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HolobiomicsLab

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1. ▌
   Internal Standard Area Mis Injection Flagging · holobiomicslab

   Use when after building a SummarizedExperiment object containing LC-MS peak areas and internal standard assignments, when you need to identify study samples with anomalous Internal Standard signal (indicating syringe failure, capillary clogging, or injection system malfunction) before applying.
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   Isotopologue Adduct Cross Assay Link Encoding · holobiomicslab

   Use when after structural cluster assignment and correlation clustering are complete, and you need to represent the full set of structural relationships (isotopologues, adducts, cross-assay links, and correlation co-membership) in a single unified graph for interactive visualization, network.
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3. ▌
   Isotopologue Distribution Matrix Construction · holobiomicslab

   Use when when you have measured fractional abundances of isotopologues (FAM) from LC-MS in an isotope labeling experiment and need to correct them to obtain true mass distribution vectors (MDV) that reflect only the labeling contribution, accounting for background from naturally occurring isotopes.
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4. ▌
   Lc Ms Gradient Encoding Vector Representation · holobiomicslab

   Use when when you have a set of candidate LC gradients (parameter combinations) that you wish to evaluate with a Gaussian process model, or when you need to convert raw gradient specifications into a standardized numerical format for Bayesian optimization acquisition function computation.
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5. ▌
   Masking Augmentation For Contrastive Learning · holobiomicslab

   Use when when training a transformer encoder on tandem mass spectra (MS/MS) and you need to generate positive sample pairs for contrastive learning without access to labeled chemical or spectral analogues.
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6. ▌
   Mass Spectrometry Data Loading And Formatting · holobiomicslab

   Use when you have raw or curated mass spectrometry data (MS1, MS2, or MSMS) in mzML, mzXML, CDF, MGF, MSP formats, or from a MassBank/MetaboLights repository, and need to convert it into an in-memory or on-disk spectral object that supports filtering, comparison, and annotation workflows.
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7. ▌
   Mass Spectrometry Feature Tree Interpretation · holobiomicslab

   Use when after running qiime qemistree make-hierarchy and obtaining a tree artifact (qemistree.
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8. ▌
   Mass Spectrometry Spectral Embedding Learning · holobiomicslab

   Use when when you have preprocessed MS/MS spectral pairs (peak intensities and m/z values) and need to predict molecular structural similarity scores, or when you want to project spectra into a learned chemical embedding space for visualization (e.g., via UMAP) or downstream similarity searches.
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9. ▌
   Mass Spectrum Normalization And Preprocessing · holobiomicslab

   Use when you have raw tandem mass spectra data (mz/intensity pairs and precursor m/z values) and need to train interpretable machine learning models (regression or tree-based) where feature interpretability and direct chemical meaning are required.
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10. ▌
    Mass To Charge Retention Time Feature Mapping · holobiomicslab

    Use when you have centroided data-dependent acquisition (DDA) mzML files from LC- or GC-HRMS measurements and need to convert continuous raw mass spectrometric signals into discrete, quantifiable chromatographic features (m/z, RT, intensity, charge, isotope) before PFAS-specific prioritization or.
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11. ▌
    Memo Ms API Usage And Parameter Configuration · holobiomicslab

    Use when you have aligned feature tables (CSV format) with corresponding MS2 spectra data (MGF or mzML files), and need to construct a sample-level vectorization matrix where each row represents a sample and columns encode the occurrence counts of MS2 peaks and neutral losses observed in that.
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12. ▌
    Metabolite Feature Grouping By Adduct Isotope · holobiomicslab

    Use when immediately after generating a feature table (m/z, retention time, intensity) from centroided mzML data when you need to collapse multiple feature detections of the same compound (arising from different ionization states, charge states, or isotope patterns) into unified empirical compound.
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13. ▌
    Microbiome Metabolome Abundance Normalization · holobiomicslab

