Data & Analytics

holobiomicslab Skill

Use when you are creating a new backend to expose MS data (m/z, intensity, retention time, and other spectral properties) from a specific storage format or data source (e.g., mzML files, SQL databases, in-memory matrices, or spectral libraries) to Spectra objects.

holobiomicslab Skill

Use when after calling peaks and annotating cells in an ArchR project, when you need to perform trajectory analysis using STREAM or other external tools that require a peak-by-cell matrix in a specific tabular format (CSV or TSV) rather than native ArchR objects.

holobiomicslab Skill

Use when when you have obtained raw metabolite abundance data in a format that separates the measurement matrix from sample-level metadata (e.g., run day, plate ID, cohort variables) and feature-level annotations (e.

holobiomicslab Skill

Use when you have a metabolomics dataset (CSV format) containing detected peaks with m/z values and optionally retention time windows, and you need to filter a Pickaxe-generated compound expansion to keep only compounds whose masses match these observed peaks within a specified mass tolerance.

holobiomicslab Skill

Use when after loading and parsing raw MRM export files (TSV, CSV, or vendor-specific formats) into a pandas DataFrame and decomposing lipid nomenclature, before performing lipid matching, statistical analysis, or visualization.

holobiomicslab Skill

Use when you have raw mass spectrometry data in CSV or mzML format and need to visualize it using pyOpenMS-viz, or you are working with MS data that contains retention time (rt), m/z, intensity, and optionally ion mobility dimensions that must be structured as a Pandas DataFrame before plotting.

holobiomicslab Skill

Use when you have a log2-scaled metabolomics feature matrix in CSV format with samples in rows, metabolic features in columns, and batch identifiers in the first column, and you need to remove technical heterogeneity or drift across analytical batches before estimating biological mechanisms.

holobiomicslab Skill

Use when you have extracted multiple per-sample feature tables (in CSV format, each with feature ID, m/z, intensity, and retention time columns) and need to identify which features are the same compound detected across different breath samples.

holobiomicslab Skill

Use when when you have metabolomics comparison results from one or more studies in tabular format (spreadsheet or text file) with columns for compound name/identifier, statistical p-value, relative fold-change (including negative values indicating down-regulation), study sample size (N), and.

holobiomicslab Skill

Use when when you have loaded raw MS1 data from multiple instrument formats (Agilent, Thermo, Bruker, mzML) across a multi-sample cohort and need to prepare the ion intensity feature matrix for PCA analysis or cross-sample quality assessment.

holobiomicslab Skill

Use when after loading preprocessed MSI intensity data (via msimat from CSV export) or a simple numeric vector of mass peak values, when you need to discover which masses in your dataset co-vary as parent–adduct pairs.

holobiomicslab Skill

Use when when you have mass-spectrometry data in tabular form (Pandas DataFrame) with columns for m/z, retention time or ion mobility, and intensity, and you need to visualize the 2D distribution of peaks to assess peak separation, detect co-elution, or examine chromatographic and mass resolution.

holobiomicslab Skill

Use when after computing InChIKey and neighbourhood scores for library match candidates, you need to write results to a persistent format (CSV, JSON, or database) for storage, sharing, and interpretation.

holobiomicslab Skill

Use when when you have a feature table (CSV with m/z and retention time columns) and corresponding LC-MS raw data files (.mzXML or .mzML) from an untargeted metabolomics experiment, and need to filter out spurious peaks before downstream statistical or biological interpretation.

holobiomicslab Skill

Use when you have generated or curated a lipid spectral library (with precursor m/z, adduct information, charge states, retention times, and fragmentation patterns) and need to export it for use in either Excalibur-based DDA experiments on an Orbitrap mass spectrometer, or in Skyline for targeted.

holobiomicslab Skill

Use when you have aligned feature tables (CSV format) paired with MS2 spectral data (MGF or mzML files) and need to compare chemodiverse samples with poor feature overlap or strong retention-time shifts across different LC methods or mass spectrometer technologies (e.g., Orbitrap vs. Q-ToF).

holobiomicslab Skill

Use when immediately after MZmine feature detection when you have both MGF (MS/MS spectra) and CSV (metadata) output files for one or both ionization modes and wish to construct a deduplicated molecular network.

