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HolobiomicsLab

@holobiomicslab source repo

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1. ▌
   Polarity Mode Filtering Lcms · holobiomicslab

   Use when you have a comprehensive target list (containing compounds from both positive and negative ionization modes) but need to screen or detect peaks in a single LC-MS run acquired in a specific polarity mode.
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2. ▌
   Precursor Product Mz Parsing · holobiomicslab

   Use when you have raw MRM sample files from a LC-MS/MS instrument and need to systematically recover all precursor m/z and product m/z pairs for each transition. Use this as an initial parsing step before quantitation, method optimization, or transition verification workflows.
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   Proforma Peptidoform Parsing · holobiomicslab

   Use when when you have a ProForma 2.0–formatted peptide string with PSI-MOD or UniMod modification labels and need to extract the underlying amino acid sequence and modification positions before performing theoretical fragment ion calculation or spectrum peak matching.
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4. ▌
   Project Metadata Integration · holobiomicslab

   Use when a paired omics project record contains a genome identifier (e.g., GenBank accession) but lacks the corresponding organism name field, and you need to populate that metadata field to enable full text search, sample tracking, or project validation before archival to Zenodo.
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5. ▌
   Pytorch Model Implementation · holobiomicslab

   Use when you have preprocessed MS/MS spectral data (m/z and intensity arrays) and need to map it to a fixed-size latent vector for use in an encoder–decoder architecture. Specifically applicable when the downstream task requires a molecular structure reconstruction (e.
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6. ▌
   Qc Sample Quality Assessment · holobiomicslab

   Use when after drift correction and before imputation when you have LC-MS data with designated QC samples and you need to remove features with poor reproducibility across QC replicates.
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7. ▌
   Retention Time Peak Flagging · holobiomicslab

   Use when after applying peak detection algorithms to identify local maxima in feature signals across retention time or m/z dimensions, but before exporting or filtering the peak list for further analysis.
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8. ▌
   Retention Time Peak Matching · holobiomicslab

   Use when after drift correction and quality flagging, when you have a feature abundance matrix with associated metadata (Feature_ID, m/z, retention time) and need to identify which features likely represent the same underlying metabolite or adduct series before statistical analysis or metabolite.
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9. ▌
   Sample Missingness Filtering · holobiomicslab

   Use when when you have loaded (un)targeted metabolite data into a Metaboprep object and need to exclude samples with excessive missing values before quality control or statistical analysis. This is typically applied early in the QC pipeline when sample-level data completeness is a concern (e.
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10. ▌
    Session Object Serialization · holobiomicslab

    Use when after you have processed raw LC-MS/MS spectral data through the specXplore importing pipeline in a Jupyter notebook and produced an in-memory specXplore session data object containing t-SNE embeddings (based on ms2deepscore similarity scores) and associated spectral metadata.
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11. ▌
    Siamese Architecture Scoring · holobiomicslab

    Use when after a TCN-based formula prediction model has generated initial formula candidates from MS/MS spectra, apply this skill to rescore and refine those candidates when you need to improve ranking accuracy.
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12. ▌
    Similarity Matrix Generation · holobiomicslab

    Use when you have a collection of cleaned spectra in supported formats (mzML, mzXML, msp, MGF, JSON) and need to compute all pairwise similarity scores to identify related or duplicate spectra, support spectral library searching, or enable network-based analysis.
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13. ▌
    Spectral Data Format Parsing · holobiomicslab

    Use when when you have raw LC-MS/MS data files in mzML, mzXML, or vendor-specific formats and need to load them into a Java-based mass spectrometry analysis framework for downstream spectral analysis, fragmentation tree computation, or metabolite identification.
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14. ▌
    Spectral Entropy Calculation · holobiomicslab

    Use when when you have preprocessed MS/MS spectral peak data (m/z and intensity pairs) and need to compute a complexity metric for individual spectra prior to similarity comparisons, or when benchmarking compound identification performance against dot product–based methods.
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15. ▌
    Spectral Fragment Assignment · holobiomicslab

    Use when when you have an experimental tandem mass spectrum (peaks with m/z and intensity values) and wish to identify which fragments or chemical subformulae each peak corresponds to.
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16. ▌
    Spectral Mz Window Filtering · holobiomicslab

    Use when when you have resolved mzML or mzXML spectrum files and need to isolate signals for a target m/z value (e.g., 870.954) across all retention times or a specific scan.
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17. ▌
    Spectral Similarity Grouping · holobiomicslab

