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HolobiomicsLab

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1. ▌
   Contrastive Learning Objective Formulation · holobiomicslab

   Use when when pre-training a graph neural network on a domain-specific molecular corpus (natural products vs. synthetic molecules) where you need to capture both evolutionary relationships encoded in molecular scaffolds and diverse structural variations in side-chains, and when supervised learning.
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   Core Node Identification From Perturbation · holobiomicslab

   Use when you have a directed metabolic network (digraph) with node perturbation data (e.g., from metabolomics fold-changes or experimental treatment effects) and you need to identify which nodes are core drivers of the observed perturbation.
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   Data Lineage Preservation In Etl Pipelines · holobiomicslab

   Use when when consolidating entries from multiple heterogeneous source databases into a unified table, and you need to maintain auditable connections between final curated records and their original source entries.
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   Decoy Database Generation For Metabolomics · holobiomicslab

   Use when performing large-scale untargeted metabolomics annotation where you need to estimate the false discovery rate of metabolite identifications.
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5. ▌
   Deep Learning Model Input Layer Adaptation · holobiomicslab

   Use when you have a trained deep-learning model (e.g., MSNovelist) that depends on a specific fingerprint format (e.g., SIRIUS 6 fingerprints) as input, but you want to substitute an alternative fingerprint prediction system (e.
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   Differential Metabolite Abundance Analysis · holobiomicslab

   Use when you have raw metabolomics data structured as a matrix with metabolites as rows and samples as columns, samples are annotated with group labels (e.
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7. ▌
   Dotnet Framework Compilation In Containers · holobiomicslab

   Use when you have a C# GUI application targeting .NET Framework 4.8 (Windows-only) and need to execute it on macOS or Linux hosts without modifying the source code. The application requires compilation from source and GUI display support via X11 forwarding or headless CLI execution.
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8. ▌
   Environmental Degradation Pathway Analysis · holobiomicslab

   Use when you have a target compound (in SMILES or structure format) and need to predict what metabolites will form via environmental microbial biotransformation, including the reaction types and parent–product relationships.
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9. ▌
   Evolutionary Pattern Encoding In Molecules · holobiomicslab

   Use when when building or fine-tuning a molecular representation model intended for natural product mining, taxonomy classification, or bioactivity prediction, and you have access to natural product SMILES data with scaffold and side-chain structural annotations.
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10. ▌
    Experimental Predicted Fragment Comparison · holobiomicslab

    Use when when you have downloaded or retrieved fragment records from both experimental and predicted fragment databases as part of the DNA adductomics resource, and you need to verify that all fragments can be successfully mapped to their parent compound entries in the reference SDF-format compound.
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11. ▌
    False Discovery Rate Estimation Untargeted · holobiomicslab

    Use when performing untargeted metabolomics annotation (i.e., matching observed spectra to a compound database without a pre-defined target list) and you need to assign statistical significance or confidence to candidate metabolite identifications.
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12. ▌
    Fragment Record Cross Reference Validation · holobiomicslab

    Use when you have downloaded fragment records from separate experimental and predicted fragment databases and need to verify that every fragment can be traced back to a valid compound entry in the reference SDF-format compound database, particularly when integrating multiple database sources into a.
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13. ▌
    Genome Scale Model Validation And Curation · holobiomicslab

    Use when you have multiple draft genome-scale metabolic reconstructions in standard formats (JSON, XML, SBML) representing individual community members or assembly variants, and you need to verify they are structurally sound and consistently annotated before merging them into a consensus model or.
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14. ▌
    Ggplot2 Multivariate Scatter Visualization · holobiomicslab

    Use when after performing PCA (or other dimensionality reduction) on a metabolite matrix, when you need to visualize whether batch effects are present in uncorrected data, or whether a batch correction method (e.
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15. ▌
    Hierarchical Clustering Dendrogram Cutting · holobiomicslab

