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omicverse

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1. ▌
   Gsea Enrichment Analysis · omicverse

   Gene set enrichment analysis with correct geneset format handling. Critical guidance for loading pathway databases and running enrichment in OmicVerse.
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   Foundation Model Analysis · omicverse bundle

   Foundation model workflows: scGPT, Geneformer, UCE, CellPLM cell embedding, annotation, integration via ov.fm unified API. 22 models.
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   Data Viz Plots · omicverse

   Publication-quality matplotlib/seaborn plots: scatter, heatmap, violin, bar, line, multi-panel figures. Works with ANY LLM provider.
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   Data Io Loading · omicverse bundle

   OmicVerse data I/O: use ov.read(), ov.io.read_h5ad, read_10x_h5, read_10x_mtx, read_visium, read_visium_hd, read_nanostring instead of scanpy. Covers h5ad, 10x, spatial, CSV formats.
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   Datasets Loading · omicverse bundle

   OmicVerse built-in datasets: pbmc3k, pancreas, dentategyrus, zebrafish, immune, spatial, multiome, plus create_mock_dataset() and predefined_signatures GMT gene sets.
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   String Protein Interaction Analysis With Omicverse · omicverse bundle

   STRING protein-protein interaction network analysis with pyPPI: query STRING database, build PPI graphs, expand with add_nodes, and visualize styled networks for bulk gene lists.
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   Single Cell Annotation Skills With Omicverse · omicverse bundle

   Cell type annotation: SCSA, MetaTiME, CellVote consensus, CellMatch, GPTAnno, weighted KNN label transfer in OmicVerse.
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   Single Cell Clustering And Batch Correction With Omicverse · omicverse bundle

   Single-cell clustering (Leiden, Louvain, scICE, GMM), batch correction (Harmony, scVI, BBKNN, Combat), topic modeling, and cNMF in OmicVerse.
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   Single Cell Multi Omics Integration · omicverse bundle

   Multi-omics integration: MOFA factor analysis, GLUE unpaired alignment, SIMBA batch correction, TOSICA label transfer, StaVIA trajectory. Covers scRNA+scATAC paired/unpaired workflows.
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    Scenic Gene Regulatory Network · omicverse bundle

    SCENIC gene regulatory network: RegDiffusion GRN inference, cisTarget regulon pruning, AUCell scoring, RSS, regulon embeddings in OmicVerse.
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    Single Trajectory Analysis · omicverse bundle

    Trajectory & RNA velocity: PAGA, Palantir, VIA, dynamo, scVelo, latentvelo, graphvelo backends via ov.single.Velo. Pseudotime, stream plots.
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    Tcga Bulk Data Preprocessing With Omicverse · omicverse bundle

    TCGA bulk RNA-seq preprocessing with pyTCGA: GDC sample sheets, expression archives, clinical metadata, Kaplan-Meier survival analysis, and annotated AnnData export.
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    Bulk Wgcna Analysis With Omicverse · omicverse bundle

    WGCNA co-expression network: soft-threshold, module detection, eigengenes, hub genes, and trait correlation in OmicVerse.
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    Single Cell Cellphonedb Communication Mapping · omicverse bundle

    CellPhoneDB v5 ligand-receptor analysis, CellChatViz plots, and the newer ccc_heatmap / ccc_network_plot / ccc_stat_plot communication visualizations in OmicVerse.
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    Biocontext Knowledge Queries · omicverse bundle

    BioContext knowledge: UniProt, AlphaFold, STRING, Reactome, GO, PanglaoDB, PubMed, OpenTargets queries via ov.biocontext for gene annotation.
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    Bulk Rna Seq Deseq2 Analysis With Omicverse · omicverse bundle

    PyDESeq2 differential expression: ID mapping, DE testing, fold-change thresholding, and GSEA enrichment visualization in OmicVerse.
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    Single Cell Preprocessing With Omicverse · omicverse bundle

    Single-cell QC, normalization, HVG detection, PCA, neighbor graph, UMAP/tSNE embedding pipelines in OmicVerse (CPU/GPU).
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    Bulk Rna Seq Batch Correction With Combat · omicverse bundle

    Bulk RNA-seq batch correction with pyComBat: remove batch effects from merged cohorts, export corrected matrices, and benchmark visualizations.
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    Single Popv Annotation · omicverse bundle

    PopV population-level cell annotation: 10 algorithms (SCVI, SCANVI, CellTypist, OnClass, RF, SVM, XGBoost, BBKNN, HARMONY, SCANORAMA), consensus voting, pretrained hub models.
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    Cellfate Pseudotime Gene Analysis · omicverse bundle