    Use when when you have computed a sparse pairwise distance matrix from nearest neighbor indexes of high-resolution MS/MS spectra and need to partition them into clusters such that spectra within each cluster correspond to similar fragmentation patterns (e.g., same peptide or metabolite).
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18. ▌
    Spectral Workflow Validation · holobiomicslab

    Use when after implementing or modifying a cross-language integration layer that wraps Python mass spectrometry functions (e.g., spectral matching, peak detection, normalization from matchms or spectrum_utils) as R-callable functions within Spectra workflows.
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19. ▌
    Spectrum Metadata Extraction · holobiomicslab

    Use when when you have raw mass spectrometry data from diverse instrument vendors (Thermo, Sciex, etc.) and need to harmonize and standardize spectrum-level metadata—including scan information, precursor m/z and charge, and ion selection parameters—into a queryable, vendor-agnostic tabular schema.
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20. ▌
    Sterol Isomer Classification · holobiomicslab

    Use when you have LC-IM-MS/MS raw data from sterol-containing tissue samples and need to assign detected peaks to specific structural isomers (e.g., distinct double bond positions or saturation patterns in C27–C29 sterols).
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21. ▌
    Tandem Mass Spectrum Parsing · holobiomicslab

    Use when when you have raw or instrument-native tandem mass spectrometry data (MS/MS) in formats such as mzML, mzXML, or proprietary binary formats, and you need to align, match, or compare spectra using methods like SIMILE that require structured access to precursor m/z, fragment m/z values, and.
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22. ▌
    Tandem Ms Feature Assessment · holobiomicslab

    Use when after importing raw peak tables from tandem MS/MS preprocessing software (e.
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23. ▌
    Targeted Peak Extraction Ms1 · holobiomicslab

    Use when you have raw MS data in a supported instrument format (Agilent .d, Thermo .raw, Bruker .d, mzML) and a predefined list of molecular targets (CSV with m/z and/or retention time) that you need to quantify.
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24. ▌
    Two Layer Topology Traversal · holobiomicslab

    Use when you have an untargeted metabolomics dataset with partial metabolite annotations (from database matching or prior curation) and need to extend annotation coverage to unannotated metabolites.
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25. ▌
    Model Metadata Extraction · holobiomicslab

    Use when when you need to programmatically interface with a TensorFlow Serving model instance and must discover or validate the expected input names (e.
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26. ▌
    Multitask Model Inference · holobiomicslab

    Use when you have a trained multitask model checkpoint and preprocessed spectral inputs (1D NMR spectra, 1H-only, 13C-only, or combined 1H+13C), and you need to generate simultaneous predictions of molecular formula and connectivity structure to quantify modality contributions, compare single vs..
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27. ▌
    Nmr Peak Table Generation · holobiomicslab

    Use when you have raw 1D NMR spectra (in NMRPipe or similar format) from biological samples (urine, worm, or other metabolomics experiments) and need to extract a structured peak table with chemical shifts and intensities for downstream metabolite identification or quantification.
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28. ▌
    Nmr Spectra Preprocessing · holobiomicslab

    Use when when you have raw 1D NMR spectroscopic data (urine, worm, or other biological samples) in a format supported by NMRBox/NMRPipe and need to generate peak tables with automatic phasing, baseline correction, and spectral deconvolution without manual intervention.
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29. ▌
    Optimal Transport Scoring · holobiomicslab

    Use when when you have an observed NMR mixture spectrum and one or more candidate reconstructed spectra (each formed by combining single-compound reference spectra from a library), and you need a scalar similarity score to rank or select the approximation.
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30. ▌
    CI Workflow Execution · holobiomicslab

    Use when you have a GitHub repository with published CI workflow badges (e.g., unit test or package test badges in the README) and need to independently verify that the workflows execute successfully, reproduce the pass/fail status, and collect structured test results.
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31. ▌
    JSON Response Parsing · holobiomicslab

    Use when you receive a JSON response from a REST API endpoint (e.g., TensorFlow Serving /model/metadata) and need to extract and validate specific fields such as model input names, signatures, or outputs.
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32. ▌
    Module Import Testing · holobiomicslab

    Use when releasing a new version of a Python package, validating packaging infrastructure changes, or confirming that distribution channels (PyPI, Bioconda) remain functional after upstream updates. Use it as a gate before finalizing a release to catch installation or import breakage early.
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33. ▌
    Qcpool Cv Calculation · holobiomicslab