    Use when after XCMS feature detection, grouping, retention time correction, and missing value filling have produced an aligned feature matrix, when you need to group features (m/z, retention time pairs) that likely originate from the same metabolite.
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16. ▌
    Hierarchical Metabolite Annotation Mapping · holobiomicslab

    Use when you have a metabolomics count data frame with metabolite identifiers (e.g., compound IDs, KEGG IDs) and wish to organize them into functional or taxonomic hierarchies for class-based statistical testing, fold-change stratification, or visualization by metabolic pathway or organism type.
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17. ▌
    Imputation Performance Metrics Calculation · holobiomicslab

    Use when when you have imputed a metabolomics dataset using multiple MNAR (missing not at random) imputation methods and possess the ground-truth values (from simulation or manual retrieval).
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18. ▌
    Ion Image Augmentation Intensity Dependent · holobiomicslab

    Use when training a contrastive encoder on mass spectrometry imaging (MSI) data in ISO mode (isotope ions from the same molecule).
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19. ▌
    Isoform Sequence Extraction And Formatting · holobiomicslab

    Use when after completing differential isoform expression analysis using IsoformSwitchAnalyzer or equivalent isoform quantification, when you have identified sets of differentially expressed isoforms and need to perform functional characterization through sequence-based homology and structural.
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20. ▌
    Iterative Peak Selection From Latent Space · holobiomicslab

    Use when you have latent low-dimension peak features extracted by a Graph-attention autoencoder from imaging mass spectrometry (IMS) datasets, and you need to automatically identify a ranked subset of marker ions without manual inspection.
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21. ▌
    M Z To Normalized Kendrick Mass Conversion · holobiomicslab

    Use when you have uploaded peak list data containing m/z values and wish to construct a Kendrick mass plot where alkane homolog series (or other homologous families) are expected to appear as horizontal lines.
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22. ▌
    Machine Learning Cross Validation Training · holobiomicslab

    Use when you have a labeled dataset (e.g., mass spectra with molecular structures, SIRIUS 6 fingerprint annotations) that you wish to train a supervised deep learning model on, and you need to estimate generalization performance and reduce variance from a single train–test split.
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23. ▌
    Machine Learning Hyperparameter Extraction · holobiomicslab

    Use when you have completed cross-validation tuning of one or more machine-learning models (e.g., AdaBoost, SVM, Random Forest) on a development dataset using the caret package and need to identify which specific hyperparameter values were selected as optimal.
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24. ▌
    Mass Spectrometry Data Matrix Construction · holobiomicslab

    Use when you have raw spatial metabolomics imzML files (paired with .ibd binary data files) that need to be loaded into a unified AnnData format for integration with spatial transcriptomics data or for cross-modal spatial pattern identification in single or multiple sample datasets.
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25. ▌
    Metabolic Model Merging Consensus Building · holobiomicslab

    Use when you have multiple draft metabolic reconstructions (in JSON, XML, or SBML format) representing individual members or assembly variants of a community and need a single, non-redundant consensus model that captures shared and community-supported metabolic capacity.
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26. ▌
    Metabolic Network Reconstruction From Kegg · holobiomicslab

    Use when you have selected two organisms (by KEGG code, e.
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27. ▌
    Metabolite Change Direction Categorization · holobiomicslab

    Use when when you have omu_summary output containing log2FoldChange values and adjusted p-values (padj) for metabolites, and you need to stratify them by direction of change within a specific compound class (e.g., organic acids, amino acids) at a defined significance threshold (typically padj ≤ 0.
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28. ▌
    Metabolite Disease Correlation Computation · holobiomicslab

    Use when after training a DeepMSProfiler deep learning model and generating per-sample predictions and metabolite signal intensities, use this skill when you need to: (1) identify which metabolites are most strongly associated with each disease class, (2) generate publication-ready visualizations.
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29. ▌
    Metabolite Feature Distribution Comparison · holobiomicslab

