Integrations & APIs

holobiomicslab Skill

Use when you have acquired complementary spectroscopic measurements (NMR, HSQC, COSY, IR) for the same molecular sample and need to combine them for structure elucidation.

holobiomicslab Skill

Use when you have executed batch spectral searches against two or more domain-specific MASSTs (via the Fast Search API or individual MASST tools) and need to combine the resulting hit lists, match scores, and taxonomic/metadata annotations into a single ranked table for downstream analysis.

holobiomicslab Skill

Use when when you have raw SMILES strings collected from multiple external databases that require standardization and deduplication before integration into a unified chemical structure database. Specifically, apply this skill when you need to convert interim/tables/0_original/structure/smiles.tsv.

holobiomicslab Skill

Use when when a Shiny application is documented or observed to run only on Windows, blocking deployment to Linux or macOS users. Typical triggers include hardcoded Windows path separators, unavailable packages on non-Windows systems, or system calls specific to the Windows API.

holobiomicslab Skill

Use when when you have a base message passing neural network (e.g., chemprop) trained on molecular graphs and need to augment it with infrared spectral information to improve prediction accuracy for molecular properties.

holobiomicslab Skill

Use when after loading centroided .mzML LC-MS data and defining a target list (compound ID, name, m/z, RT, polarity) when you need to: (1) automatically locate and integrate peaks for known compounds across multiple runs; (2) generate per-target and per-run quantitative metrics;

holobiomicslab Skill

Use when you have modified the Scanpy codebase (e.g., added a feature or bugfix) and need to confirm that all unit and integration tests pass before submitting a pull request, or when a CI workflow fails and you need to reproduce the failure locally to diagnose the root cause.

holobiomicslab Skill

Use when you have centroided mzML files from LC–MS experiments and need to perform targeted metabolomics or lipidomics analysis. Specifically, use it when you require polarity filtering, QC-sample stratification, or batch-aware peak detection—all of which depend on sampleData$type annotations (e.

holobiomicslab Skill

Use when you have preprocessed and filtered ST and SM AnnData objects with spatial coordinates and features, and you need to establish spot-level correspondence between the two modalities to enable downstream joint analysis.

holobiomicslab Skill

Use when when implementing a custom MsBackend class for the Spectra package, you must define these two methods to satisfy the MsBackend virtual class API contract.

holobiomicslab Skill

Use when immediately after integrating multiple metabolomic datasets from different analytical experiments into a unified feature-by-sample matrix. Batch effects manifest as systematic unwanted variations correlated with analytical batch rather than biological sample groups;

holobiomicslab Skill

Use when you have MZmine-aligned features with m/z and retention time, and you have generated spectral annotations from two or more database sources (e.g., GNPS/ISDB spectral matching and SIRIUS in silico structure elucidation).

holobiomicslab Skill

Use when when you have pre-written R functions organized across multiple .R files (e.g., GSimp.R, GSimp_evaluation.R, Impute_wrapper.

holobiomicslab Skill

Use when when executing peak integration on preprocessed GC-IMS data (after alignment and baseline correction) and you need to decide whether to include or exclude peaks that exhibit saturation artifacts from the RIP signal. Set a threshold (e.g., 0.

holobiomicslab Skill

Use when after running tardisPeaks() in screening mode or peak detection mode, when you need to visually confirm that target compounds are visible in the expected m/z and retention time windows, verify that peak integration boundaries are correct, diagnose whether sawtooth artefacts are present.

holobiomicslab Skill

Use when you have R Spectra objects and need to apply Python-only MS algorithms (e.

holobiomicslab Skill

Use when when you have experimental UHPLC-HRMS/MS data targeting lipid species not adequately covered by LipidMatch's built-in library (500,000+ species across 60+ lipid types), or when working with specialized applications requiring custom lipid definitions (e.

