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HolobiomicsLab

@holobiomicslab source repo

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1. ▌
   Cohort Stratified Metabolic Performance Analysis 2 · holobiomicslab

   Use when when you have uploaded a pre-analytical data table containing sample metadata, processing delay annotations (pre- and post-centrifugation times), and paired NMR metabolomic measurements for a plasma or serum cohort, and you need to determine how processing delays impact metabolite.
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   Composite Mass Track Assembly And Peak Detection 2 · holobiomicslab

   Use when after mass tracks have been aligned across all samples into a MassGrid (via sample-wise or centroid-based alignment), you have a unified set of m/z features tracked across the entire study.
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3. ▌
   Confidence Score Extraction From Neural Networks 2 · holobiomicslab

   Use when when you have a trained deep learning model (e.g., PS2MS) and an evaluation dataset of compounds, and you need to assess how prediction confidence varies across structural novelty classes (e.g., training-similar vs. structurally novel NPS analogues).
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4. ▌
   Dataset Object Serialization And Deserialization 2 · holobiomicslab

   Use when you have mass spectrometry data arriving through heterogeneous input formats (Task ID from GNPS, Universal Spectrum Identifiers, or Feature-Based Molecular Networking identifiers) and need to load, validate, and store them as a single standardized dataset object for interactive peak.
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5. ▌
   Empirical Compound Annotation And Representation 2 · holobiomicslab

   Use when you have a tab-delimited feature table (m/z, retention time, intensities) from LC-MS preprocessing and need to group related ions (isotopologues, adducts, in-source fragments) into compound-level annotations with inferred neutral mass.
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6. ▌
   Mass Spectrometry Reference Database Integration 2 · holobiomicslab

   Use when you have individual MS/MS spectra or batch .mgf files from untargeted metabolomics experiments and need to search them against domain-specific reference libraries (e.
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7. ▌
   Quantum Chemistry Based Fragmentation Prediction 2 · holobiomicslab

   Use when when you have SMILES strings or molecular formulae for N-Me derivatized unsaturated sterol lipids and need to generate theoretical MS/MS spectra (predicted fragment m/z values and intensities) to compare against experimental LC-IM-MS/MS data before performing CCS prediction or downstream.
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8. ▌
   Retention Time Calibration Via Lowess Regression 2 · holobiomicslab

   Use when after mass track extraction and alignment across samples, when preparing to detect elution peaks on composite mass tracks. Use this when inter-sample retention time variation exceeds acceptable alignment tolerance (e.
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9. ▌
   Sample Study Size Stratified Algorithm Selection 2 · holobiomicslab

   Use when when beginning mass alignment in a multi-sample LC-MS metabolomics study, before constructing the MassGrid.
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10. ▌
    Spectral Tensor Representation And Preprocessing 2 · holobiomicslab

    Use when when preparing MS/MS spectra from .msp files for transformer-based deep learning models in IDSL_MINT. Specifically: you have raw spectral data with variable peak counts and need fixed-size tensor inputs;
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11. ▌
    Statistical Distribution Analysis Across Cohorts 2 · holobiomicslab

    Use when when you have prediction scores (softmax probabilities, uncertainties) from a trained deep learning model evaluated on a heterogeneous dataset and you need to determine whether prediction confidence or accuracy varies systematically across structurally distinct or novel compound.
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12. ▌
    Systematic Mass Calibration And Drift Correction 2 · holobiomicslab

    Use when when processing multiple LC-MS samples in a cohort study and MassGrid construction reveals that anchor mass tracks (13C/12C isotope or Na/H adduct pairs) in non-reference samples deviate systematically from the reference sample's m/z values by >1 ppm.
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13. ▌
    Transition List Formatting For Targeted Analysis 2 · holobiomicslab