    Use when you have Sciex Multiquant txt exports containing signal intensities from QCpool samples injected at regular intervals (e.g., every 10–20 samples) during one or more analytical sequences in a metabolomics or lipidomics study.
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34. ▌
    Batch Timing Analysis · holobiomicslab

    Use when when evaluating whether a new or candidate mass spectrometry processing library (e.g., spectrum_utils) offers faster spectrum processing throughput than established alternatives (e.
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35. ▌
    CSV Format Validation · holobiomicslab

    Use when when you have a .csv file intended as input to CypReact containing SMILES strings and need to verify it meets the required comma-separated format specification and that each SMILES string is syntactically valid before processing molecules through CYP isoform testing.
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36. ▌
    Isotopic Peak Removal · holobiomicslab

    Use when after loading raw FT-ICR MS peak lists with assigned molecular formulas when you have detected peaks across multiple m/z values that correspond to isotopic variants of the same parent compound.
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37. ▌
    Job Tuple Enumeration · holobiomicslab

    Use when when initializing MSMetaEnhancer or extending it with new converters, you need to discover all available (source_attribute, target_attribute, converter_name) conversion triples to build a complete job registry.
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38. ▌
    JSON Metadata Parsing · holobiomicslab

    Use when you have a repository of JSON-formatted scientific annotations (e.g., MIBiG curation data) and need to systematically extract structured metadata fields (such as cluster.
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39. ▌
    Msi Data Table Export · holobiomicslab

    Use when after calculating mean intensity values across all spectra in an MSI dataset (or within a manually selected ROI), and you need to store the resulting m/z–intensity table in a portable format for downstream ROI analysis, database annotation, or external statistical pipelines.
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40. ▌
    R Matrix Manipulation · holobiomicslab

    Use when when working with three-part metabolomics data structures (featuredata matrix, metabolitedata and sampledata dataframes) and you need to: (1) identify subsets of metabolites by their annotation (e.
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41. ▌
    R Package Integration · holobiomicslab

    Use when when you have a functional R package with core statistical or computational logic that needs to be made accessible to non-R users, or when you want to streamline a multi-step analytical workflow (e.
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42. ▌
    Roc Curve Computation · holobiomicslab

    Use when you have paired columns of predicted probabilities (or decision scores) and true binary class labels, and need to assess classifier discrimination ability across all decision thresholds.
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43. ▌
    Sdf Format Validation · holobiomicslab

    Use when when you have downloaded an SDF-formatted compound database file (such as from the DNA adductomics database) and need to verify that the file is not corrupted, that each record represents a single valid chemical structure, and to obtain a record count before proceeding to structure-based.
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44. ▌
    Smiles String Parsing · holobiomicslab

    Use when when you have a raw list of SMILES strings in a file and need to prepare them as input for CFM-ID in silico spectrum generation to create pseudo SMILES-spectrum training pairs.
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45. ▌
    Spectral Token Design · holobiomicslab

    Use when when you need to prepare mass spectra and molecular structures for joint modeling in a BART or transformer-based sequence model, and you lack a unified representation scheme that allows both modalities to be encoded and decoded without collision or information loss.
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46. ▌
    Test Suite Validation · holobiomicslab

    Use when after rewriting, refactoring, or updating a core computational module (e.g., calculate_feature_overlap.py in FERMO 0.8.7) to confirm that all existing unit tests pass with zero failures and that the rewritten code does not introduce regressions or break existing contracts.
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47. ▌
    Coeluting Ion Separation · holobiomicslab

    Use when raw GC-MS data (netCDF or mzML format) exhibits overlapping chromatographic peaks—i.e., when multiple analytes elute at similar retention times and produce composite mass spectra with ambiguous m/z signals.
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48. ▌
    Retention Time Filtering · holobiomicslab

    Use when working with GCIMS datasets where retention time spans a wide range (e.g., 0–1500 s) but your analytes of interest are confined to a narrower window (e.g., 0–1100 s).
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49. ▌
    Spectral Noise Filtering · holobiomicslab

    Use when immediately after extracting ion chromatograms (EICs) by binning mass spectral data across the full m/z range from raw LC/HRMS files (mzML, mzXML, or netCDF format).
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50. ▌
    Ensemble Model Inference · holobiomicslab

    Use when you have a set of compounds (as SMILES strings or molecular structures) that need retention order predictions in a reversed-phase liquid chromatography (RPLC) system at eluent pH ~2.7, and you want to quantify prediction uncertainty rather than relying on a single model's output.
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51. ▌
    File Format Writing Mzml · holobiomicslab