    Use when when you have raw count matrices from paired microbiome (16S rRNA or metagenomic) and metabolomic (LC-MS/MS) profiling data that will be used to train or apply a predictive model (e.g., MiMeNet, MelonnPan, Random Forest) to predict metabolite abundances from microbial composition.
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14. ▌
    Molecular Fingerprint Generation And Encoding · holobiomicslab

    Use when when you have paired tandem MS spectra and corresponding molecular structures (as SMILES strings or InChI keys) and need to train a model that jointly embeds spectra and structures for structure annotation by database lookup.
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15. ▌
    Multi Dimensional Data Loading And Formatting · holobiomicslab

    Use when you have raw or processed mass spectrometry data in HDF5 (.h5) or mzML format and need to ingest it into DEIMoS for multi-dimensional analysis.
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16. ▌
    Neural Network Ensemble Inference Via Dropout · holobiomicslab

    Use when when you have a trained neural network and need to quantify prediction uncertainty or improve accuracy by filtering low-confidence predictions. Particularly useful when input spectra pairs have variable quality or when downstream tasks (e.
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17. ▌
    Oracle Mode Structural Constraint Integration · holobiomicslab

    Use when when you have loaded both a known compound and its modified analog with MS/MS spectra, initially generated baseline modification probability scores, and then obtained or confirmed the structure of the modified compound.
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18. ▌
    Pairwise Alignment With Anchor Prioritization · holobiomicslab

    Use when when processing LC-MS metabolomics datasets with 10 or fewer samples and requiring reproducible mass track alignment across the cohort.
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19. ▌
    Peak Annotation Quantification And Comparison · holobiomicslab

    Use when you have a tandem mass spectrum (MsmsSpectrum) from a known peptide and need to determine what fraction of observed peaks can be explained by expected fragment ions.
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20. ▌
    Peak Table Filtering By Mz And Retention Time · holobiomicslab

    Use when you have a table of detected chromatographic peaks (e.g., from CentWave peak detection in xcms) and need to isolate a single target m/z (e.g., m/z 304.1131 for a pesticide) or a narrow m/z range, or when you must restrict analysis to a known retention time window (e.
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21. ▌
    Penalty Factor Application In Spectral Search · holobiomicslab

    Use when when performing reverse spectral search on MS/MS data suspected to contain chimeric spectra (multiple co-isolated precursors), and you need to increase the number of reliable spectral matches while filtering out false positives.
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22. ▌
    Peptidoform Representation And Interpretation · holobiomicslab

    Use when when you have a tandem mass spectrometry spectrum with a known or inferred peptide sequence that may contain post-translational modifications (phosphorylation, glycosylation, cross-links), and you need to annotate which observed m/z peaks correspond to specific fragment ion types (b, y, a.
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23. ▌
    Probabilistic Modeling Convergence Assessment · holobiomicslab

    Use when during the LDA training phase when you need to decide whether the model has learned a stable representation of Mass2Motifs.
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24. ▌
    Retention Time Correlation Similarity Scoring · holobiomicslab

    Use when when you have detected multiple features from non-targeted mass spectrometry and need to group them by putative compound origin.
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25. ▌
    Tab Delimited Export Formatting For Metabolomics · holobiomicslab

    Use when after completing batch normalization and quality control filtering on a Metaboprep object, when you need to share processed metabolomics data with collaborators, import into other statistical packages, or archive results in a platform-independent format.
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26. ▌
    Targeted Metabolite Screening Mode Configuration · holobiomicslab

    Use when beginning a targeted LC–MS metabolomics or lipidomics study with a predefined list of compounds (e.g., 10+ targets) and you have centroided .
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27. ▌
    Training Data Generation From Reference Features · holobiomicslab

    Use when you have isolated, manually curated reference chromatographic peaks (ground-truth features) matched to local maxima detected in LC-HRMS profile-mode data, and you need to train a CNN classifier to distinguish peaks from background signals across diverse sample compositions.
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28. ▌
    Metabolomicshub Announcement Authoring · holobiomicslab