    Use when you have generated or assembled a lipid spectral library with precursor m/z values, adduct information, and fragmentation patterns, and you need to import those spectra into Skyline for targeted data-independent or parallel-reaction-monitoring (PRM) analysis.
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14. ▌
    Within Batch Randomization By Metadata Attribute 2 · holobiomicslab

    Use when you have already assigned samples to batches (inter-batch balance is fixed) and need to shuffle injection order within each batch to decorrelate sample properties from time-dependent instrumental effects. Use it when your metadata table includes a randomization dimension (e.
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15. ▌
    Feature Property Refinement From Training Data 2 · holobiomicslab

    Use when you have a set of training LC-HRMS chromatograms (retention time × m/z matrix format) and a manually curated reference list of isolated single chromatographic peaks, and you need to update the reference peak properties (retention time, m/z, peak shape) to match the actual peak signatures.
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16. ▌
    Marker Feature Visualization Retention Time Mz 2 · holobiomicslab

    Use when after NPFimg's automated detection algorithm has identified marker features from a two-dimensional MS map (m/z vs retention time), especially when you need to validate feature positions, inspect co-localization patterns, or communicate results to stakeholders.
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17. ▌
    Redundant Node Removal And Edge Classification 2 · holobiomicslab

    Use when after network partitioning, when you have identified connected subnetworks of features matched by isotope or adduct patterns and need to sanitize and categorize the relationships before tree construction. Use it when redundant features (e.
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18. ▌
    Reproducibility Signal Detection Nonparametric 2 · holobiomicslab

    Use when when you have high-dimensional replicate experimental data (e.g., metabolomics, proteomics, genomics assays) where technical or biological variability threatens reproducibility, and you need to distinguish genuine reproducible signals from noise without assuming normality.
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19. ▌
    Msi Metadata Harmonization Across Image Stacks 2 · holobiomicslab

    Use when you have processed and quantified MSI data from one or more imzML files in LipidQMap and need to export them as a unified, standards-compliant HDF5 container that preserves feature-by-pixel intensity matrices, per-feature lipid annotations (m/z, lipid class, adduct, neutral ID), per-pixel.
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20. ▌
    Peak Intensity Normalization Weighted Aggregation 2 · holobiomicslab

    Use when when training Word2Vec embeddings on mass spectra represented as peak-word documents, and you need to preserve the quantitative intensity relationships between fragments without allowing a single dominant peak to overwhelm the learned word associations.
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21. ▌
    Bayesian Meta Learning Chromatographic Projection 2 · holobiomicslab

    Use when you have experimental retention times (RTs) for a small set of calibration molecules (≥10) measured on both a source chromatographic method and a target method, and you need to predict RTs for candidate metabolites on the target method to rank annotation candidates.
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22. ▌
    Biotransformation Rule Application To Metabolites 2 · holobiomicslab

    Use when you have untargeted metabolomics data with unknown or ambiguous molecular identities, anchor metabolites (known structures in SMILES or MOL format), and a curated database of biotransformation rules (e.g., from KEGG, RetroRules, or domain-specific repositories).
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23. ▌
    Computational Resource Profiling And Benchmarking 2 · holobiomicslab

    Use when when evaluating a new or updated version of a data processing tool (especially asari or similar LC-MS workflows) before production deployment, or when verifying claims about scalability, memory efficiency, or throughput on specific hardware classes (e.g., ≤16 GB RAM single-core systems).
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24. ▌
    Ion Mobility Mass Spectrometry Data Preprocessing 2 · holobiomicslab

    Use when when you have raw IM-MS data from drift tube (DT) or SLIM instruments in Agilent MassHunter (.
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25. ▌
    Mass Spectrometry Annotation Engine Customization 2 · holobiomicslab

    Use when when you have baseline MS/MS peak annotations from a known compound but need to refine them using newly available structural information (e.
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26. ▌
    Metabolomics Database Search And Formula Matching 2 · holobiomicslab

