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HolobiomicsLab

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1. ▌
   Normalization Method Comparative Evaluation · holobiomicslab

   Use when you have raw metabolomics intensity data affected by batch effects and signal drift, and need to select among multiple normalization approaches based on published comparative metrics.
   0 installs
2. ▌
   Numerical Feature Serialization And Storage · holobiomicslab

   Use when after computing molecular descriptors (RDKit, mordred) or featurizing chromatographic parameters (column metadata, gradient slopes, pH, additives), you have a NumPy array or list of arrays in memory that must be persisted for reproducibility, shared across pipeline stages, or archived.
   0 installs
3. ▌
   Outlier Detection Via Multivariate Distance · holobiomicslab

   Use when after batch normalization when you have a Metaboprep object with metabolomic feature data and need to identify samples that are statistical outliers in the high-dimensional feature space. Use it when you want to detect unusual sample profiles before downstream multivariate analyses (e.
   0 installs
4. ▌
   Pearson Correlation Coefficient Computation · holobiomicslab

   Use when after annotating mass-difference pairs with candidate adduct identities, compute correlations to test whether putative parent and adduct ion intensities co-vary across the imaging pixel grid. High positive correlation (with Bonferroni-corrected p-value < 0.
   0 installs
5. ▌
   Polarity Filtering In Targeted Metabolomics · holobiomicslab

   Use when when working with targeted LC–MS metabolomics or lipidomics data where compound targets span both positive and negative ionization modes, or when your raw .
   0 installs
6. ▌
   Rt Uncertainty Quantification And Filtering · holobiomicslab

   Use when you have a set of metabolite candidates with observed retention times and access to a trained DNN retention time predictor (MAE 39.2±1.2 s) or meta-learned projections between chromatographic methods.
   0 installs
7. ▌
   Scalable Network Inference High Dimensional · holobiomicslab

   Use when when working with feature abundance tables (rows=features, columns=samples) where the feature count exceeds 10,000 and you need to infer a network of correlations between features.
   0 installs
8. ▌
   Spearman Correlation Computation Microbiome · holobiomicslab

   Use when you have cross-validated predictions of metabolite abundances from a microbiome-metabolome model and need to: (1) measure predictive accuracy at the individual metabolite level; (2) aggregate performance across all metabolites to report mean SCC;
   0 installs
9. ▌
   Spectral Feature Extraction And Integration · holobiomicslab

   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.
   0 installs
10. ▌
    Spectral Vector Representation And Encoding · holobiomicslab

    Use when when you have a set of mass spectra (query or library) that need to be searched against a large spectral reference database, and you want to use fast approximate nearest neighbor methods rather than exhaustive pairwise comparisons.
    0 installs
11. ▌
    Spectrum Processing Throughput Benchmarking · holobiomicslab

    Use when when selecting a spectrum processing library for high-throughput proteomics or metabolomics workflows, or when optimizing an existing pipeline for computational efficiency. Apply this skill when you have access to representative raw MS data (e.
    0 installs
12. ▌
    String Value Construction From Tabular Data · holobiomicslab

    Use when you have a JSON table (e.g., protocol records with 'type', 'id', and 'description' fields) and need to create a single concatenated string value by selecting a subset of records matching a condition (e.
    0 installs
13. ▌
    Structural Identifier Completeness Checking · holobiomicslab

    Use when preprocessing open mass spectrometry libraries (OMSLs) or aggregated spectral datasets where structural identifiers are inconsistently populated.
    0 installs
14. ▌
    Tensor Dimension Alignment And Broadcasting · holobiomicslab

    Use when when implementing a multi-task deep learning model that predicts charge, isotope count, and retention time simultaneously from mass spectrometry data, and separate feature extraction branches produce tensors of different semantic dimensions that must be integrated.
    0 installs
15. ▌
    Tool Parameter Specification And Validation · holobiomicslab

    Use when you have a working R package (e.g., IonFlow for ionomics data analysis) that performs well in standalone R environments, but need to expose it as a reusable Galaxy tool so that non-expert users can invoke it without writing R code, while preserving parameter semantics and validating input.
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16. ▌
    Unit Test Design For Data Structure Classes · holobiomicslab