    Use when publishing a metabolomics dataset into the MetabolomicsHub index — building the common-model file, deriving and validating the announcement against the right profile, minting an identifier, and submitting a revision whose outcome only a polled task reports.
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29. ▌
    Categorical Annotation Handling In Omics Data · holobiomicslab

    Use when you have (1) spatial omics data loaded in AnnData format with a pre-built spatial neighbor graph (from squidpy.gr.spatial_neighbors() or similar), (2) a categorical variable in the AnnData object (e.g., cell type, tissue compartment, annotation stored as .obs or .
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30. ▌
    Coefficient And Standard Error Interpretation · holobiomicslab

    Use when after fitting a linear model to expression data using limma's lmFit function on a design matrix encoding experimental groups, inspect the resulting MArrayLM object to retrieve coefficient estimates (log-fold-changes) and standard errors needed to assess which genes show meaningful.
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31. ▌
    Deseqdataset Construction From Quantification · holobiomicslab

    Use when you have transcript-level quantification files (quant.sf, kallisto abundance.h5, or RSEM output) from one or more RNA-seq samples and need to construct a count matrix for differential expression analysis.
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32. ▌
    Differential Expression Result Interpretation · holobiomicslab

    Use when you have fitted a linear model to gene expression data (microarray, RNA-seq, qPCR, or proteomics) across multiple samples and need to compute gene-level test statistics.
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33. ▌
    Image Feature Extraction From Tissue Sections · holobiomicslab

    Use when you have a spatial transcriptomics dataset (AnnData object) with cell/spot coordinates and an associated tissue microscopy image file, and you need to compute image-derived features (e.
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34. ▌
    Transcript Expression Quantification Handling · holobiomicslab

    Use when you have transcript abundance estimates from RNA-seq quantification tools (e.
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35. ▌
    Drift Time Retention Time Distance Metric Selection · holobiomicslab

    Use when after peak detection in GCIMS when you need to group detected peaks across multiple samples into reproducible clusters.
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36. ▌
    Mz Retention Time Tolerance Parameter Configuration · holobiomicslab

    Use when when invoking Asari to process centroid mzML files for the first time in a PCPFM experiment, or when RT and m/z accuracy characteristics of your LC-MS instrument differ from the pipeline defaults (e.
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37. ▌
    Scalability Extrapolation And Throughput Estimation · holobiomicslab

    Use when you have a new or modified LC-MS data processing tool and need to determine whether it can handle production-scale sample cohorts (50–100+ samples) on modest hardware (single-core CPU, ≤16 GB RAM).
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38. ▌
    Benchmarking And Comparative Performance Evaluation · holobiomicslab

    Use when you have trained a new machine learning model for chemical formula or adduct assignment from MS/MS spectra and need to assess whether it offers genuine performance gains over established baselines. Use it specifically when you have access to ground-truth annotations (e.
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39. ▌
    Command Line Interface Configuration And Automation · holobiomicslab

    Use when you have a batch of raw LC-IMS-MS/MS data in mzML or mzML.
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40. ▌
    Constraint Based Model Sampling And Flux Prediction · holobiomicslab

    Use when you have constraint-based metabolic models with integrated multi-omics constraints (transcriptomics via Reaction Activity Scores, extracellular flux ratios via YSI bioanalyzer or LC-MS, nutrient availability bounds), and you need to determine whether differential enzyme expression.
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41. ▌
    Cross Spectrum Negative Generation Within Mz Window · holobiomicslab

    Use when when preparing augmented training data for a Siamese rescore model that must learn to rank correct molecular formulas above incorrect ones;
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42. ▌
    Empirical Compound Grouping By Adducts And Isotopes · holobiomicslab

    Use when after feature table normalization and imputation are complete, immediately before MS1 and MS2 annotation.
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43. ▌
    Feature Network Construction From Mass Spectrometry · holobiomicslab