    CellFateGenie: Adaptive Threshold Regression for pseudotime-associated gene discovery, Mellon density, lineage scoring via ov.single.Fate.
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    Single2spatial Spatial Mapping · omicverse bundle

    Map scRNA-seq atlases onto spatial transcriptomics slides using omicverse's Single2Spatial workflow for deep-forest training, spot-level assessment, and marker visualisation.
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    Single Cell Downstream Analysis · omicverse bundle

    AUCell pathway scoring, metacell DEG, scDrug response, SCENIC regulons, cNMF programs, and NOCD community detection in OmicVerse.
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    Bulk Rna Seq Deconvolution With Bulk2single · omicverse bundle

    Turn bulk RNA-seq cohorts into synthetic single-cell datasets using omicverse's Bulk2Single workflow for cell fraction estimation, beta-VAE generation, and quality control comparisons against reference scRNA-seq.
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    Bulktrajblend Trajectory Interpolation · omicverse bundle

    Extend scRNA-seq developmental trajectories with BulkTrajBlend by generating intermediate cells from bulk RNA-seq, training beta-VAE and GNN models, and interpolating missing states.
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    Bulk Rna Seq Differential Expression With Omicverse · omicverse bundle

    Bulk RNA-seq DEG pipeline: gene ID mapping, DESeq2 normalization, statistical testing, volcano plots, and pathway enrichment in OmicVerse.
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    Spatial Transcriptomics Tutorials With Omicverse · omicverse bundle

    Spatial transcriptomics: Visium/HD, Stereo-seq, Slide-seq preprocessing (crop, rotate, cellpose), deconvolution (Tangram, cell2location, Starfysh), clustering (GraphST, STAGATE), integration, trajectory, communication.
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    Omicverse Single Cell Scenic · omicverse bundle

    Convert OmicVerse SCENIC notebooks into a reusable, triggerable skill for single-cell AnnData regulon analysis. Use when initializing SCENIC with cisTarget resources, choosing the RegDiffusion, GRNBoost2, or GENIE3 GRN branch, tuning regulon-construction thresholds, or running downstream RSS, binarization, and regulon-focused GRN exploration.
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    Omicverse Micro Metabol Paired · omicverse bundle

    Paired microbiome × metabolomics integration on sample-aligned AnnDatas. Provides three methods - Spearman correlation (fast pairwise FDR), sklearn CCA (linear canonical mode shared between modalities), and MMvec (PyTorch low-rank co-occurrence model with conditional probabilities and biplot embeddings). Use when you have paired microbe-counts + metabolite-intensity tables on the same samples and want to find microbe-metabolite pairs that covary, or build a shared latent space.
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    Omicverse Microbiome Phylogeny · omicverse bundle

    Build an ASV-level phylogenetic tree (MAFFT alignment + FastTree GTR+Γ), attach it to a 16S AnnData, and run phylogenetically-aware diversity (Faith PD, weighted / unweighted UniFrac). Use when adding tree-aware metrics on top of the basic 16S amplicon AnnData, or when the cohort needs UniFrac-driven beta-diversity instead of Bray-Curtis.
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    Omicverse Visualization For Bulk Color Systems And Single Ce · omicverse bundle

    OmicVerse plotting: volcano, venn, boxplot, embedding, density, dotplot, convex hull, stacked bar, and Forbidden City color palettes.
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    Omicverse Single Cell Annotation · omicverse bundle

    Annotate single-cell AnnData with OmicVerse using the CellTypist, gpt4celltype, or SCSA branches. Use when turning OmicVerse annotation notebooks into a reusable, triggerable skill, selecting a backend, or mapping a clustered AnnData object to cell-type labels.
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    Omicverse Single Cell Cytotrace2 · omicverse bundle

    Predict single-cell developmental potency with OmicVerse CytoTRACE2 on AnnData. Use when converting an OmicVerse CytoTRACE2 notebook into a reusable skill, when running the pretrained CytoTRACE2 potency workflow on mouse or human scRNA-seq data, or when choosing preprocessing mode, species, and parallelization settings for potency scoring and optional embedding overlays.
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    Omicverse Microbiome Da Comparison · omicverse bundle

    Run all three differential-abundance methods (Wilcoxon, pyDESeq2, ANCOM-BC) on the same microbiome AnnData, compare their hit sets via 3-way Venn / overlap counts, and decide which to trust on a given cohort. Use when the user wants to benchmark DA methods on a 16S study, when picking between methods on a small or zero-inflated cohort, or when reporting consensus features that survive multiple tests.
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    Omicverse Microbiome Meta Analysis · omicverse bundle