    Use when when metabolomics data contains both QC control samples and biological samples that will be normalized together using methods like tGAM, rGAM, rLOESS, QC-RLSC, or QC-RSC. Apply this skill to verify that QC and biological sample distributions remain consistent;
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30. ▌
    Metabolite Missingness Threshold Filtering · holobiomicslab

    Use when when you have a metabolite measurement matrix with missing values across samples and need to decide which metabolites to retain before applying k-nearest neighbor imputation.
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31. ▌
    Metabolite Sample Clustering Visualization · holobiomicslab

    Use when when you have a feature-by-sample metabolomic matrix (finalData) and corresponding sample group labels (finalLabel) and need to visualize how samples cluster together and separate by group using hierarchical clustering;
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32. ▌
    Metabolomic Workflow Ranking Visualization · holobiomicslab

    Use when after running NOREVA's multi-class or time-course assessment functions (normulticlassqcall, normulticlassnoall, normulticlassisall, nortimecourseqcall, or nortimecoursenoall) that produce an overall ranking CSV file of preprocessed workflows.
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33. ▌
    Missing Value Detection And Quantification · holobiomicslab

    Use when you have a raw abundance matrix (e.g., metabolite or gene features × samples) and need to decide which features to retain before downstream analysis.
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34. ▌
    Module Refactoring And Legacy Code Removal · holobiomicslab

    Use when when a major version release (e.g., v1.x → v2.0.0) deprecates a core neural module class, and new equivalent modules must be designed and integrated without breaking downstream prediction pipelines. Triggered by breaking changes in CHANGELOG or deprecation warnings in model initialization.
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35. ▌
    Molecular Descriptor Calculation Via Rdkit · holobiomicslab

    Use when when you have a set of SMILES strings representing small molecules and need to generate a unified descriptor feature matrix for downstream machine learning (e.g., retention time prediction, property regression).
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36. ▌
    Multi Class Performance Metric Computation · holobiomicslab

    Use when when evaluating a taxonomy classification model on held-out natural product data where multiple classes exist (e.
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37. ▌
    Multi Tool Spectral Compatibility Encoding · holobiomicslab

    Use when after RAMClustR clustering and do.findmain molecular weight inference are complete, when you need to submit the same inferred spectra to multiple third-party annotation tools (MSFinder and Sirius) that each require distinct file formats and cannot share a common intermediate representation.
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38. ▌
    Multivariate Feature Importance Extraction · holobiomicslab

    Use when you have a preprocessed peak table (feature matrix: samples × peaks) with known class labels or phenotype groupings, and you want to identify which individual peaks contribute most to classification or discrimination between groups.
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39. ▌
    Neural Network Hyperparameter Optimization · holobiomicslab

    Use when when you have preprocessed joint ST/SM AnnData objects (output from joint_adata_sm_st and normalize_total_joint_adata_sm_st) and need to fit a ConditionalVAESTSM model to unify spatial transcriptomics and spatial metabolomics data to a common resolution.
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40. ▌
    Parameter Flag Toggling For Software Modes · holobiomicslab

    Use when when integrating edited Modular.r scripts into the LipidMatch-4.2 distribution and you need to switch between Modular mode (standalone R execution with manual or CSV inputs) and Flow mode (integrated pipeline execution).
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41. ▌
    Pathway Enrichment Analysis Tool Selection · holobiomicslab

    Use when you have completed differential expression analysis (DEA) on genes, miRNA, proteins, or lipids and need to identify significantly enriched biological pathways. The multiOmicsIntegrator pipeline requires explicit tool selection via the pea_genes parameter in params.
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42. ▌
    Pathway Feature Selection Information Gain · holobiomicslab

    Use when after computing a pathway dysregulation score matrix (PDSmatrix) from metabolite-to-pathway mappings, apply this skill when you need to reduce the dimensionality of pathway features and select only those pathways with sufficient information content to discriminate between phenotype classes.
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43. ▌
    Performance Metric Aggregation And Display · holobiomicslab