    Use when you have a preprocessed feature table (tab-delimited: feature ID, m/z, retention time, intensity columns) from LC-MS data and need to annotate which observed features represent the same underlying compound via isotope or adduct relationships.
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44. ▌
    Instrumental Parameter Validation Mass Spectrometry · holobiomicslab

    Use when when you have obtained a raw Orbitrap mass spectrometry file and need to verify that the instrument was configured as claimed in the methods section or dataset documentation—especially before investing in peptide fragmentation analysis, spectrum library matching, or quantitative proteomics.
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45. ▌
    Interquartile Range Filtering For Outlier Rejection · holobiomicslab

    Use when when you have ensemble predictions (e.g., from Monte-Carlo Dropout inference with N ≥ 10 forward passes per input) and need to distinguish high-confidence from uncertain predictions before downstream analysis.
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46. ▌
    Ionization Mode Inference From Acquisition Metadata · holobiomicslab

    Use when when beginning preprocessing of a new LC-MS dataset with mzML files or raw acquisitions and you need to determine which ionization mode was used before running feature detection (Asari) or empirical compound grouping (khipu).
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47. ▌
    Mass Spectral Similarity Binning And Stratification · holobiomicslab

    Use when you have predicted structural similarity scores (e.g., Tanimoto or Dice scores) for a large set of spectrum pairs and need to assess prediction accuracy across the full range of possible similarities. Critical when evaluating whether uncertainty filtering (e.
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48. ▌
    Naturally Occurring Isotope Contribution Accounting · holobiomicslab

    Use when you have raw fractional abundances of measured isotopologues (FAM) from LC-MS instruments in an isotope labeling experiment and need to correct them to obtain true mass distribution vectors (MDV) reflecting only the contribution from the isotopic tracer.
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49. ▌
    Post Translational Modification Pattern Recognition · holobiomicslab

    Use when you have centroided LC-MS/MS spectra (in MGF, mzXML, mzML, or mzData format) and genomically-predicted precursor peptide sequences, and you need to identify which predicted RiPPs are actually expressed and modified in the sample.
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50. ▌
    Precision Recall Curve Generation For Ranking Tasks · holobiomicslab

    Use when when you have computed similarity scores (e.g., MS2DeepScore, Spec2Vec, modified Cosine) between pairs of spectra or compounds and want to compare their ability to retrieve chemically related pairs. Apply this skill if you have ground-truth structural similarity labels (e.
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51. ▌
    Probability Threshold Tuning For Chemical Detection · holobiomicslab

    Use when you have a trained NeatMS neural network model (.h5 format) and need to select an operating threshold for peak classification on your LCMS dataset.
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52. ▌
    Ranking Metric Computation For Spectral Predictions · holobiomicslab

    Use when after generating ranked predictions of chemical formulas or subformulas for MS/MS spectra (from a neural network model like MIST-CF's formula transformer), compare predicted assignments against ground-truth reference labels to measure ranking quality.
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53. ▌
    Smarts Pattern Matching For Chemical Transformation · holobiomicslab

    Use when when you have seed metabolite structures (SMILES or MOL format) from metabolomics data and a curated biotransformation rule database (each rule specifying reactant SMARTS, product SMARTS, and transformation type), and you need to systematically enumerate plausible biotransformation.
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54. ▌
    Spectral Annotation Filtering By Similarity Metrics · holobiomicslab

    Use when after running GNPS molecular networking, SIRIUS compound identification, or ISDB in silico annotation on LC-MS/MS data, when you have provisional annotations for features but need to filter them to retain only high-confidence matches before calculating novelty scores, detecting chemical.
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55. ▌
    Spectral Annotation Recall Precision Quantification · holobiomicslab

    Use when you have run two or more annotation pipelines on the same MS/MS spectral dataset (e.
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56. ▌
    Spectral Peak Retention Signal Noise Discrimination · holobiomicslab

    Use when when you have replicate MS/MS spectra for the same feature (precursor m/z and retention time) and need to distinguish genuine fragment ions from noise.
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57. ▌
    Multimodal Spectral Alignment By Molecular Identifier · holobiomicslab