    Use when after completing an Environment simulation or replay with scan-level MS2 acquisition control, and evaluation data has been collected in memory.
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52. ▌
    Genome Identifier Lookup · holobiomicslab

    Use when a paired omics project JSON document contains genome identifiers (e.g. IMG IDs, NCBI accessions) but lacks corresponding organism names.
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53. ▌
    Gnps Mgf Format Handling · holobiomicslab

    Use when you have mass spectrometry MS/MS spectral data in GNPS-style MGF format and need to feed it into the Mass2SMILES deep learning model for structure and functional group prediction.
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54. ▌
    Gnps Repository Querying · holobiomicslab

    Use when when you have a USI string (e.g., mzspec:GNPS:TASK-d93bdbb5cdda40e48975e6e18a45c3ce-...
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55. ▌
    Hrms Data Format Parsing · holobiomicslab

    Use when you have raw or processed HRMS/MS data from Q-Exactive, Agilent Q-TOF, Bruker Q-TOF, or SCIEX Q-TOF instruments in formats such as mzML, CSV peaklists, or vendor-specific formats, and you need to extract experimental fragment m/z values and their intensities for comparison against.
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56. ▌
    Ionization Mode Handling · holobiomicslab

    Use when your MZmine MGF and CSV input files contain mixed or ambiguous ionization modes, or when your experimental design specifies separate negative (NEG) and positive (POS) ionization mode acquisitions that must be processed independently before merging.
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57. ▌
    Isf Feature Table Export · holobiomicslab

    Use when after completing Part 4 (Identification of ISF Features) in the ISFrag workflow, when you have an ISFrag analysis results object containing identified ISF features and need to save them as a portable, tabular file for external analysis, reporting, or integration with other metabolomics.
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58. ▌
    JSON Document Enrichment · holobiomicslab

    Use when when your project JSON document contains public identifiers (genome IDs, biosample accessions, etc.) that lack human-readable or linked metadata, and you need to populate those fields programmatically before storage or publication to enable full-text search, validation, or cross-linking.
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59. ▌
    Lc Ms Data Preprocessing · holobiomicslab

    Use when you have raw mzML files and corresponding feature tables (CSV format, mzmine-formatted) from untargeted LCMS experiments, and you need to convert them into uniformly-shaped peak matrices (2 × 120 per peak: margin + signal regions) as input for neural network classification of MS1 peak.
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60. ▌
    Lcms Data Format Parsing · holobiomicslab

    Use when when you have raw LC/MS data in mzML format and need to execute the LAGF non-targeted screening pipeline. Use this skill as the first step before applying the LAGF algorithm workflow to extract and annotate features from mass spectrometry data.
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61. ▌
    Mass To Charge Filtering · holobiomicslab

    Use when after generating theoretical B/Y ion spectra or after importing experimental MS/MS scans when your analysis goal requires restricting the ion population to a specific m/z window (e.g., m/z < 2000).
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62. ▌
    Mass Tolerance Filtering · holobiomicslab

    Use when when you have observed fragment peak m/z values from tandem mass spectra and need to assign chemical subformulae to them.
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63. ▌
    Matlab Package Execution · holobiomicslab

    Use when you have two separate LC-MS untargeted metabolomic feature datasets (each with retention time and m/z values) and need to establish feature-to-feature correspondence between them.
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64. ▌
    Ms Ms Ion Interpretation · holobiomicslab

    Use when when you have two MS/MS spectra (each with a precursor m/z and a list of fragment ion m/z values) and need to identify which fragment ions correspond between them, especially when structural differences make simple monotonic alignment unreliable.
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65. ▌
    Ms2 Precursor Extraction · holobiomicslab

    Use when when you have a resolved spectrum file in mzML or mzXML format and need to identify where MS2 (tandem mass spectrometry) scans occur within an LC-MS run, particularly to annotate XIC displays with MS2 precursor positions or validate that MS2 events align with expected chromatographic.
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66. ▌
    Mzml File Format Parsing · holobiomicslab

    Use when when you have mass spectrometry raw data in mzML format and need to execute MassQL queries, perform batch analysis across a directory of spectra files, or programmatically access MS1 and MS2 scan data with retention time, m/z, and intensity metadata.
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67. ▌
    Mzml File Format Writing · holobiomicslab