    Use when after running NOREVA's assessment functions (normulticlassqcall, normulticlassnoall, normulticlassisall, nortimecourseqcall, nortimecoursenoall) on preprocessing workflows, use this skill to synthesize performance results across all five criteria into a single ranked output and create a.
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44. ▌
    Preprocessing Workflow Comparative Ranking · holobiomicslab

    Use when you have multi-class or time-course metabolomic peak table data (raw or already peak-detected) with or without quality control samples and/or internal standards, and you need to evaluate which preprocessing workflow (normalization, imputation, scaling combination) will yield the most.
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45. ▌
    Quality Control Sample Metadata Extraction · holobiomicslab

    Use when you have txt files exported from Sciex MultiQuant (>v3.0.
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46. ▌
    Quality Control Visualization Metabolomics · holobiomicslab

    Use when after completing kNN imputation, outlier sample removal, and variance-stabilizing normalization (vsn) on a MultiAssayExperiment object containing metabolite measurements.
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47. ▌
    Raw Spectral Data Import And Preprocessing · holobiomicslab

    Use when you have raw metabolomics data in mzML or mzXML format and need to convert it into a normalized feature table (CSV or mzTab) via automated batch processing.
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48. ▌
    Regression Model Evaluation Absolute Error · holobiomicslab

    Use when after training a regression model on labeled continuous data (e.g., retention times, physicochemical properties) and generating predictions on held-out test data, compute MAE and MedAE to assess model generalization and compare against published reference performance.
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49. ▌
    Retention Order Prediction Model Execution · holobiomicslab

    Use when you have access to the aalto-ics-kepaco/retention_order_prediction repository, have installed all Python (scipy, numpy, sklearn, joblib, pandas, networkx) and R dependencies, possess molecular feature data (MACCS fingerprints or equivalent), and need to execute a specific evaluation.
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50. ▌
    Risk Stratification And Prognosis Indexing · holobiomicslab

    Use when when you have paired survival data (event indicator and follow-up time vectors), expression or metabolomic feature matrices, and need to generate individual prognosis indices and assign samples to risk groups for downstream prognostic classification or treatment planning.
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51. ▌
    Robustness Metric Computation Metabolomics · holobiomicslab

    Use when when you have applied multiple normalization methods (e.g., tGAM, rGAM, rLOESS, QC-RLSC, QC-RSC) to metabolomics datasets and need to rank them by robustness and execution speed to determine which method is most suitable for your experimental design and computational constraints.
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52. ▌
    Search Result Visualization Across Domains · holobiomicslab

    Use when you have executed batch searches of MS/MS spectra against multiple domain-specific MASST indices and need to synthesize results across domains (e.
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53. ▌
    Sequence Motif Identification And Matching · holobiomicslab

    Use when you have differentially expressed isoform or exon FASTA sequences and need to identify conserved regulatory or structural motifs as part of comprehensive functional annotation.
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54. ▌
    Siamese Neural Network Architecture Design · holobiomicslab

    Use when when you have pairs of mass spectrometry spectra and need to predict their molecular structural similarity as a scalar Tanimoto score in the range [0, 1].
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55. ▌
    Signal Peptide Prediction And Localization · holobiomicslab

    Use when you have differentially expressed isoform or exon FASTA sequences from transcript assembly (e.g., IsoformSwitchAnalyzer output) and need to identify which predicted coding isoforms encode signal peptides for secretion or membrane targeting.
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56. ▌
    Small Molecule Structure Input Preparation · holobiomicslab

    Use when you have a small-molecule chemical structure in an initial or non-standard format and need to predict its environmental microbial degradation, gut microbiota metabolism, or mammalian biotransformation using BioTransformer's prediction modules.
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57. ▌
    Soil Aquatic Microbiota Pathway Simulation · holobiomicslab