    Use when after you have detected LC-MS features, grouped them into empirical compounds via isotope and adduct clustering (using khipu), and have accurate m/z and retention time values.
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27. ▌
    Nearest Neighbor Clustering For Mass Spectrometry 2 · holobiomicslab

    Use when you have extracted mass tracks (EICs) from individual samples at 0.001 amu m/z resolution and need to align them into a composite mass grid for feature detection.
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28. ▌
    Internal Standardization Correction Lipidomes 2 · holobiomicslab

    Use when you have IM-MS lipidomics data from samples spiked with U13C-labeled internal standards (fully labeled yeast extract) and measured CCS values need bias assessment or correction.
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29. ▌
    Machine Learning Based Conformation Filtering 2 · holobiomicslab

    Use when when you have generated multiple 3D conformations for a molecule or set of ionized adducts (e.g., via RDKit) and need to retain only the most energetically favorable structures before expensive quantum calculations.
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30. ▌
    Preprocessing Pipeline Parameter Optimization 2 · holobiomicslab

    Use when you have raw TOF-MS or IM-MS data in Agilent MassHunter (.d) or UIMF format with jagged peaks and low-abundance ions that require signal enhancement, but you need to decide whether to apply smoothing, and at what strength, to avoid over-smoothing real signals or under-removing artifacts.
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31. ▌
    Signal Quality Enhancement Low Abundance Ions 2 · holobiomicslab

    Use when you observe jagged or noisy peak profiles in low-abundance ions after loading raw IM-MS data (Agilent MassHunter .
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32. ▌
    Chemical Formula Deduplication Across Databases 2 · holobiomicslab

    Use when you have retrieved chemical formulae and metadata from two or more of HMDB, ChEMBL, or PubChem and need to merge them into a single searchable database without formula duplication.
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33. ▌
    Chromatographic Peak Detection Gradient Descent 2 · holobiomicslab

    Use when you have LC-HRMS profile-mode data (retention time × m/z matrix format) and need to automatically identify chromatographic peak locations and boundaries prior to feature extraction, reference matching, or training a peak-classification CNN model.
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34. ▌
    Coordinate System Normalization 1based Indexing 2 · holobiomicslab

    Use when when exporting quantified ion images and pixel metadata from LipidQMap to HDF5 format for use in downstream Cardinal or other MSI analysis workflows.
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35. ▌
    Internal Standard Ion Selection And Application 2 · holobiomicslab

    Use when after isotope correction has been applied to MSI ion images, when you have sprayed or identified a reference lipid standard of known amount (pmol/mm²) and need to normalize target lipid intensities against this standard to remove matrix effects and enable cross-pixel and cross-sample.
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36. ▌
    Spatial Transcriptome Metabolome Coregistration 2 · holobiomicslab

    Use when you have paired spatial transcriptome and metabolome datasets (both as h5ad files with .obsm['spatial'] coordinate matrices and .
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37. ▌
    Structure Organism Pair Counting And Deduplication 2 · holobiomicslab

    Use when when you have downloaded a curated structure-organism dataset (such as LOTUS) in TSV or CSV format with separate 2D and 3D structure-organism pair tables, and need to produce authoritative headline counts of unique referenced pairs, unique curated structures, unique organisms, and source.
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38. ▌
    Unknown Pollutant Identification Mass Spectrometry 2 · holobiomicslab

    Use when you have UPLC-HRMS raw data (ThermoFisher, Agilent, or compatible vendor format) from water samples or environmental matrices containing unknown organic pollutants, a Windows environment with ≥16 GB RAM and ≥2 GB NVIDIA GPU, and you need compound identification with confidence scores and.
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39. ▌
    Visual Pattern Recognition In Spectral Data 2 · holobiomicslab

    Use when after database search algorithms have scored unknown MS samples against reference species, and you need to visually inspect and confirm species assignments or identify ambiguous classifications.
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40. ▌
    Ion Mobility Mass Spectrometry Data Processing 3 · holobiomicslab