holobiomicslab Skill

Use when when converting mwTab-formatted metabolomics files (containing MS/NMR tabular data blocks) to JSON, or when you need to extract, manipulate, and re-serialize tabular sections from mwTab files while maintaining column structure and type information.

holobiomicslab Skill

Use when you have extracted quantitative genomic features (e.g., insulation scores, boundary annotations) as a pandas DataFrame with bin coordinates and boolean or numeric columns, and need to export them as BED format for visualization in genome browsers (e.

holobiomicslab Skill

Use when when building a scientific visualization library that must support multiple plotting backends and needs to avoid backend-specific code duplication. Specifically: (1) your domain (e.

holobiomicslab Skill

Use when you have one or more mzML files (XML-based mass spectrometry data) and need to convert them into mzPeak format for downstream analysis, archival, or integration with tools that consume Parquet-based spectra.

holobiomicslab Skill

Use when after feature extraction and alignment when you have a numerical feature table (CSV or dataframe) with intensity values across retention time or m/z dimensions, and you need to identify and rank peaks by signal quality and prominence rather than relying on all extracted features equally.

holobiomicslab Skill

Use when you have a collection of standardized molecular structures (SMILES or MOL format) that have been submitted to ClassyFire and you need to collect and organize their classification results into a single CSV or JSON table for downstream analysis, validation, or retention time modeling.

holobiomicslab Skill

Use when when you have validated mass spectrometry transition data (precursor m/z, product m/z, intensity, retention time, and compound identifiers) formatted in one tabular format (CSV or XLSX) and need to convert it to the other format for ingestion into EISA-EXPOSOME or long-term archival.

holobiomicslab Skill

Use when you have numeric distribution data (e.g., gene expression, abundance, or measurement values) in CSV format and need to compare cumulative empirical distributions either within a single cohort or across multiple sample groups (e.g., control vs. treatment, disease subtype vs. healthy).

holobiomicslab Skill

Use when when uploading a delimited CSV or similar tabular file to Punc'data containing high-resolution mass spectrometry results, and the column headers are present but their semantic roles (e.

holobiomicslab Skill

Use when you have mass spectrometry data in a pandas DataFrame with retention time (rt) and intensity columns, and need to produce a static figure for publication, presentation, or archival.

holobiomicslab Skill

Use when you have a pre-trained MSGO model checkpoint (PFAS or lipid variant) and need to evaluate it against a real mass spectrometry dataset (300+ real spectra, LC–QTOF, or custom CSV) to generate predicted molecular structures and compare against ground truth or baseline results.

holobiomicslab Skill

Use when you have a collection of molecules represented as CSV or EXCEL files containing computed molecular descriptors (e.g., physicochemical properties, structural features) and you need to predict whether each molecule will taste bitter.

holobiomicslab Skill

Use when you have mass spectrometry data (retention time, m/z, intensity, or mobility dimensions) already loaded into a Pandas DataFrame and need to produce publication-ready or exploratory visualizations. Use this when you want to leverage Pandas' native .

holobiomicslab Skill

Use when you have a metabolomic feature table (rows=features, columns=samples) with peak height and peak area measurements from chromatographic processing, and you suspect data quality issues such as misaligned features or erratic peak integration across your sample cohort.

holobiomicslab Skill

Use when you have raw LipidSearch or LIQUID output files (CSV or TSV format) containing lipid identifiers and quantification columns (relative intensity or area values), and need to construct a clean, analyzable data matrix with consistent lipid nomenclature and no missing values in critical fields.

holobiomicslab Skill

Use when you have a set of molecules (as SMILES strings in CSV format) and a pre-trained NaFM checkpoint, and you need to generate embeddings or predictions for virtual screening, bioactivity regression, or compound ranking tasks.

holobiomicslab Skill

Use when you have MSI intensity data exported from commercial software (MSiReader, SCiLS) or Cardinal processing pipelines as plain-text CSV files or Cardinal MSProcessedImagingExperiment/MSContinuousImagingExperiment objects, and you need to prepare it for mass-difference tabulation and adduct.

holobiomicslab Skill

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

holobiomicslab Skill

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