Data & Analytics

holobiomicslab Skill

Use when when you have a preprocessed metabolomics matrix (log2-scaled, CSV format with batch identifiers in the first column) and need to determine whether technical variation is present, which correction model (ber, ber-bagging, parametric ComBat, or non-parametric ComBat) performs on your.

holobiomicslab Skill

Use when you have a workflow.csv file co-located with sequence.csv in a SmartPeak session directory and need to load a default or custom workflow configuration into an executable command sequence.

holobiomicslab Skill

Use when when processing a multi-sample LC-MS metabolomics project after mass-track extraction and retention-time calibration have been applied to all individual samples, and you need to detect peaks across the entire cohort.

holobiomicslab Skill

Use when after peak detection and feature alignment in a metabolomic LC–MS/MS or GC–MS workflow, when you have a feature table (rows=metabolic features, columns=samples) split into separate .csv files for peak height and peak area.

holobiomicslab Skill

Use when you have LC-MS data from isotope labeling experiments where FAM measurements must be transformed to MDV values. Specifically, apply this when: (1) you have measured fractional abundances of isotopologues (FAM) in XLSX format from a high-resolution instrument (e.g., Orbitrap);

holobiomicslab Skill

Use when when migrating an existing file-based spectral library (stored as JSON, CSV, or binary formats) into a production system that requires frequent subset queries by metadata filters, similarity scoring across large spectral collections, or integration into downstream tools like MS2Query that.

holobiomicslab Skill

Use when you have a CSV-formatted target list with m/z, retention time, or ion mobility identifiers and need to locate and extract peak abundances from raw MS data files (Agilent .d, Thermo .raw, Bruker .d, mzML) acquired across LC-MS, LC-IMS-MS, DDA, DIA, or direct infusion modes.

holobiomicslab Skill

Use when you have raw NMR metabolomics measurements paired with pre-analytical metadata (e.g., processing delay times, sample type designations [plasma vs. serum], cohort identifiers) and need to investigate how delays affect measured metabolic parameters.

holobiomicslab Skill

Use when you have a tabular file (CSV or Excel) that has been manually or semi-automatically tagged with export tags, and you need to verify tag correctness before running the extract command to convert the tagged table into intermediate JSON.

holobiomicslab Skill

Use when when reading mzPeak files or other Parquet-backed mass spectrometry archives where spectral m/z and intensity arrays are stored in columnar layouts (point or chunked format) and you need to reconstruct them into Python NumPy arrays, pandas DataFrames, or equivalent in-memory structures for.

holobiomicslab Skill

Use when you have expression or metabolomic feature matrices, paired with event indicators (e.g., disease recurrence, mortality) and follow-up times for a cohort of samples, and you need to derive risk scores or prognosis indices for survival prediction or patient stratification.

holobiomicslab Skill

Use when you have raw metabolomics data in CSV format (samples as rows, features as columns, batch identifier in the first column) that contains missing values (zeros or NAs) and requires standardization before batch effect correction.

holobiomicslab Skill

Use when you have an expression matrix (e.g., heatmap_test.csv with genes as rows and samples as columns) and a corresponding group annotation file (e.g., group_info.csv with sample IDs and their group assignments).

holobiomicslab Skill

Use when you have a raw or extracted peak feature table (CSV or tabular format) containing mass-to-charge ratios, retention times, and intensity values across multiple samples from different experimental groups, and you need to identify which peaks show statistically significant differential.

holobiomicslab Skill

Use when you have experimental protocol information scattered across multiple rows in a spreadsheet or table (e.

holobiomicslab Skill

Use when when you have received raw MRM lipidomics export files in vendor-specific formats (TSV, CSV) with inconsistent column naming, unparsed lipid identifiers (e.g., 'PC(36:1)' as a single string), and unknown data quality issues.

holobiomicslab Skill

Use when raw MRM lipidomics data arrives in vendor-specific export formats (e.g., Sciex, Waters, Thermo TSV/CSV) with inconsistent or proprietary column naming, numeric encoding, and lipid nomenclature.