    Use when you have peak properties (mass, intensity, retention time) for biomolecules, selected noise and fragmentation models, and instrument parameters (e.
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68. ▌
    Mzml XML Deserialization · holobiomicslab

    Use when your input is an mzML file (XML-based mass spectrometry data format) and you need to expose spectral metadata, scan information, and ion data in a structured, programmatic form for alignment, clustering, drift correction, or quantification within the BMXP pipeline.
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69. ▌
    Numpy Array Manipulation · holobiomicslab

    Use when when you have raw MS/MS peak lists that need to be loaded, analyzed for intensity frequency patterns, or filtered based on noise characteristics.
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70. ▌
    Peak Formula Enumeration · holobiomicslab

    Use when when you have tandem mass spectra (mz/intensity pairs with precursor m/z) and need to train interpretable machine learning models—particularly decision trees or regression models—where each feature must correspond to a concrete chemical entity (peak or neutral loss) rather than a latent.
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71. ▌
    Peak Matrix Construction · holobiomicslab

    Use when when you have raw mzML files and a corresponding feature table (CSV format, e.g., from mzmine) and need to generate peak matrices with fixed dimensions (e.g., 2 × 120) that encode margin vs. peak signal regions for training a neural network classifier to filter false positive LCMS peaks.
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72. ▌
    Peak Property Validation · holobiomicslab

    Use when after converting a CSV file of molecule definitions into a peak properties dictionary using csv_to_peak_properties, or after loading a serialized peak properties dictionary from pickle/JSON storage, before passing it to SMITER simulation functions (e.g., smiter.synthetic_mzml.write_mzml).
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73. ▌
    Peak Table Deconvolution · holobiomicslab

    Use when your peak table contains ions with similar retention time and mass-to-charge ratios that likely represent isotopic patterns incorrectly split during preprocessing or detector artifacts.
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74. ▌
    Peak Table Harmonization · holobiomicslab

    Use when when you have extracted peak tables from multiple independent LC/HRMS analysis batches (each with retention time and m/z values) and need to align peaks across batches to create a unified feature matrix for downstream statistical analysis.
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75. ▌
    Precursor Mass Filtering · holobiomicslab

    Use when after retrieving top-scoring library candidates from a full MS2Deepscore comparison, but before or during final re-ranking.
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76. ▌
    Qiime2 Artifact Handling · holobiomicslab

    Use when you have raw mass-spectrometry files (MGF, BIOM, mzXML, mzML) or feature abundance tables from external tools (MZmine2, peak detection software) and need to convert them into QIIME 2's typed artifact format before running downstream plugins like q2-qemistree, diversity analyses, or.
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77. ▌
    R Data Frame Aggregation · holobiomicslab

    Use when when you have extracted multiple spectral attributes (e.g., base-peak m/z, intensity, retention time, scan index) from individual MS scans via accessor functions and need to organize them into a single rectangular data frame for batch analysis, filtering, or export to external tools.
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78. ▌
    Retention Time Alignment · holobiomicslab

    Use when after peak detection in untargeted metabolomics when you have identified ion signals across mass-to-charge and retention-time dimensions from replicate injections of the same samples, and you need to group peaks from different runs that represent the same metabolite before building a.
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79. ▌
    Shiny App Initialization · holobiomicslab

    Use when you have developed an R-based workflow (e.g., data processing, peak detection, quality review) that is complex enough to warrant interactive parameter tuning and visual feedback, and you need to distribute it to collaborators or end-users who prefer a graphical interface over scripting.
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80. ▌
    Similarity Score Sorting · holobiomicslab

    Use when after a deep-learning model has predicted structural similarity scores between an unknown metabolite's MS/MS spectrum and all known metabolites in a reference database.
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81. ▌
    Spectral Data Validation · holobiomicslab

    Use when when raw spectra have been imported from common MS/MS file formats but contain inconsistent, missing, or malformed metadata fields that could compromise spectral similarity comparisons or cause downstream pipeline failures.
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82. ▌
    Spectrum Data Formatting · holobiomicslab

    Use when you have acquired raw or semi-processed mass spectra and need to search them against curated domain-specific databases (microbeMASST, plantMASST, etc.) using standalone web applications or batch APIs.
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83. ▌
    Spectrum Pair Prediction · holobiomicslab

    Use when you have pairs of MS/MS spectra and need to estimate their structural similarity (Tanimoto score based on molecular fingerprints) as a proxy for compound relatedness.
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84. ▌
    Binary File Format Parsing · holobiomicslab