    Use when you have IM-MS lipidomics samples spiked with U13C-labeled internal standards (e.
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41. ▌
    Peak Apex Identification From Intensity Profiles 2 · holobiomicslab

    Use when after EIC candidate generation and peak detection have been completed on LC/HRMS data, when you need to extract the retention time and intensity values at peak maxima for each detected peak.
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42. ▌
    Reference Peak Matching Retention Time Alignment 2 · holobiomicslab

    Use when you have training LC-HRMS chromatograms (rt × m/z matrix format) from which you have already extracted peak candidates using smoothing and gradient-descent peak detection, and you possess a curated reference list of isolated single chromatographic peaks (ground truth).
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43. ▌
    Reproducibility Statistic Computation Rank Based 2 · holobiomicslab

    Use when you have high-dimensional replicate experiment data (metabolomics, proteomics, or genomics) with multiple biological or technical replicates per sample, and you need to assess which features are reproducible across replicates and which sample pairs show consistent reproducibility patterns.
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44. ▌
    Dimension Scale Linking And Cross Group Indexing 2 · holobiomicslab

    Use when when exporting quantified MSI data as HDF5 containers following the Cardinal::HDF5 layout convention, and you need to establish bidirectional indexing between intensity data (feature-by-pixel matrix) and metadata groups (featureData, pixelData).
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45. ▌
    Single Cell Spatial Metabolomics Data Processing 2 · holobiomicslab

    Use when you have raw IMC and SIMS image data from the same tissue region(s) and need to: (1) register the two modalities spatially, (2) segment individual cells across both images, (3) extract per-cell protein and metabolite intensity vectors, and (4) prepare the data for downstream joint analysis.
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46. ▌
    CI CD Workflow Adaptation To Organization Standards 2 · holobiomicslab

    Use when when a Python package is being relocated to a new GitHub organization (e.
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47. ▌
    Collision Cross Section Measurement Quality Control 2 · holobiomicslab

    Use when you have IM-MS lipidomics data from samples spiked with U13C-labeled internal standards (fully labeled yeast extract) and you need to quantify whether measured CCS values deviate systematically from theoretical values, or when you want to correct CCS measurements before downstream lipid.
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48. ▌
    Feature Network Construction From Mass Spectrometry 2 · 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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49. ▌
    Metabolite Identification From Transformation Rules 2 · holobiomicslab

    Use when when you have a small-molecule structure (SMILES, MOL, or SDF format) and need to identify probable metabolites or degradation products in a specific biological compartment (e.g., soil/aquatic microbiota, mammalian liver, or gut microbiota).
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50. ▌
    Principal Component Extraction From Pathway Subsets 2 · holobiomicslab

    Use when you have a log2-normalized, zero-mean and unit-variance standardized intensity matrix of metabolite features (rows=metabolites, columns=samples) and need to compute a single activity score per pathway that reflects the coordinated expression behavior of all metabolites assigned to that.
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51. ▌
    Scalability Extrapolation And Throughput Estimation 2 · 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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52. ▌
    Smarts Pattern Matching For Chemical Transformation 2 · 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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53. ▌
    Structural Similarity Calculation Fingerprint Based 2 · holobiomicslab

    Use when you have a set of compounds (e.g., novel NPS analogues in an evaluation dataset) and need to classify them as structurally similar to or divergent from a reference set (e.g., training compounds).
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54. ▌
    Spectral Data Compression By Frame And Mobility 2 · 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 need to reduce data volume while preserving signal integrity for subsequent HRdm demultiplexing and peak deconvolution.
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55. ▌
    Probabilistic Classification Network Construction 2 · 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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56. ▌
    Annotation Accuracy And Coverage Metrics Computation 2 · holobiomicslab

    Use when after executing an end-to-end structure annotation pipeline (such as BAM) on a validation dataset with known reference annotations.
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57. ▌
    Bayesian Optimization Acquisition Function Selection 2 · 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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58. ▌
    Metabolite Phenotype Association Partial Correlation 2 · holobiomicslab