    Use when you have parsed spectral data from four distinct modalities (NMR, HSQC, COSY, IR) stored as separate numpy arrays or DataFrames with normalized chemical shifts and intensity ranges, and you need to create paired multimodal training records where each molecule's spectra across all.
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58. ▌
    Tile Matrix Generation Counting Strategy Selection · holobiomicslab

    Use when after importing fragment files into AnnData using pp.import_fragments and before performing spectral embedding (tl.spectral) or other dimension reduction.
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59. ▌
    Transcription Factor Cooperative Binding Detection · holobiomicslab

    Use when you have a chromVARDeviations object with precomputed bias-corrected deviations and z-scores for multiple annotation sets (e.
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60. ▌
    Variability Score Comparison Across Parameter Sets · holobiomicslab

    Use when when you have computed deviation and variability scores using chromVAR for two or more discrete parameter configurations (e.g., 6-mer vs 7-mer kmers, or different motif databases) and need to determine which parameter set produces stronger or more discriminative variability signals.
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61. ▌
    Background Distribution Significance Thresholding · holobiomicslab

    Use when use when the workflow requires background-distribution-significance-thresholding.
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62. ▌
    Deep Learning Module Instantiation And Validation · holobiomicslab

    Use when you have cloned or loaded a deep-learning architecture extension (e.g., chemprop-IR) and need to verify that its feature extraction component can be instantiated and produces correctly shaped feature tensors before integrating it into a larger pipeline or training loop.
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63. ▌
    Docker Container Runtime Execution And Monitoring · holobiomicslab

    Use when you have a vendor mass spectrometry raw file (e.g., .raw format) that requires conversion to Aird format using AirdPro CLI, and you are running on macOS or Linux.
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64. ▌
    Mass Spectrum Noise Threshold Parameter Selection · holobiomicslab

    Use when when processing raw or centroid mass spectra (e.g., ESI-MS or FT-ICR data from Bruker .d or Thermo .raw formats) and you need to remove instrument noise and low-abundance peaks before peak picking or molecular formula assignment.
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65. ▌
    Permanova Statistical Testing Multivariate Groups · holobiomicslab

    Use when you have normalized peak intensities or abundance matrices from mass spectrometry (e.
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66. ▌
    Shell Environment Setup And Dependency Management · holobiomicslab

    Use when when you need to verify that a GitHub Actions workflow (e.g., main.yml) executes successfully on your local machine, reproduce a reported passing or failing CI build status, or debug why a workflow badge reports success/failure.
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67. ▌
    Approximate Nearest Neighbor Indexing For Spectra · holobiomicslab

    Use when when you have a large spectral library (hundreds of thousands or millions of spectra) and need to search unknown query spectra against it for both unmodified and post-translationally modified peptides.
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68. ▌
    Background Distribution Generation Shuffled Omics · holobiomicslab

    Use when when training a regression or neural-network model on paired microbiome and metabolome data, and you need to establish a statistically principled cutoff for identifying metabolites (or other features) whose prediction correlations are significantly better than random chance.
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69. ▌
    Compound Structure Enumeration Across Generations · holobiomicslab

    Use when you have a set of seed compounds (as SMILES strings) and want to predict downstream products by systematically applying generalized reaction rules (e.g., from MetaCyc) across multiple reaction generations.
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70. ▌
    Diffusion Score Normalization With Graph Topology · holobiomicslab

    Use when after propagating diffusion scores through the FELLA metabolite network using set.diffusion, when raw diffusion scores would otherwise favor candidates in high-degree regions or penalize those in sparse regions. Use this normalization when building the final ranked annotation table (Ranked.
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71. ▌
    False Discovery Rate Control In Spectral Matching · holobiomicslab

    Use when after performing spectral library matching (whether unmodified or open modification search) and ranking candidate matches by similarity score, apply FDR control when you need to report a curated set of identifications with quantified confidence.
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72. ▌
    High Resolution Mass Spectrometry Data Processing · holobiomicslab