    Use when when implementing a new data structure class that extends standard Python collections (e.g., collections.UserDict) and must support multiple initialization modes, operator overloading (__add__, __sub__), custom string formatting (__str__, __repr__), and validation logic.
    0 installs
17. ▌
    Visual Pattern Recognition In Spectral Data · holobiomicslab

    Use when after database search algorithms have scored unknown MS samples against reference species, and you need to visually inspect and confirm species assignments or identify ambiguous classifications.
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18. ▌
    CI Pipeline Reproducibility Verification · holobiomicslab

    Use when you have cloned a scientific Python project (e.g., scverse/scanpy) and need to verify that your local development environment matches the CI specification before submitting contributions, or when auditing whether the published test suite executes without failures on a fresh checkout.
    0 installs
19. ▌
    Dimensionality Reduction Via Reverse Pca · holobiomicslab

    Use when you have a normalized single-cell expression matrix (e.g., after SCTransform) and need to compute gene-level covariance structure for pathway enrichment analysis (e.g., GESECA) rather than cell-level dimensionality reduction.
    0 installs
20. ▌
    Gene Set Enrichment Scoring Coregulation · holobiomicslab

    Use when you have a normalized gene expression matrix (log2-quantile normalized, filtered to high-variance genes) and a collection of annotated gene sets (e.g., Reactome, MSigDB pathways), and need to test whether specific pathways show significant coordinated expression shifts.
    0 installs
21. ▌
    Gsea Result Interpretation Visualization · holobiomicslab

    Use when after running fgsea() on a preranked gene list when you need to: (1) identify which pathways are most significantly enriched or depleted (lowest p-values), (2) distinguish between upregulated pathways (ES > 0) and downregulated pathways (ES < 0) within your gene set collection, (3).
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22. ▌
    Hypothesis Weighting Statistical Control · holobiomicslab

    Use when when you have completed DESeq2 differential expression analysis on RNA-seq count data and obtained p-values for each gene, use IHW if you want to improve power to detect true positives beyond standard independent filtering.
    0 installs
23. ▌
    Lazy Computation Orchestration With Dask · holobiomicslab

    Use when your input is a spatial dataset (AnnData object with coordinate metadata) paired with a large tissue image, and you need to extract spatial features (via squidpy.im.calculate_image_features or similar operations) without loading the entire computation graph into memory.
    0 installs
24. ▌
    Linear Model Fitting For Gene Expression · holobiomicslab

    Use when you have a normalized gene expression matrix (genes × samples) and an experimental design with known treatment groups or conditions, and you need to estimate the effect of those conditions on expression levels while accounting for sample-to-sample variability.
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25. ▌
    Ma Plot Visualization And Interpretation · holobiomicslab

    Use when after obtaining shrunken or unshrunken log fold change estimates from DESeq2 results objects, particularly when comparing multiple shrinkage estimator types (apeglm, normal, ashr) or evaluating the effect of shrinkage on fold change estimates across genes with varying expression levels.
    0 installs
26. ▌
    Marker Gene Identification By Clustering · holobiomicslab

    Use when you have a processed single-cell expression matrix (AnnData object) with pre-computed cluster assignments (e.g., leiden or louvain clusters in adata.obs) and want to discover which genes define each cluster's transcriptional identity.
    0 installs
27. ▌
    Memory Efficient Single Cell Computation · holobiomicslab

    Use when your input is a single-cell gene expression matrix too large to fit in RAM, or you are working in a resource-constrained environment (e.g., shared compute cluster, laptop with limited memory). You have constructed or loaded an AnnData object with X as a dask.array.
    0 installs
28. ▌
    Pathway Cell Type Association Validation · holobiomicslab

    Use when you have a multi-cluster single-cell RNA-seq dataset with cell-type annotations and you want to test whether known biological pathways (e.g., KEGG or MSigDB gene sets) show significantly elevated or differential enrichment across cell types.
    0 installs
29. ▌
    Rna Seq Differential Expression Analysis · holobiomicslab