    Use when when you have a small molecule (SMILES, MOL, or SDF format) and need to predict its degradation products in soil or aquatic microbial ecosystems; when environmental risk assessment, persistence prediction, or metabolite identification in these compartments is required;
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58. ▌
    Spectrum Chromatogram Mobilogram Rendering · holobiomicslab

    Use when you have mass spectrometry data in a Pandas DataFrame with columns for m/z and intensity (spectrum), retention time and intensity (chromatogram), or drift time and intensity (mobilogram), and need to render 1D traces as static or interactive plots for exploratory analysis, quality control.
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59. ▌
    Sqlite Database Design And Schema Creation · holobiomicslab

    Use when you have an mzML file and need to enable random-access spectrum retrieval by integer or string identifiers without holding the entire mzML in memory or decompressing indexed gzip files.
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60. ▌
    Statistical Significance Filtering Q Value · holobiomicslab

    Use when after performing univariate statistical tests (e.g., ANOVA, t-tests) across sample classes in a normalized metabolomic feature table, you need to control false discovery rate and select a high-confidence subset of significantly differentiated metabolites for downstream analysis (e.
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61. ▌
    String Value Concatenation With Delimiters · holobiomicslab

    Use when converting intermediate JSON to a target format (e.g., mwTab) and a single string field must be populated from multiple source records—for example, combining author names separated by semicolons, or concatenating a list of instrument names or experimental conditions.
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62. ▌
    Structural Level Pathway Topology Modeling · holobiomicslab

    Use when when you need to compare metabolic network architecture between two organisms and want to analyze their topological properties (e.g., network connectivity, reaction ordering, pathway structure) separately from functional annotations.
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63. ▌
    Structural Representation Format Detection · holobiomicslab

    Use when when you have a mixed-format input query that may contain a compound identifier and a structural representation separated by a tab delimiter, and you need to determine which ClassyFire API endpoint (SMILES, InChI, IUPAC, or FASTA) to submit the query to for chemical classification.
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64. ▌
    Subpfam Functional Resolution Augmentation · holobiomicslab

    Use when you have annotated genes with Pfam domains but require higher functional specificity to detect natural product sub-clusters.
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65. ▌
    Tissue Spatial Analysis Pipeline Execution · holobiomicslab

    Use when you have raw IMC (protein imaging) and SIMS (metabolite imaging) data from tissue regions that require spatial co-registration, single-cell-level intensity quantification, and joint analysis of protein–metabolite relationships.
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66. ▌
    Unit Test Development For Spectral Loaders · holobiomicslab

    Use when when you have implemented parser functions for one or more mass spectrometry file formats and need to verify that metadata and peak lists are correctly extracted and converted into matchms Spectrum objects before committing to a feature branch.
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67. ▌
    Validity Constraint Enforcement For Msdata · holobiomicslab

    Use when when designing a custom MsBackend subclass (e.g., MsBackendTest) that stores spectral data in multiple slots (a data.frame for spectra variables, NumericList objects for m/z and intensity peaks). Use this skill to guard against slot desynchronization—e.
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68. ▌
    Workflow Orchestration And Parallelization · holobiomicslab

    Use when when you have a multi-step computational chemistry or molecular modeling pipeline (3+ sequential or parallel stages) that must process many molecules, each requiring repeated tool invocations with different parameters, and you need reproducibility, fault tolerance, and the ability to.
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69. ▌
    XML Parsing And Element Tree Serialization · holobiomicslab

    Use when when spectrum or chromatogram data is stored as serialized XML strings in a database or file system and must be converted into pymzML Spectrum or Chromatogram objects for programmatic access.
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70. ▌
    Dask Array Lazy Evaluation Verification · holobiomicslab

    Use when when applying Scanpy preprocessing functions (e.g., pp.normalize_total, pp.pca) to AnnData objects where the expression matrix X is backed by a dask.array.Array, you need to verify that the operation completed without eagerly loading the full matrix.
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71. ▌
    Dispersion Estimation Negative Binomial · holobiomicslab

