Data & Analytics

holobiomicslab Skill

Use when you have MSI intensity data exported from MSiReader, SCiLS, or Cardinal as plain-text CSV files or as Cardinal MSProcessedImagingExperiment/MSContinuousImagingExperiment objects, and need to load it into R as a validated msimat object for mass difference and adduct analysis.

holobiomicslab Skill

Use when after feature detection and alignment (XCMS or equivalent), when you have a CSV feature table with m/z and retention time columns and need to group features derived from the same compound.

holobiomicslab Skill

Use when you have mass spectrometry data (m/z, retention time, intensity) loaded into a Pandas DataFrame and need to explore the full 3D structure of a peak map interactively, particularly when static 2D heatmaps obscure important intensity relationships or when stakeholders require browser-based.

holobiomicslab Skill

Use when when uploading a new mass spectrometry data file to Punc'data in CSV or delimited-text format, before attempting to map columns to their semantic roles (m/z value, intensity, formula).

holobiomicslab Skill

Use when after loading a numeric elemental ratio column (H/C, O/C, N/C, etc.) from a CSV file into Punc'data, when you need to visualize the distribution of molecular formulas across a sample and want bin spacing to adapt automatically: narrow bins (0.1) for low-range data (span ≤ 3.

holobiomicslab Skill

Use when when you have differential expression results with both fold-change and p-value columns from a CSV file (e.g., volcano_example.

holobiomicslab Skill

Use when you need to enable users to express complex domain-specific queries in a natural, succinct syntax—particularly when SQL patterns are familiar but must be augmented with domain assumptions (e.

holobiomicslab Skill

Use when after mzRAPP has exported a benchmark CSV file from processing a batch of centroided mzML files (e.

holobiomicslab Skill

Use when after XCMS feature detection and retention time correction, when you have a feature table (CSV or XCMS object) with m/z and retention time values aligned across samples.

holobiomicslab Skill

Use when you have a formula-assigned FT-ICR MS dataset (CSV or tab-delimited table with molecular formulas and mass values) and need to quantify the structural diversity, elemental stoichiometry patterns, or complexity landscape of the organic mixture.

holobiomicslab Skill

Use when when building a mass spectrometry visualization library that must support multiple plotting backends (matplotlib for static output, bokeh and plotly for interactive exploration) with a uniform DataFrame-based API, and where different plot kinds (spectrum, chromatogram, mobilogram, peakmap).

holobiomicslab Skill

Use when when you have raw metabolomics results from multiple studies in heterogeneous file formats (xls/xlsx, csv, or txt) and need to harmonize them into a single, machine-readable tibble structure with required columns (compound identifier, p-value, fold-change, study size N, reference) for.

holobiomicslab Skill

Use when when you have raw mzML files and a feature table (CSV from mzMine or XCMS) with labeled peaks of unequal class sizes (e.g., fewer false positives than true positives) and plan to train a CNN classifier on the LCMS data.

holobiomicslab Skill

Use when you have a feature table and accompanying CSV metadata that includes a 'Sample Type' field (or equivalent) with entries such as 'BLANK', 'QC', 'STD', or 'Unknown'.

holobiomicslab Skill

Use when when you have mass spectrometry data (chromatograms, spectra, mobilograms, or peak maps) in a Pandas DataFrame and need to generate the same visualization in multiple formats—e.

holobiomicslab Skill

Use when you have mass spectrometry data loaded as a pandas DataFrame with at minimum two numeric columns representing retention time and intensity values, and you need to generate a chromatogram visualization for exploratory analysis, quality control, or publication.

holobiomicslab Skill

Use when when implementing a new MsBackend subclass and need to return complete spectra data as a single DataFrame object that combines required core variables with backend-specific metadata columns, while avoiding cyclic function calls between spectraData() and spectraVariables().