    Use when you have RNA-seq read count data (from alignment tools, transcript quantification, or feature counting) organized in a count matrix with samples as columns and genes as rows, paired with sample metadata (condition, batch, treatment), and you want to test which genes show statistically.
    0 installs
30. ▌
    Electrophoretic System Parameter Specification · holobiomicslab

    Use when you are preparing to perform effective mobility transformation of CE-MS data and must establish the electrophoretic system's calibration context.
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31. ▌
    Ms2 Spectrum Extraction And Consensus Building · holobiomicslab

    Use when when you have DDA LC-MS/MS data (mzML format) with identified chromatographic peaks at a specific m/z (e.g., 304.1131) and multiple MS2 spectra fragmented from that precursor, and you need to produce a single high-confidence MS2 spectrum for comparison against reference databases (e.
    0 installs
32. ▌
    Chromatogram Baseline Correction Preprocessing · holobiomicslab

    Use when you have raw or folded 2D-TIC chromatogram data (typically imported from NetCDF files into RGCxGC chromatogram objects) that exhibits baseline drift, chemical noise, or instrumental artifacts that would obscure true metabolite peaks.
    0 installs
33. ▌
    Hierarchical Clustering Parameter Optimization · holobiomicslab

    Use when after peak detection in GC-IMS preprocessing, when you need to group peaks across multiple samples and must decide whether euclidean distance is appropriate for your drift time and retention time coordinate space, and when you need to validate that your chosen dt_cluster_spread_ms and.
    0 installs
34. ▌
    Mass Spectrometry Matrix Background Simulation · holobiomicslab

    Use when when you need to create negative control or background-only reference datasets for LC/GC-MS analysis pipelines—specifically to validate peak-picking algorithms, assess false-positive rates, or simulate instrument background and matrix effects (e.
    0 installs
35. ▌
    Mass Spectrometry Tolerance Window Calibration · holobiomicslab

    Use when after feature extraction from raw LC-MS or GC-MS data (using XCMS, MS-Dial, or similar), when you have a feature intensity table with m/z and RT metadata and a reference compound database (known molecules list with m/z, RT, and annotation metadata), and you need to assign confidence-ranked.
    0 installs
36. ▌
    Molecular Identifier Completeness Verification · holobiomicslab

    Use when during MSP, MGF, JSON, or CSV file parsing when standardizing mass spectra from heterogeneous open mass spectral libraries (OMSLs).
    0 installs
37. ▌
    Spectral Batch Submission To Networking Server · holobiomicslab

    Use when you have deconvolved GC-MS spectra in GNPS_GC input-compatible format and want to construct a molecular network to identify relationships between unknown compounds and perform structured chemical similarity analysis.
    0 installs
38. ▌
    Spectral Library Matching And M Z Peak Ranking · holobiomicslab

    Use when you have raw GC-MS output in CSV format (with Component.RT, Base.Peak.MZ, Component.Area, Compound.Name, Match.Factor, and File.Name columns) and need to systematically rank putative identifications by match quality and exact mass agreement.
    0 installs
39. ▌
    Word2vec Model Inference Unknown Word Handling · holobiomicslab

    Use when when applying a pre-trained Word2Vec model to mass spectra at inference time (e.g., library matching or molecular networking), especially when the query spectra may contain fragment peaks or neutral losses not represented in the model's training vocabulary.
    0 installs
40. ▌
    Chimeric Spectra Classification Model Training · holobiomicslab

    Use when when your DDA-mode LC-MS/MS data exhibits chimeric spectra patterns that differ systematically from the reference training set used in DNMS2Purifier, or when you wish to optimize purification sensitivity/specificity for your particular instrument, ionization method, or sample matrix.
    0 installs
41. ▌
    Chromatogram And Ionization Intensity Modeling · holobiomicslab

    Use when when you need to simulate LC-MS/MS data for fragmentation strategy development and do not have (or wish to augment) real experimental chromatograms. Specifically: (1) you have a list of known or sampled chemical compounds with molecular formulae;
    0 installs
42. ▌
    Chromatographic Peak Detection And Integration · holobiomicslab