    Use when you have a SummarizedExperiment object containing NMR or MS metabolomic data with aligned phenotype information (BMI, disease status, age, gender), and you need to identify metabolites associated with a continuous or categorical outcome while controlling for known confounders that might.
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59. ▌
    One Hot Encoding Categorical Chromatography Features 2 · holobiomicslab

    Use when you have raw HPLC column metadata containing categorical fields (e.g., column manufacturer 'Waters', USP type 'L1', solvent identities 'H2O'/'MeOH'/'ACN') that must be converted into numerical representations before featurization for a machine learning pipeline.
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60. ▌
    Precision Recall Optimization In Spectral Annotation 2 · holobiomicslab

    Use when you have extracted fragmentation patterns from a collection of MS/MS spectra (using mineMS2) and have partitioned spectra into components via GNPS molecular networking (e.g., connected components, cliques, or high-similarity pairs with cosine > threshold).
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61. ▌
    Mass Spectrometry Feature Similarity Modeling 2 · holobiomicslab

    Use when when you have an untargeted metabolomics dataset with MS2 fragmentation spectra and need to annotate metabolites beyond what reference databases alone provide.
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62. ▌
    Data Quality Assessment From Molecular Descriptors 2 · holobiomicslab

    Use when processing spectral datasets from open mass spectra libraries (OMSLs) where structural identifiers and ionization metadata are incomplete or inconsistent.
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63. ▌
    Spectrum Document Conversion Peak Loss Representation 2 · holobiomicslab

    Use when when preparing MS/MS spectral data for training word-embedding models (Word2Vec, Skip-gram, CBOW) that will learn relationships between fragment ions and neutral losses.
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64. ▌
    Automated Peak Detection Without Conventional Picking 2 · holobiomicslab

    Use when you have a two-dimensional GC–MS or LC–MS dataset (m/z vs retention time) and need to identify discriminative analyte features without relying on conventional peak picking algorithms. This is especially valuable when analyzing complex, low-abundance samples (e.
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65. ▌
    Comparative Algorithm Benchmarking For Peak Detection 2 · holobiomicslab

    Use when you have developed or adapted a peak detection method for chromatography–mass spectrometry and need to validate its reliability against an established baseline on the same raw GC–MS dataset. Specifically when: (1) the input is raw GC–MS data in m/z vs retention time format;
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66. ▌
    Machine Learning Model Training With Cross Validation 2 · holobiomicslab

    Use when when you have a labeled peak quality matrix (with known pass/fail labels), need to objectively compare performance across multiple classification algorithms (e.
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67. ▌
    Peak Evaluation Metrics Cselectivity Snr Gaussian Fit 2 · holobiomicslab

    Use when after scipy.signal.find_peaks has identified candidate peaks on a composite mass track segment, evaluate each peak to decide whether to retain it in the final feature table.
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68. ▌
    Ppb Level Marker Detection And Sensitivity Assessment 2 · holobiomicslab

    Use when you have raw GC–MS or LC–MS data in two-dimensional m/z vs retention time format and need to identify marker features at parts-per-billion sensitivity without relying on conventional peak picking.
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69. ▌
    Relative Standard Deviation Calculation Qc Replicates 2 · holobiomicslab

    Use when you have a peak table from XCMS preprocessing with intensity measurements for the same set of metabolites across multiple QC replicate injections (samples marked SampleType='LQC'), and you need to filter out EICs with poor reproducibility before evaluating peak quality or training a.
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70. ▌
    Spectral Dataset Partitioning And Train Test Curation 2 · holobiomicslab

    Use when when you have a pre-cleaned spectral library (e.g., GNPS, MoNA, or MTBLS1572) with an existing training/test boundary established by prior work (e.g., MSBERT), and you need to report model performance with uncertainty quantification across multiple random partitions.
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71. ▌
    Multi Mode Filter Application High Dimensional Data 2 · holobiomicslab