holobiomicslab Skill

Use when you have co-registered IMC (protein imaging mass cytometry) and SIMS (secondary ion mass spectrometry for metabolites) data from the same tissue regions, cell segmentation masks, and need to assign cell types based on protein expression patterns, then overlay those assignments onto.

holobiomicslab Skill

Use when after batch effect removal and data integration, when you have a feature-by-sample matrix (finalData) and wish to separate and visualize sample groups by their metabolomic profiles using clustering methods such as hierarchical clustering analysis (HCA).

holobiomicslab Skill

Use when you have centroided LC–MS data in .mzML format, a validated table of target compounds with adjusted expected retention times (RT in minutes), and you need to extract peak areas and quality metrics across all sample runs.

holobiomicslab Skill

Use when when baseline MLP or GNN models for spectral prediction show limited performance on metabolite annotation tasks, and you have access to auxiliary spectral topic labels (e.g., via LDA on spectral features) that could provide regularization signal.

holobiomicslab Skill

Use when you need to generate synthetic LC/GC-MS feature tables or raw mzML files with realistic peak complexity, ion multiplicities, and natural spectral variation—not just theoretical m/z values.

holobiomicslab Skill

Use when you have two or more independently processed MemoMatrix objects (each generated from a separate sample set) and your analysis goal requires direct comparison of MS2 fingerprint profiles across those samples.

holobiomicslab Skill

Use when you have completed batch spectral searches against multiple domain-specific MASST tools (via Fast Search API or individual domain searches) and need to combine and visualize the aggregated match results in a format compatible with metadataMASST web interface or downstream analysis.

holobiomicslab Skill

Use when when you have lipidomics quantitation data (lipid abundances across samples) that you need to load into a unified, annotated R object for analysis—either from public Metabolomics Workbench studies via API, Skyline mass spectrometry software exports, or a custom numerical matrix with.

holobiomicslab Skill

Use when you have centroided .mzML LC–MS data, a validated target compound list with adjusted expected retention times (e.g., after a screening mode run), and need to quantify peak quality and integration reliability across multiple sample runs to support metabolomics or lipidomics workflows.

holobiomicslab Skill

Use when after statistical analysis (e.g., MB-PLS with permutation testing) has identified a subset of significant LC-MS features (p < 0.05 or similar threshold) that require structural interpretation.

holobiomicslab Skill

Use when when you have LC/MS feature data (m/z, retention time, intensity) that must be annotated against a metabolic database and you have access to (or can construct) knowledge about biochemical reaction networks, expected isotope patterns, and instrument-specific adduct formation rules.

holobiomicslab Skill

Use when when you have imzML and accompanying ibd (ion binary data) files from spatial metabolomics experiments and need to load them into a unified AnnData object representation for integration with spatial transcriptomics or other spatial multi-omics data.

holobiomicslab Skill

Use when after integrating feature matrices from multiple analytical experiments (batches) into a single MutileAlign matrix. Batch effects manifest as systematic differences in metabolite intensities across batches even when analyzing the same samples.

holobiomicslab Skill

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.

holobiomicslab Skill

Use when you have measured intracellular metabolite concentrations (e.g., via LC–MS/MS) across multiple cell lines or samples and want to predict which metabolic reactions are substrate-limited versus transcriptionally regulated.

holobiomicslab Skill

Use when when deploying a high-throughput molecular classification API (e.g., /classify endpoint) where the same SMILES strings are expected to be queried repeatedly across multiple users or time windows, and response latency is a performance constraint.

holobiomicslab Skill

Use when after completing a Pickaxe reaction network expansion across N generations, when you need to store the predicted compounds and reactions in a format suitable for querying, sharing, or integration with other bioinformatic pipelines.

holobiomicslab Skill

Use when when a tool like TARDIS extends its API to accept multiple input types (e.g., both file paths and MsExperiment objects), and you need to confirm that screening-mode diagnostic outputs (e.g., EIC plots, peak detection metrics) are identical regardless of which invocation pattern is used.

holobiomicslab Skill

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.