    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;
    0 installs
43. ▌
    Chromatographic Peak Quality Metric Evaluation · holobiomicslab

    Use when when processing untargeted LC-MS metabolomics data with XCMS and need to identify low-quality peak integrations that may introduce noise or bias into subsequent compound identification and quantification.
    0 installs
44. ▌
    Contrastive Learning For Cross Modal Retrieval · holobiomicslab

    Use when you have paired MS/MS spectra and molecular structures (SMILES or SDF format) and need to perform compound identification by retrieving the correct structure for an unknown spectrum.
    0 installs
45. ▌
    Distribution Shape Adaptation In Normalization · holobiomicslab

    Use when after imputation and correction, when metabolomic feature intensities exhibit heteroscedastic variance or non-normal distributions across samples.
    0 installs
46. ▌
    Domain Specific Spectrum Search Implementation · holobiomicslab

    Use when you have acquired one or more tandem MS/MS spectra and need to identify metabolites against a reference library filtered by biological domain (e.
    0 installs
47. ▌
    Feature Abundance Pattern Correlation Analysis · holobiomicslab

    Use when after initial retention-time-based feature grouping has been performed on LC-MS data but before final EIC similarity refinement.
    0 installs
48. ▌
    Feature Annotation Via Isotope Adduct Grouping · holobiomicslab

    Use when after peak detection and feature extraction have produced a composite feature table with m/z, retention time, and intensity values for individual samples.
    0 installs
49. ▌
    Feature Based Molecular Network Interpretation · holobiomicslab

    Use when you have a feature-based molecular network generated from non-targeted LC-MS/MS metabolomics data (e.
    0 installs
50. ▌
    Feature Quality Assessment By Rsd Within Class · holobiomicslab

    Use when after blank subtraction and background drift removal in an MS-DIAL peak list, when you need to exclude features with high within-class measurement variability. Apply this when you have replicate samples assigned to distinct classes (e.
    0 installs
51. ▌
    Feedforward Neural Network Architecture Design · holobiomicslab

    Use when you have preprocessed molecular structures as fixed-length feature vectors and need to establish a fair-comparison baseline model for tandem mass spectrum prediction.
    0 installs
52. ▌
    Intensity To Absolute Concentration Conversion · holobiomicslab

    Use when your lipidomics experiment includes spiked internal lipid standards of known concentration, and you have raw signal intensity matrices from LipidSearch or LIQUID output.
    0 installs
53. ▌
    Lipid Fingerprint Regeneration Neural Networks · holobiomicslab

    Use when you have MS/MS spectra with initial lipid annotations from spectral library matching (e.g., from XCMS + CAMERA or LipidIN's Expeditious Querying module) and seek to improve recall, precision, and annotation coverage.
    0 installs
54. ▌
    Machine Learning Performance Metric Evaluation · holobiomicslab

    Use when after training a binary MS/MS spectral classifier on labeled data, apply this skill to quantify classifier performance before deployment.
    0 installs
55. ▌
    Mass Spectra Clustering Hyperdimensional Space · holobiomicslab

    Use when you have large-scale MS/MS spectra datasets (hundreds of thousands to millions of spectra) in MGF format that need to be grouped by similarity, and you have access to NVIDIA GPU hardware (GTX 1080Ti or GTX 3090).
    0 installs
56. ▌
    Mass Spectrometry Peak Alignment Visualization · holobiomicslab

    Use when when you have aligned peak data from molecular networking (with m/z, intensity, retention time, and alignment quality metrics across multiple spectra) and need to interactively explore peak alignments under multiple filtering criteria (intensity thresholds, alignment score cutoffs, peak.
    0 installs
57. ▌
    Mass Spectrometry Reference Standard Alignment · holobiomicslab

    Use when you have positive- or negative-mode tunemix reference data (with known CCS values, m/z, and measured drift times) and need to establish a calibration model for converting observed drift times into CCS values for downstream feature annotation.
    0 installs
58. ▌
    Monte Carlo Dropout Uncertainty Quantification · holobiomicslab