    Use when you have raw RNA-seq count data (from HTSeq, featureCounts, Salmon, or similar quantification tools) organized in a count matrix with samples as columns and genes as rows, and you need to test for differential expression between conditions using a negative binomial model.
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72. ▌
    Mapping Agreement Cross Tool Validation · holobiomicslab

    Use when when you have built a new tool implementation or major version and need to verify it produces equivalent results to a reference implementation on the same input data and index.
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73. ▌
    Monte Carlo P Value Estimation Adaptive · holobiomicslab

    Use when when you have ranked gene statistics and gene set collections, and your analysis requires P-value discrimination below a fixed lower bound (e.g., distinguishing between pathways at p < 1e-10).
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74. ▌
    Prior Distribution Fitting For Genomics · holobiomicslab

    Use when you have a fitted linear model (lmFit object) from microarray or RNA-seq count data and need to compute differential expression statistics, especially when the number of biological replicates is small (fewer than ~5–10 arrays/samples per group) and you want to avoid inflated variance.
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75. ▌
    Results Table Extraction And Comparison · holobiomicslab

    Use when after running DESeq() on a DESeqDataSet and obtaining initial results via results(), use this skill when you need to (1) extract base results tables for specific contrasts (e.
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76. ▌
    Scanpy Preprocessing Pipeline Execution · holobiomicslab

    Use when you have raw or minimally processed single-cell RNA-seq expression data loaded into an AnnData object (dense, sparse, or Dask-backed array as X), and you need to apply standardized preprocessing transformations (normalization, filtering, PCA) before downstream analysis such as clustering.
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77. ▌
    R Workflow Scripting For Analytical Chemistry · holobiomicslab

    Use when you have raw CE-MS or LC-MS instrument files (stored as OnDiskMSnExp objects or similar Bioconductor containers) and need to extract quantitative features (migration times, m/z values, peak intensities) by orchestrating multiple R packages in a controlled, documented sequence.
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78. ▌
    Large Scale All Pairs Similarity Benchmarking · holobiomicslab

    Use when you have multiple competing spectral similarity scoring methods (e.
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79. ▌
    Metabolite Quantification Accuracy Assessment · holobiomicslab

    Use when you have executed mzExacto() on a preprocessed GC-MS dataset and need to verify that the returned dataframe correctly matches query chemicals to their m/z peaks, retention times, and quantitative measurements (area values).
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80. ▌
    Multi Batch Experimental Design Understanding · holobiomicslab

    Use when your metabolomics experiment includes samples acquired across multiple instrument runs, different preparation dates, or distinct sample cohorts.
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81. ▌
    Retention Index Extraction From Nist Database · holobiomicslab

    Use when you have a compiled EI or MS2 library object (from read_lib or c() combination of multiple sources) and a local NIST library installation with accessible ri.dat and USER.DBU files in the mssearch/nist_ri directory.
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82. ▌
    Structural Similarity Ground Truth Validation · holobiomicslab

    Use when you have a spectral library with structural ground truth (InChIKey or SMILES annotations for ≥50% of spectra) and want to benchmark whether a new or existing spectral similarity scorer ranks structurally related compounds higher than unrelated ones.
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83. ▌
    Untargeted Metabolomics Marker Identification · holobiomicslab

    Use when when you have untargeted GC–MS or LC–MS data in the form of a two-dimensional m/z vs retention time map and need to identify marker features without conventional peak picking.
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84. ▌
    Word2vec Embedding Training Mass Spectrometry · holobiomicslab

    Use when you have a large collection of preprocessed MS/MS spectra (typically >10,000 spectra) with diverse chemical structures and you need to learn embeddings that capture fragmentation patterns and neutral loss relationships.
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85. ▌
    Chromatographic Alignment Parameter Selection · holobiomicslab