holobiomicslab Skill

Use when immediately after loading a raw GC-MS CSV file and before executing the spreadOut() function. Use it when you have received peak table data from an instrument vendor (e.

holobiomicslab Skill

Use when you have raw mzML files and a feature table (CSV) from LCMS data processed by tools like mzMine, and you need to create train/test/validation batches with specific matrix dimensions (120 × 2) and verified margin/peak signal separation before training or evaluating a neural network.

holobiomicslab Skill

Use when when you have feature lists (in CSV format) from two or more different MS acquisition methods (e.g., LC-MS vs. LC-IMS-MS), different processing software (e.

holobiomicslab Skill

Use when after feature extraction or feature alignment when you have FeatureSet or Sample objects that must be exported as CSV files for sharing, archival, or downstream analysis. Specifically: (1) when exporting single-sample feature tables from find_feature() output;

holobiomicslab Skill

Use when you have a preprocessed LC-MS peak table exported from peak-picking software (e.g., MS-DIAL) in Excel format with three logical compartments: sample annotation (rows), feature annotation (columns), and abundance matrix (numeric values).

holobiomicslab Skill

Use when you have LC- or GC-HRMS data in mzML format and a feature list (CSV/TSL/Excel) from external feature detection software (e.

holobiomicslab Skill

Use when you have experimental fragment m/z values from HRMS/MS instruments (Q-Exactive orbitrap, Q-TOF) in CSV or mzML-derived peaklist format, and need to match them against a library of 500,000+ in-silico fragmented lipid species.

holobiomicslab Skill

Use when you have experimental fragment m/z peaklists from Q-Exactive orbitrap, Agilent Q-TOF, Bruker Q-TOF, or SCIEX Q-TOF UHPLC-HRMS/MS instruments (in CSV or mzML-derived table formats) and need to assign lipid identities using untargeted or targeted tandem MS data.

holobiomicslab Skill

Use when after executing a MassQL query against mzML mass spectrometry data files and obtaining tabulated results (DataFrame or equivalent in-memory table), apply this skill to persist those results in both human-readable CSV format and visual image form for archival, sharing, and downstream.

holobiomicslab Skill

Use when you have a CSV file with rows of molecule definitions (chemical formulas, retention times, intensities, or other peak properties) and need to feed them into SMITER's simulation workflow.

holobiomicslab Skill

Use when a Python library exposes functionality that depends on external packages (like sqlalchemy, pandas, or lxml) that are not required for core operations.

holobiomicslab Skill

Use when you have a user-supplied metabolite set file (CSV or JSON) defining custom groupings of metabolites (e.

holobiomicslab Skill

Use when when designing or optimizing an MsBackend implementation (or similar columnar data structure) you must decide whether to pre-allocate all known columns in the backing DataFrame at initialization or defer column creation until first access.

holobiomicslab Skill

Use when you have a raw LC–MS compound metadata file (xlsx or csv) with heterogeneous column names and column order, and you need to prepare it for targeted peak detection in TARDIS.

holobiomicslab Skill

Use when you have mzML-format mass spectrometry data files and need to load them into memory as structured data (pandas DataFrame) to prepare for visualization with pyOpenMS-Viz or other analysis pipelines.

holobiomicslab Skill

Use when when you have a tagged tabular file (Excel or CSV) with columns marked using export tag syntax (e.g., #study.id, #subject.id, #.

holobiomicslab Skill

Use when after spectral database dereplication (using Spectra) and compound database dereplication (using SIRIUS or MetFrag) have produced candidate annotations in CSV or JSON format.

holobiomicslab Skill

Use when your pandas DataFrame contains mass spectrometry data with retention time (rt) and intensity columns AND a column representing different mass-to-charge (m/z) values or ion identifiers. This is particularly relevant when generating chromatogram plots from data with multiple mass traces (e.

holobiomicslab Skill

Use when when you have an omu_summary output dataframe with Class metadata and adjusted p-values (padj), and you need to focus downstream analysis (e.