    Use when you have uploaded a delimited data file (CSV, TSV, or semicolon-separated) containing at least m/z values and intensity columns from HRMS analysis of a complex sample, and you need to (1) verify that column keywords are correctly recognized, (2) compute derived metrics such as Normalized.
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73. ▌
    Mass Spectrometry Feature Intensity Normalization · holobiomicslab

    Use when after aligning raw mass spectra into a 2D feature intensity matrix (features × time points) but before computing pointwise correlation to identify feature pairs with matching temporal profiles.
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74. ▌
    Mass Spectrometry Imaging Line Scan Data Handling · holobiomicslab

    Use when you have raw line-scan MSI data from a vendor instrument (Agilent, Bruker, Thermo, or open-source .mzML format) and need to extract ion images for specified m/z targets with spatial binning and tolerance-based filtering.
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75. ▌
    Mass Spectrometry Instrument Format Compatibility · holobiomicslab

    Use when you have raw mass spectrometry data from an instrument not yet validated in your pipeline (e.
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76. ▌
    Meta Analysis Result Visualization Interpretation · holobiomicslab

    Use when after computing a weighted meta-analysis combining p-values, fold-changes, and study sizes across multiple metabolomics studies, you need to identify which metabolites show both statistical significance (p ≤ 0.05) and biological meaningfulness (fold-change ≥ 3.
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77. ▌
    Metabolite Count Normalization And Transformation · holobiomicslab

    Use when you have a raw metabolite count data frame (e.g., c57_nos2KO_mouse_countDF) and need to prepare it for univariate statistical testing (e.g., omu_summary with log_transform=TRUE), multivariate analysis (e.g., PCA), or visualization.
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78. ▌
    Missing Value Imputation With K Nearest Neighbour · holobiomicslab

    Use when you have log-transformed metabolomics data in SummarizedExperiment format organized as multiple batches, have already filtered metabolites with >50% missing values per batch and retained only those quantified across all batches via intersect method, and now need to impute remaining missing.
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79. ▌
    Molecular Formula Representation And Manipulation · holobiomicslab

    Use when you need to represent, validate, and manipulate molecular compositions in MS analysis—specifically when annotating precursor or product ions with elemental formulas, computing monoisotopic masses for formula-to-charge assignments, predicting isotope patterns ([M]+, [M+H]+, [M+Na]+, etc.).
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80. ▌
    Multivariate Metabolomic Dimensionality Reduction · holobiomicslab

    Use when when you have a metabolite-pathway association table and per-sample metabolite abundance data (e.
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81. ▌
    Probabilistic Classification Network Construction · holobiomicslab

    Use when when you have raw mass spectrometry imaging data tensors and need to build a trainable deep-learning classifier that outputs class probabilities (tumor vs. non-tumor) without preprocessing or manual peak detection.
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82. ▌
    Sequencing Adapter Trimming And Quality Filtering · holobiomicslab

    Use when after receiving raw FASTQ files from SRA or local sequencing input, and after FastQC has identified adapter content and quality issues.
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83. ▌
    Spatial Coordinate Registration Across Modalities · holobiomicslab

    Use when when you have preprocessed spatial transcriptomics (ST) and spatial metabolomics (SM) datasets in AnnData format with spatial coordinates, and need to align them to a common resolution before joint downstream analysis.
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84. ▌
    Species Candidate Ranking From Spectral Alignment · holobiomicslab

    Use when you have an unknown sample spectrum (m/z peaks and intensities from DI-MS, ASAP-MS, or other high-throughput mass spectrometry modalities) and a reference species database of known spectra, and you need to identify the most likely species or authenticate the sample by ranking how well each.
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85. ▌
    Statistical Hypothesis Testing Pvalue Computation · holobiomicslab

    Use when after log-transformation and missing-value imputation of a metabolomics featuredata matrix, when you have a design matrix encoding one or more factors of interest and need to test the statistical significance of each metabolite's association with those factors.
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86. ▌
    Unified API Design For Heterogeneous Data Sources · holobiomicslab