    Use when when a trained Siamese neural network model makes predictions on new spectrum pairs and you need to identify and exclude high-uncertainty predictions to improve RMSE.
    0 installs
59. ▌
    Ms Ms Spectral Preprocessing And Normalization · holobiomicslab

    Use when you have a labelled dataset of raw MS/MS spectra annotated as 'relevant' (compounds of interest) or 'other' (reference standards or non-target compounds) and need to prepare them for supervised classifier training.
    0 installs
60. ▌
    Ms Ms Spectrum Tokenization And Representation · holobiomicslab

    Use when when you have raw MS/MS spectra in MSP format (or similar) with m/z–intensity peak pairs and need to prepare them for neural embedding models that require fixed-size discrete token inputs. Applies before generating dense spectral embeddings for retrieval or similarity scoring tasks.
    0 installs
61. ▌
    Multi Assay Data Integration And Harmonization · holobiomicslab

    Use when you have independent LC-MS assays (e.g., positive and negative ionization modes, different lipid profiling assays, or different chromatographic methods) analyzed on the same sample cohort and want to integrate them into a single discriminant or regression model without losing assay-level.
    0 installs
62. ▌
    Multi Window Mass Range Parameter Optimization · holobiomicslab

    Use when you observe sawtooth or discontinuous peak profiles in EICs after running tardisPeaks() on LC-MS data acquired with multiple overlapping or sequential m/z scan windows (e.g., positive and negative polarity scans, or data-independent acquisition with staggered windows).
    0 installs
63. ▌
    Multiplexed Spectra Recovery And Deconvolution · holobiomicslab

    Use when you have raw IM-MS data in UIMF or Agilent MassHunter .d format acquired from a multiplexed (interleaved) ion mobility experiment, and you need to recover individual, demultiplexed frames to reconstruct conventional IM-MS spectra for downstream omics analysis.
    0 installs
64. ▌
    Multivariate Statistical Analysis Metabolomics · holobiomicslab

    Use when when you have preprocessed non-targeted LC-MS/MS feature tables (post-merging, cleanup, blank removal, and batch correction) and seek to uncover multivariate patterns across samples, discriminate between experimental groups, or reduce dimensionality of high-dimensional metabolomic data.
    0 installs
65. ▌
    Nearest Neighbor Clustering By Mass Difference · holobiomicslab

    Use when processing LC-MS metabolomics studies with >10 samples where sample count and memory constraints make pairwise mass alignment infeasible.
    0 installs
66. ▌
    Network Component Identification And Filtering · holobiomicslab

    Use when you have a GNPS GraphML molecular network and need to isolate cohesive subsets of spectra (components) before analyzing which fragmentation patterns explain them.
    0 installs
67. ▌
    Neural Network Layer Design And Implementation · holobiomicslab

    Use when when replacing deprecated model components (e.
    0 installs
68. ▌
    Peak Map Rendering Retention Time Mz Intensity · holobiomicslab

    Use when when you have loaded mass spectrometry data (from mzML or Bruker .
    0 installs
69. ▌
    Peak Neutral Loss Occurrence Data Manipulation · holobiomicslab

    Use when you have aligned MS2 spectra from multiple samples and need to create a matrix representation where rows are samples and columns are MS2 peaks or neutral losses (mass differences to the precursor), with counts of occurrences.
    0 installs
70. ▌
    Percentile Feature Filtering For Normalization · holobiomicslab

    Use when you have a raw or pre-processed LC-MS feature table with multiple samples and need to normalize for inter-sample intensity biases before downstream statistical analysis.
    0 installs
71. ▌
    Performance Benchmarking Computational Systems · holobiomicslab

    Use when you have implemented or reconstructed a performance-critical computational module (e.g., an expeditious querying engine, a database lookup accelerator, or a real-time matching algorithm) and need to validate that it achieves claimed throughput targets.
    0 installs
72. ▌
    Probabilistic Topic Modeling Mass Spectrometry · holobiomicslab

    Use when you have preprocessed tandem mass spectrometry spectra converted into a bag-of-fragments representation (with fragments and neutral losses extracted and noise filtered) and your goal is to discover recurring fragmentation patterns or substructures across a large spectral dataset without.
    0 installs
73. ▌
    Python Class Interface Design For Ml Libraries · holobiomicslab