    Use when you have high-dimensional biological data (e.
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72. ▌
    Word2vec Vocabulary Matching And Unknown Peak Handling 2 · holobiomicslab

    Use when converting MS/MS spectra into Spec2Vec embeddings using a pre-trained Word2Vec model that was trained on reference data (e.g., a subset of GNPS or MassBank).
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73. ▌
    Categorical Numerical Feature Concatenation For Graphs 2 · holobiomicslab

    Use when when preparing heterogeneous column-metadata inputs for a graph transformer model that operates on molecular graphs. Specifically: (1) you have both categorical metadata (e.
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74. ▌
    Chromatographic Peak Detection With Prominence Control 2 · holobiomicslab

    Use when after constructing baseline-corrected mass tracks (either composite across samples or per-sample) when you need to identify individual chromatographic peaks for feature extraction in LC-MS or GC-MS metabolomics workflows.
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75. ▌
    Connected Component Decomposition In Mass Spectrometry 2 · holobiomicslab

    Use when you have a feature list from LC-MS preprocessing (e.g., asari output) and have already identified all pairwise feature matches to isotope and adduct patterns. Apply this skill when you need to separate feature matches into disjoint empirical compounds—i.
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76. ▌
    Metabolomics Functional Prediction Workflow Validation 2 · holobiomicslab

    Use when a Python-based metabolomics analysis package has been relocated to a new GitHub organization (e.g., metabolomics-cloud) and you need to confirm that the migration preserved package integrity, installation, and runtime correctness.
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77. ▌
    Molecular Geometry Neural Network Potential Evaluation 2 · holobiomicslab

    Use when after RDKit has generated multiple conformations for a molecule in an SDF or XYZ format, and you need to reduce the conformational ensemble to a tractable size (by energy-based ranking) before submitting to expensive quantum-chemical calculations (e.g., QUICK).
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78. ▌
    Peak Quality Assessment By Selectivity And Snr Metrics 2 · holobiomicslab

    Use when after elution peaks have been detected on composite mass tracks using local maxima and prominence thresholds, and before mapping detected features back to individual samples or performing pre-annotation.
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79. ▌
    Separation Efficiency Calculation From Retention Times 2 · holobiomicslab

    Use when you have extracted retention times from MS1 spectra for top signals in a single LC-MS/MS run and need to evaluate whether that gradient's separation performance is sufficient, or when you are building the objective function for an iterative gradient optimization loop where each candidate.
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80. ▌
    Biotransformation Candidate Integration With Networking 2 · holobiomicslab

    Use when you have output from a biotransformation rules module (candidate transformed structures linked to anchor molecules) and untargeted MS/MS spectral data, and you want to identify molecular families and annotate features with predicted structures by leveraging spectral similarity and network.
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81. ▌
    Biotransformation Prediction Across Microbiota Contexts 2 · holobiomicslab

    Use when you have one or more small-molecule chemical structures (as SMILES, MOL, or SDF) and need to systematically explore their fate across mammalian biotransformation, human gut microbial degradation, or environmental (soil/aquatic) microbial degradation.
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82. ▌
    Ion Tree Structure Optimization And Trunk Establishment 2 · holobiomicslab

    Use when after you have partitioned a feature network into connected subnetworks (each containing ion features linked by isotope or adduct mass differences).
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83. ▌
    Sample Batch Balancing Across Classification Dimensions 2 · holobiomicslab

    Use when designing multi-batch LC/GC-MS experiments where samples belong to multiple groups or conditions and you need to ensure that each injection plate receives a balanced representation of all groups.
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84. ▌
    Species Authentication Classification Evaluation 2 · holobiomicslab

    Use when when you have high-throughput mass spectrometry data (DI-MS, ASAP-MS, LDI-MS, or other ambient ionization formats) from unknown biological samples and need to determine their species identity against a curated reference database.
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85. ▌
    Raw Spectral Data Input Handling Without Peak Picking 2 · holobiomicslab