holobiomicslab Skill

Use when after per-sample quantification is complete (e.g., salmon has produced quant.sf files for all samples in the cohort) and you need to prepare data for differential expression analysis, quality control comparisons, or multi-omics integration.

holobiomicslab Skill

Use when when you have a mzPeak file (uncompressed ZIP archive containing Parquet tables) and need to access decoded spectral data arrays (m/z, intensity), spectrum metadata (scan descriptors, precursors), or chromatogram data in a form suitable for Python/R analysis pipelines.

holobiomicslab Skill

Use when you have a formula-assigned FT-ICR MS dataset (CSV or tab-delimited table with molecular formulas and mass values) and need to quantify molecular chemodiversity, characterize elemental stoichiometry patterns, or explore transformation pathways across samples.

holobiomicslab Skill

Use when after loading spatial metabolomics data (from CSV, imzML, or merged positive/negative ionization modes) into an AnnData object, and before filtering metabolites or performing cross-modal integration.

holobiomicslab Skill

Use when after loading spatial metabolomics data (from CSV, imzML, or merged positive/negative mode files) into an AnnData object and before filtering or integrating with spatial transcriptomics.

holobiomicslab Skill

Use when you have a set of molecular structures (as .sdf or .csv with SMILES) and need to predict metabolic susceptibility across multiple CYP isoforms (e.g., 1A2, 2A6, 2B6) to prioritize drug candidates or screen for potential metabolic liabilities across the major human CYP enzyme portfolio.

holobiomicslab Skill

Use when you have loaded a metabolomics dataset with a metabolitedata dataframe containing annotation columns (e.

holobiomicslab Skill

Use when after retention-time correction and alignment of centroided mzML or mzXML LC-MS files across a sample cohort.

holobiomicslab Skill

Use when your input is an mzPeak archive (ZIP of Parquet files) and you need to recover spectrum signal data (m/z values and intensities) for downstream analysis, visualization, or format conversion. Use this skill when working with profile or centroid mode spectra stored in `spectra_data.

holobiomicslab Skill

Use when you are building a new data representation or storage strategy for MS spectra (e.g., on-disk HDF5, SQL database, remote file access) and need to integrate it seamlessly into workflows that use the Spectra package.

holobiomicslab Skill

Use when your input is a Pandas DataFrame containing mass spectrometry measurements (m/z and intensity columns for spectra, retention time and intensity for chromatograms, or x, y, z for 2D/3D peak maps) and you need to generate static plots (matplotlib) or interactive web-based visualizations.

holobiomicslab Skill

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.

holobiomicslab Skill

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

holobiomicslab Skill

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.

holobiomicslab Skill

Use when after loading raw Agilent Unknowns Analysis CSV output and when you need to convert tentative compound identifications (matched only by GC-MS library cosine similarity or Match.Factor score) into searchable, curated chemical records with exact masses and multi-source confirmation.

holobiomicslab Skill

Use when when you have mzML files from targeted or untargeted metabolomics experiments run in multiple ionization modes (e.g., KO_NEG, KO_POS, STD_NEG, STD_POS, WT_NEG, WT_POS) and a CSV list of reference compounds (e.

holobiomicslab Skill

Use when after spreadOut() has converted raw CSV peak data into a structured list, when you have one or more Compound.Name entries from GC-MS that may be ambiguous, non-canonical, or missing standardized properties (exact mass, published retention times, reactive groups, database presence).

holobiomicslab Skill

Use when you have mass spectrometry data loaded into a pandas DataFrame with m/z, retention time, and intensity columns, and need to confirm that pyOpenMS-Viz can produce visualizations (spectra, chromatograms, or peak maps) using a specific plotting backend (matplotlib, Bokeh, or Plotly) on real.

holobiomicslab Skill

Use when assembling a reanalysis or meta-analysis cohort from public metabolomics studies held in MetaboLights, Metabolomics Workbench and GNPS/MassIVE, and the instrument, polarity or open-format filters have to be honest about which repositories they silently exclude.