    Use when when building a machine learning library for scientific workflows where users need to: (1) prepare domain-specific data (e.
    0 installs
74. ▌
    Resolution Effect Correction Unlabeled Samples · holobiomicslab

    Use when when you have paired LC-MS data from both labeled (isotope-tracer dosed) and unlabeled (control) samples of the same analytes, and you want to correct FAM to true MDV while accounting for resolution effects from the mass spectrometer (especially relevant for high-resolution instruments.
    0 installs
75. ▌
    Retention Time Regression Output Specification · holobiomicslab

    Use when after initializing and executing a forward pass through a dual-branch RT-Transformer model (combining fingerprint and molecular graph inputs) on a batch of molecular samples, to verify that the output tensor conforms to the expected shape, data type, and numeric range for retention time.
    0 installs
76. ▌
    Ribosomally Synthesized Peptide Identification · holobiomicslab

    Use when you have LC-MS/MS spectral data (in MGF, mzXML, mzML, or mzData format) and corresponding genomic sequence data (raw FASTA nucleotide sequences or genome mining tool outputs like antiSMASH .final.
    0 installs
77. ▌
    Selectivity Metric Computation Chromatographic · holobiomicslab

    Use when after peak detection on composite mass tracks when you need to evaluate whether a detected peak represents a pure, interference-free signal on its m/z channel.
    0 installs
78. ▌
    Source Code Inspection For Preprocessing Steps · holobiomicslab

    Use when when you need to verify that a specific data transformation (e.g., precursor m/z zeroing, feature scaling, or field masking) is applied consistently across multiple execution workflows (training, evaluation, inference) in a codebase.
    0 installs
79. ▌
    Spectral Denoising Via Training Data Filtering · holobiomicslab

    Use when you have untargeted MS2 spectra from environmental or clinical samples that will be used for natural product identification (e.g., linking to BGCs via IOKR or other kernel-based methods), and you have access to a high-quality training library of annotated spectra with known structures.
    0 installs
80. ▌
    Spectral Format Conversion Mzml To Proprietary · holobiomicslab

    Use when after clustering features with RAMClustR and inferring molecular weights via do.findmain, when you need to perform structure elucidation or molecular formula prediction using MSFinder (for .mat format) or Sirius (for .ms format).
    0 installs
81. ▌
    Spectral Peak Frequency Threshold Optimization · holobiomicslab

    Use when you have MS/MS spectra with fragment frequency annotations (from consensus spectrum generation) and need to decide which fragments to retain versus remove. Trigger conditions: (1) you have replicate MS/MS spectra for the same feature with per-fragment recurrence frequencies calculated;
    0 installs
82. ▌
    Spectral Similarity Prediction Neural Networks · holobiomicslab

    Use when when you have paired MS/MS spectra with known structural similarity labels (Tanimoto scores from molecular fingerprints) and need to predict structural similarity for new spectrum pairs faster than fingerprint-based methods, or when you want to assess model prediction confidence per.
    0 installs
83. ▌
    Spectrum Preprocessing For Similarity Analysis · holobiomicslab

    Use when when you have raw MS/MS spectra with residual noise or low-intensity peaks and plan to calculate spectral entropy, entropy similarity, or perform spectral library matching.
    0 installs
84. ▌
    Statistical Hypothesis Testing Mean Comparison · holobiomicslab

    Use when you have computed raw or standardised correlation scores (or other link-ranking metrics) for all possible GCF-MF pairs in a dataset and want to verify that validated links (those with known strain co-occurrence or experimental confirmation) are significantly enriched at higher score values.
    0 installs
85. ▌
    Tandem Mass Spectrum Prediction Fragment Level · holobiomicslab

    Use when you have a molecular structure (SMILES, InChI, or chemical formula) and need to predict its collision-induced dissociation (CID) tandem mass spectrum with fragment-level resolution. Use this when chemical-formula-level predictions (e.
    0 installs
86. ▌
    Technical Replicate Reproducibility Assessment · holobiomicslab