    Use when when you have raw mass spectrometry imaging data (full m/z profiles with intensity arrays) and want to classify spatial regions (e.
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86. ▌
    Retention Time Projection Across Chromatographic Methods 2 · holobiomicslab

    Use when when you have retention times measured on one chromatographic method and need to predict or map them to another method with minimal or no overlap in measured molecules.
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87. ▌
    Tree Structure Optimization For Metabolite Deconvolution 2 · holobiomicslab

    Use when you have a connected subnetwork of LC-MS features that matched isotope or adduct patterns, and you need to establish a canonical tree representation with a single neutral mass assignment.
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88. ▌
    Mass Spectrometry Instrument Format Compatibility 2 · holobiomicslab

    Use when you have raw mass spectrometry data from an instrument not yet validated in your pipeline (e.
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89. ▌
    Species Candidate Ranking From Spectral Alignment 2 · 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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90. ▌
    Mobility Dimension Interpolation For Peak Resolution 2 · holobiomicslab

    Use when working with raw multiplexed IM-MS data (UIMF or Agilent MassHunter .
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91. ▌
    Plant Species Authentication Via Mass Spectrometry 2 · holobiomicslab

    Use when when you have mass spectrometry raw data (DI-MS or ASAP-MS format) from plant samples that are easily confused due to morphological similarity, or when you need to verify or authenticate the species identity of a plant material against a reference database.
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92. ▌
    Computational Efficiency Single Pass Vs Repeated Detecti 2 · holobiomicslab

    Use when when processing aligned LC-MS data across multiple samples where the computational bottleneck is repeated peak-detection algorithm calls (one per sample per m/z value). Typical scenario: >10 samples with >1000 m/z values each, where N individual find_peaks invocations dominate runtime.
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93. ▌
    Image Processing On Two Dimensional Mass Spectrometry Ma 2 · holobiomicslab

    Use when when you have raw GC–MS data in two-dimensional m/z × retention time format (NetCDF or proprietary binary) and need to identify marker features across aroma or breath samples at parts-per-billion concentration levels, particularly when conventional peak picking introduces false positives.
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94. ▌
    Mass Spectrometry Feature Extraction From Cardinal Objec 2 · holobiomicslab

    Use when you have a Cardinal MSImagingExperiment object (e.g., from imzML or Analyze 7.5 files) and need to retrieve the complete set of m/z values and their intensities for annotation against metabolite databases (HMDB, Lipidmaps) or for statistical analysis.
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95. ▌
    Metabolite Cluster Identification From Correlated Featur 2 · holobiomicslab

    Use when after preprocessing, imputation, and batch correction of LC-MS peak tables when you need to group redundant or related feature measurements (e.g., [M+H]+ and [M+Na]+ adducts, or isotope peaks) into metabolite-level clusters before statistical testing or identification.
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96. ▌
    Software Performance Characterization And Scaling Analys 2 · holobiomicslab

    Use when when a tool claims to be 'scalable' or 'performance-conscious' but lacks published performance benchmarks, or when you need to confirm that runtime and memory scale linearly (or predictably) with sample count before deploying the tool on large LC-MS datasets (e.g., >100 samples).
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97. ▌
    Mass Spectrometry Peak Identification And Extraction 2 · holobiomicslab

    Use when when you have raw mass spectrometry data from direct-infusion (DI-MS) or ambient surface analysis probe (ASAP-MS) instruments and need to identify which m/z peaks are biologically or chemically informative for sample classification, rather than processing the entire spectrum including.
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98. ▌
    Baseline And Noise Level Estimation From Quartile Statis 2 · holobiomicslab

    Use when before peak detection on a composite or individual mass track when you need to filter out low-intensity noise and baseline drift without removing true signal.
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99. ▌
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100. ▌
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