    Use when you encounter a proprietary or undocumented binary file (e.
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85. ▌
    HTTP Post Request Assembly · holobiomicslab

    Use when you have NMR peak data (1H and 13C chemical shift values) that must be submitted to a remote DeepSAT SMART 3 classification API for structural prediction, and you need to format the data correctly, validate the endpoint schema, and parse the response to extract predictions and confidence.
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86. ▌
    Nmr Peak Quality Filtering · holobiomicslab

    Use when you have a CSV or table-formatted input spectral peak list (chemical shifts, intensities, metadata) destined for NMRformer metabolite identification, and you observe or suspect that the list contains noisy peaks or peaks with chemical shifts that do not match known reference standards.
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87. ▌
    Nmr Spectrum Normalization · holobiomicslab

    Use when when preparing library compound spectra and observed mixture spectra for mcfNMR analysis, normalize both spectra to cumulative distribution functions (CDFs) scaled by total intensity.
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88. ▌
    Nmr Spectrum Preprocessing · holobiomicslab

    Use when you have raw or semi-processed 1D NMR spectra (¹H and/or ¹³C) from routine laboratory instruments and need to feed them into a CNN–transformer architecture for end-to-end structure elucidation.
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89. ▌
    Nmr Spin System Simulation · holobiomicslab

    Use when when you have known metabolite concentrations and their spin-system coupling constants (J-values) and need to generate synthetic ¹H NMR spectra for method validation, metabolite library construction, or as a training dataset.
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90. ▌
    Peak Similarity Assessment · holobiomicslab

    Use when you have a set of picked peaks from INADEQUATE NMR spectra and need to group them into networks to identify which peaks co-originate from the same metabolite compound.
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91. ▌
    Pytorch Module Composition · holobiomicslab

    Use when when you need to extract both local spatial patterns and global long-range dependencies from sequential or spectral data (e.g., 1H NMR spectra), and neither CNNs nor Transformers alone are sufficient.
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92. ▌
    Spectral Database Matching · holobiomicslab

    Use when when you have MS2 .mzML format data files from untargeted metabolomics or proteomics experiments and need to perform an initial annotation step by matching experimental spectra against known reference libraries (GNPS, HMDB, MassBank) with a defined precursor mass tolerance (e.g., 15 ppm).
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93. ▌
    Spectral Query Formulation · holobiomicslab

    Use when you have a query mass spectrum (or representative metabolite spectrum from public data) and need to identify it by searching against large spectral reference databases (millions to billions of spectra).
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94. ▌
    File Format Validation · holobiomicslab

    Use when when you have authored a custom .csv lipid library and need to confirm it adheres to LipidMatch's documented schema before placing it in the designated library directory and running the library integration/loading step.
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95. ▌
    Git Repository Cloning · holobiomicslab

    Use when you need to obtain source code or computational workflows from a published repository, particularly when the article explicitly provides a GitHub URL and documents that the repository contains code required to regenerate published results (e.g., simulation outputs, figures, or tables).
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96. ▌
    R Cmd Check Validation · holobiomicslab

    Use when after installing an R package from a non-CRAN repository (such as r-universe) to confirm the package build is sound, dependencies resolve correctly, and no warnings or errors are introduced. Use it as a gate before relying on the package for downstream analysis or distribution.
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97. ▌
    Toml Format Validation · holobiomicslab

    Use when you are setting up a new LipoCLEAN analysis for MS-DIAL output and need to create a configuration file tailored to your MS-DIAL version (4 or 5).
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98. ▌
    Auc Metric Calculation · holobiomicslab

    Use when when you have computed true positive rate (TPR) and false positive rate (FPR) values across classification probability thresholds from predicted probabilities and true labels, and need to generate a single aggregate performance metric for model comparison or reporting in omics.
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99. ▌
    Effect Size Estimation · holobiomicslab

    Use when after performing statistical tests (t-test, Mann–Whitney U, ANOVA) on matched lipid abundance data grouped by experimental condition, to report not only whether differences are statistically significant but also the magnitude and direction of change.
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100. ▌
     Excel Template Parsing · holobiomicslab

     Use when when you have an Excel file downloaded from InjectionDesign's template or conforming to its schema, and you need to extract sample identifiers, classification dimensions (up to three), and QC type labels (Blank QC, Solvent QC, Pooled QC, Long-Term Reference QC, or custom) into a structured.
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