    Use when you have tandem MS data with technical replicates and need to remove features showing high variability between replicates.
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87. ▌
    Thermo Fisher Orbitrap Metadata Interpretation · holobiomicslab

    Use when when you have a Thermo Fisher Scientific .
    0 installs
88. ▌
    Univariate Statistical Analysis Interpretation · holobiomicslab

    Use when you have a preprocessed feature table from non-targeted LC-MS/MS metabolomics data (after data merging, cleanup, blank removal, and batch correction) and need to test whether individual metabolomic features show statistically significant differences between experimental groups or.
    0 installs
89. ▌
    Untargeted Metabolomics Feature Interpretation · holobiomicslab

    Use when you have an untargeted metabolomics feature table (m/z values, retention times, p-values from statistical testing) and need to infer which metabolic pathways are active without performing metabolite identification.
    0 installs
90. ▌
    Cohort Stratified Metabolic Performance Analysis · holobiomicslab

    Use when when you have uploaded a pre-analytical data table containing sample metadata, processing delay annotations (pre- and post-centrifugation times), and paired NMR metabolomic measurements for a plasma or serum cohort, and you need to determine how processing delays impact metabolite.
    0 installs
91. ▌
    Heteronuclear Homonuclear Correlation Extraction · holobiomicslab

    Use when you have 2D NMR spectral data from multiple samples (a large sample set) including both heteronuclear experiments (HSQC, HMBC) and homonuclear experiments (COSY), and your goal is to identify which structural features are conserved across samples, prioritize samples by scaffold similarity.
    0 installs
92. ▌
    Nmr Spectra Preprocessing And Feature Extraction · holobiomicslab

    Use when when you have raw or lightly processed 1D NMR spectra (¹H and/or ¹³C) from unknown organic compounds and need to extract latent spectral features prior to structure elucidation.
    0 installs
93. ▌
    Python Deep Learning Model Loading And Execution · holobiomicslab

    Use when when you have a pre-trained deep learning model checkpoint (saved in PyTorch format) and new 1D 1H NMR spectral data in CSV and peak-list TXT formats, and you need to generate peak-to-metabolite assignments or other structured outputs from that model without modification of model weights.
    0 installs
94. ▌
    Spectral Visualization And Result Interpretation · holobiomicslab

    Use when after peak clustering, network filtering, and database matching have identified candidate metabolites and their associated peak networks.
    0 installs
95. ▌
    Chromatin Accessibility Deviation Computation · holobiomicslab

    Use when when you have filtered ATAC-seq or DNAse-seq peak counts (after GC bias correction, sample filtering, and peak filtering) and wish to measure how strongly each annotation (motif or kmer) influences chromatin accessibility variability in each sample relative to a background expectation.
    0 installs
96. ▌
    Library Module Organization And Accessibility · holobiomicslab

    Use when you are building or extending a multi-module Python library for scientific computation (e.
    0 installs
97. ▌
    Methylation Region Genomic Context Assignment · holobiomicslab

    Use when after identifying differentially methylated bases or regions (via calculateDiffMeth() and getMethylDiff()), when you need to characterize WHERE these methylation changes occur relative to gene structure and CpG density landscapes.
    0 installs
98. ▌
    Sample Similarity Assessment From Methylation · holobiomicslab

    Use when after merging methylation calls across all samples using unite() to create a methylBase object, apply this skill to characterize whether replicate samples cluster together and to visualize methylation-driven separation between biological groups (e.g., test vs. control).
    0 installs
99. ▌
    Single Cell Atac Seq Dimensionality Reduction · holobiomicslab

    Use when after loading and preprocessing raw scATAC-seq data into an ArchR project object when you need to compute low-dimensional embeddings for clustering, UMAP/tSNE visualization, or integrated multi-omic analysis.
    0 installs
100. ▌
     Chemical Structure Representation Conversion · holobiomicslab

     Use when when you have .msp mass spectrometry metadata containing chemical identifiers (e.g., compound names or SMILES strings) and need to compute derived chemical properties (e.g., InChI, InChIKey, molecular formula) locally without network latency or service availability constraints.
     0 installs