    Use when your analysis pipeline must ingest mass-spectrometry data from mixed vendor sources (e.g., Thermo RAW, Agilent .d, Waters .raw, and open mzML) without writing separate parser logic for each format.
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87. ▌
    Unsupervised Clustering In High Dimensional Space · holobiomicslab

    Use when you have a preprocessed feature matrix from metabolomics data and suspect unknown batch effects, hidden sample substructures, or latent groups not captured by experimental metadata.
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88. ▌
    XML Schema Configuration For Bioinformatics Tools · holobiomicslab

    Use when you have a working R package or bioinformatics pipeline (e.g., IonFlow for ionomics analysis) and need to wrap it as a Galaxy tool so that users can invoke it through Galaxy's web interface without direct command-line access. The inputs are tabular datasets (e.
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89. ▌
    Hatch Environment Configuration And Activation · holobiomicslab

    Use when you have cloned a Python project (e.g., scverse/scanpy) that includes a hatch.toml configuration file and need to set up a consistent development or testing environment.
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90. ▌
    Transcript Quantification Benchmark Comparison · holobiomicslab

    Use when you have two implementations of the same quantification method (or major versions) and observe a persistent disagreement in mapped-read counts, per-read alignment agreement, or abundance correlations on the same reference index and read set.
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91. ▌
    Feature Intensity Normalization And Batch Correction · holobiomicslab

    Use when after blank masking and sample dropping, when you have a feature table with intensity values that exhibit systematic variation across sample collection batches or instrument runs.
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92. ▌
    Molecular Fingerprint Structural Similarity Tanimoto · holobiomicslab

    Use when when evaluating how well mass spectral similarity scores correlate with actual chemical structure for annotated spectral pairs (e.g., spectra with InChIKey metadata).
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93. ▌
    Bayesian Optimization Acquisition Function Selection · holobiomicslab

    Use when after fitting a Gaussian Process regression model to prior LC-MS gradient evaluations (where gradients are encoded as input and separation efficiency is output), use this skill to decide which candidate gradient to test next.
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94. ▌
    Correlation Coefficient Computation For Metabolomics · holobiomicslab

    Use when you have an aligned LCMS feature table (output from Eclipse or equivalent alignment tool) and need to identify candidate redundant features for clustering. It is most useful when features are suspected to originate from the same metabolite (e.
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95. ▌
    Data Dependent Acquisition Controller Implementation · holobiomicslab

    Use when you have a conceptual MS/MS fragmentation strategy (e.
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96. ▌
    Evaluation Data Collection For Simulation Assessment · holobiomicslab

    Use when you are developing or comparing new data-dependent acquisition (DDA) strategies in ViMMS and need to evaluate how well each strategy fragments sampled compounds from the HMDB database.
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97. ▌
    High Resolution Mass Spectrometry Data Preprocessing · holobiomicslab

    Use when you have acquired high-resolution MS/MS spectra in mzML, mzXML, or MGF format and need to prepare them for large-scale clustering or similarity searching.
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98. ▌
    Mass Spectrometry Peak Filtering And Noise Reduction · holobiomicslab

    Use when you have raw MS/MS spectra in MGF, mzML, or msp format and need to prepare them for mass2motif discovery or topic modeling.
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99. ▌
    Metabolic Feature Identification From Tandem Spectra · holobiomicslab

    Use when when processing data with available MS2 spectra (DDA acquisition) after MS1 peak picking has been completed, and you seek to identify additional metabolic features or validate existing peak picking results through MS2 recognition.
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100. ▌
     Metabolite Abundance Normalization Across Conditions · holobiomicslab

     Use when you have intracellular metabolomics abundance data (measured metabolite concentrations) from multiple biological replicates collected from two or more cell lines or conditions, and you need to create a normalized, cell-line-level metabolite dataset before computing Reaction Propensity.
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