    Use when after chromatographic peak detection (e.g. centWave) has been performed on LC-MS data and you need to group features that likely originate from the same compound.
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86. ▌
    Chromatographic Peak Annotation Visualization · holobiomicslab

    Use when after running tardisPeaks() with screening_mode=TRUE on centroided .mzML LC-MS data, when you need to visually inspect whether the 10 target compounds (internal standards and endogenous metabolites) were correctly detected within their expected m/z and retention time windows.
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87. ▌
    Class Imbalance Downsampling Ratio Adjustment · holobiomicslab

    Use when after generating cross-spectrum negative examples via precursor m/z windowing and before training a rescore model (e.g., Siamese architecture in FIDDLE v2.0.0).
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88. ▌
    Comparative Classifier Performance Assessment · holobiomicslab

    Use when you have a labeled peak quality dataset (development set with ground-truth pass/fail labels), a defined set of peak-quality metrics (e.
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89. ▌
    Composite Spectra Assembly From Fragment Ions · holobiomicslab

    Use when you have DDA raw mass spectrometry data (mzML, mzXML, or netCDF format) and need to reconstruct composite fragmentation spectra by associating fragment ions with their parent precursor ions.
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90. ▌
    Compound Specific Misalignment Identification · holobiomicslab

    Use when you have completed XCMS grouping on LC-MS data and suspect misaligned features due to long acquisition periods (>1 week) or large sample cohorts (hundreds of samples).
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91. ▌
    Compound Specific Warping Function Generation · holobiomicslab

    Use when xCMS alignment produces suspected misaligned feature groups across hundreds of samples or long acquisition runs (>1 week), particularly when global XCMS warping functions fail to account for compound-specific or sample-neighborhood retention-time drift structures.
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92. ▌
    Dashboard Session Initialization Verification · holobiomicslab

    Use when after loading a specXplore session data object (saved .
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93. ▌
    Deep Learning Training Convergence Monitoring · holobiomicslab

    Use when training a CNN model from scratch on LCMS peak classification tasks (or similar image-like batched data) where you need to confirm the model reaches target performance (e.g., AUC ROC > 0.9) without overfitting.
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94. ▌
    Experiment Metadata Organization And Tracking · holobiomicslab

    Use when before initiating raw file conversion or feature extraction, when you have a heterogeneous collection of raw LC-MS files (.raw or .mzML) and sample information scattered across instrument logs, sequence files, or spreadsheets.
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95. ▌
    Feature Network Construction And Partitioning · holobiomicslab

    Use when you have a preprocessed LC-MS feature table (m/z, retention time, intensity columns) and need to identify which features belong together as isotopes or adducts of the same neutral compound.
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96. ▌
    Feature Pair Alignment Parameter Optimization · holobiomicslab

    Use when after anchor selection and retention-time spline mapping have produced a candidate list of feature pair alignments, but before final scoring and reduction of the combined table.
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97. ▌
    Feature Table Annotation With Sample Metadata · holobiomicslab

    Use when your input is a feature intensity table (CSV or R data frame) with features as columns and samples as rows, and you have accompanying sample metadata (batch identifiers, QC/study sample labels, run order, sample phenotypes, collection dates).
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98. ▌
    Feature Table Generation From Aligned Spectra · holobiomicslab

    Use when after retention-time and m/z-based peak alignment has been completed across a cohort of LC-MS samples, and you need to create a unified quantitative matrix for statistical testing, multivariate analysis, or annotation workflows.
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99. ▌
    Fragment Ion Peak Detection And Normalization · holobiomicslab

    Use when immediately after loading raw MS/MS spectra from .mgf, .msp, or .mzML files, before generating the bag-of-fragments corpus or extracting neutral losses.
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100. ▌
     Interactive Application Accessibility Testing · holobiomicslab

     Use when after instantiating a specXplore dashboard session layer with a loaded session data object from disk, before conducting visual exploration of LC-MS/MS spectral data.
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