decide which peak-picking algorithm to use. Trigger this skill when: (1) raw LC-MS data must be converted into a feature matrix;
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82. ▌
    Peptide Mass Calculation Average · holobiomicslab

    Use when you have a list of polypeptide sequences (one per line or CSV format) and need to compute average mass (weighted by natural isotope abundances) to compare against experimental LC-MS or MS/MS data where the full isotopic distribution—not just the most abundant peak—is relevant for peptide.
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83. ▌
    Pfas Homologous Series Detection · holobiomicslab

    Use when you have an m/z-resolved feature list from LC- or GC-HRMS analysis (either detected by pyOpenMS or provided as a custom Excel table) and need to prioritize potential PFAS compounds by identifying clusters of homologous structures.
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84. ▌
    Proforma 2 0 Peptidoform Parsing · holobiomicslab

    Use when you have a ProForma 2.0 peptidoform string (e.
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85. ▌
    Publication Figure Customization · holobiomicslab

    Use when after annotating a mass spectrometry spectrum with fragment ions (e.g., via ProForma 2.
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86. ▌
    Qc Sample Reliability Evaluation · holobiomicslab

    Use when after drift correction has been applied to your LC-MS peak table and you need to identify low-quality metabolic features that exhibit high internal spread (RSD, RSD*) or excessive QC-versus-biological variation (D-ratio) before imputation and batch correction.
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87. ▌
    Qr Code Generation And Embedding · holobiomicslab

    Use when you need to publish metabolomics spectra in static media (PDF, print, supplementary tables) and want readers or automated systems to access the corresponding interactive spectrum visualization without manual lookup.
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88. ▌
    Quasi Molecular Adduct Filtering · holobiomicslab

    Use when after feature clustering has been applied to co-eluting LC-MS features and mass-to-charge ratio matching to KEGG has produced an annotated table with adduct assignments.
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89. ▌
    R6 Class Object Mutation Testing · holobiomicslab

    Use when when applying a series of mpactr filter functions (filter_mispicked_ions, filter_group, filter_cv, filter_insource_ions) with copy_object=FALSE to confirm that the original peak table object is mutated as intended, not silently copied.
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90. ▌
    Ranked Annotation Prioritization · holobiomicslab

    Use when you have completed cluster-based filtering of KEGG candidate assignments in untargeted LC-MS metabolomics and need to rank those candidates by biological plausibility using a metabolite interaction network. Specifically, use it after `clusterBased.
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91. ▌
    Raw File Data Extraction Via API · holobiomicslab

    Use when you have a Thermo Fisher Scientific .raw file (e.g., Q Exactive HF, Orbitrap) and need to extract specific spectral scans, chromatographic traces, scan-level metadata, or file-level headers programmatically—e.
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92. ▌
    Reference Semantics Verification · holobiomicslab

    Use when when applying sequential filters to a large metabolomics peak table (e.g., mispicked ions, group, CV, or in-source filters) and you need to confirm that setting copy_object=FALSE actually modifies the input object in-place rather than creating a hidden copy.
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93. ▌
    Relative Abundance Preprocessing · holobiomicslab

    Use when when you have raw count tables from 16S rRNA sequencing (microbiome) or LC-MS/MS metabolomics (metabolome) and need to train neural network or regression models for microbe-metabolite relationship prediction.
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94. ▌
    Replicate Spectrum Concatenation · holobiomicslab

    Use when after extracting raw MS/MS spectra from mzML files for individual features (identified by precursor m/z and retention time) and you have multiple replicate spectra for the same feature that need to be pooled for consensus analysis.
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95. ▌
    Retention Time Alignment Scoring · holobiomicslab

    Use when after feature m/z grouping and pairwise alignment detection have identified candidate feature pairs, and anchor points have been selected to establish retention time correspondence.
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96. ▌
    Retention Time Index Calibration · holobiomicslab

    Use when you have acquired a bottom-up proteomics LC-MS/MS run (e.
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97. ▌
    Retention Time Window Clustering · holobiomicslab

    Use when after filtering LC-MS features by statistical significance (e.g., p-value < 0.01) and you need to link individual m/z features into structural clusters representing the same metabolite in different ionization states or isotopic forms.
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98. ▌
    Retention Time Window Extraction · holobiomicslab

    Use when you have loaded sqMass files containing pre-extracted transition group chromatograms and need to isolate chromatographic traces within a specific retention time interval—either defined by OpenSwath feature metadata (apex retention time ± margin) or by manual user selection.
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99. ▌
    Runtime Performance Benchmarking · holobiomicslab

    Use when when you have implemented or adopted a new clustering or analysis tool and need to validate that it meets stated runtime claims on a representative production-scale dataset. Particularly important when the tool uses hardware acceleration (GPU) and the claimed speedup is a core contribution;
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100. ▌
     Scan Index Filtering By Ms Level · holobiomicslab

     Use when when you have generated a scan index from rawrr::readIndex() on a Thermo .
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