    Combine multiple per-study microbiome AnnDatas into a single cross-cohort table and run inverse-variance / random-effects meta-analysis on differential abundance. Use when you have 16S studies from multiple cohorts that need joint analysis, when you need a combined log2 fold-change with Cochran's I² heterogeneity, or when you want to find features whose effect replicates across cohorts (vs. cohort-specific signals).
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    Omicverse Reference Label Transfer · omicverse bundle

    Transfer cell labels from a reference AnnData to a query AnnData with OmicVerse AnnotationRef. Use when converting OmicVerse reference-annotation notebooks into a reusable, triggerable skill, choosing a label-transfer backend such as harmony, scVI, or scanorama, or preparing paired query/reference single-cell data for weighted kNN annotation.
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    Omicverse Single Cell Kb Alignment · omicverse bundle

    Build a triggerable kb reference and quantify single-cell FASTQs with OmicVerse alignment.single. Use when converting OmicVerse alignment notebooks into a reusable skill, when creating kallisto|bustools references with ref, or when running count on single-cell RNA-seq libraries.
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    Omicverse Single Cell Rna Velocity · omicverse bundle

    Analyze single-cell AnnData for RNA velocity with OmicVerse. Use when converting OmicVerse velocity notebooks into a reusable, triggerable skill, when deciding whether a velocity notebook subset or branch should update an existing skill, or when selecting the scvelo, dynamo, latentvelo, graphvelo, recipe, backend, or mode branches for velocity preprocessing, dynamics, and embedding.
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    Bulk Fastq Quantification · omicverse bundle

    End-to-end bulk RNA-seq quantification with omicverse's alignment module — SRA download, fastp QC, two interchangeable quantification paths (STAR + featureCount, OR alignment-free kb-python with technology='BULK'), and wiring into `ov.bulk.pyDEG` DESeq2. Single-cell kb-python (10XV2/10XV3) is out of scope — use the `single-cell-kb-alignment` skill instead.
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    Omicverse Bulk Metabol Multivariate · omicverse bundle

    Multivariate discrimination and biomarker selection on a preprocessed metabolomics AnnData. Use when running PLS-DA, OPLS-DA, VIP / S-plot inspection, per-metabolite ROC AUC with bootstrap confidence intervals, or building a multi-metabolite biomarker panel with nested CV and a permutation null. Assumes the input has already been imputed, PQN-normalized, and log-then-Pareto-transformed.
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    Omicverse Single Cell Cellrank Fate · omicverse bundle

    CellRank fate maps from RNA velocity. Combine VelocityKernel + ConnectivityKernel into a transition matrix, fit a GPCCA estimator, predict terminal states, and produce per-cell fate probabilities. Visualise with `ov.pl.branch_streamplot` and feed branch-resolved gene-trends into `ov.single.dynamic_features` / `ov.pl.dynamic_trends` / `ov.pl.dynamic_heatmap`. Use after RNA velocity is computed (scvelo / dynamo / latentvelo / graphvelo) and before reporting fate probabilities or marker dynamics.
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    Omicverse Single Cell Preprocessing · omicverse bundle

    Convert OmicVerse single-cell preprocessing and marker-discovery notebooks into a reusable, triggerable skill. Use when cleaning AnnData, choosing a preprocessing mode, building PCA and neighborhood graphs, clustering with Leiden, or extracting marker genes with OmicVerse.
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    Omicverse Bulk Metabol Preprocessing · omicverse bundle

    Run the canonical metabolomics preprocessing chain on an AnnData peak table — impute, normalize, transform — and apply LC-MS-specific drift / batch / sample QC corrections (drift_correct, SERRF, ComBat, sample_qc). Use when converting `t_metabol_01_intro` or `t_metabol_06_batch_correction` into a reusable skill, when a user has a MetaboAnalyst CSV / LC-MS peak table to clean before differential or multivariate analysis, or when choosing among PQN/TIC/median sample-normalization, log/Pareto feature-transformation, and qrilc/knn/half_min/zero imputation strategies.
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    Omicverse Single Cell Via Trajectory · omicverse bundle

    VIA (Stassen 2021) single-cell trajectory inference via `omicverse.single.pyVIA` - PARC-clustering + lazy-teleporting random-walk + automated terminal-state detection + temporal gene-trend GAMs. Two modes - vanilla (gene-distance only) and RNA-velocity-guided (`velocity_matrix`, `gene_matrix`, `velo_weight`). Use when running VIA / scVelo+VIA on AnnData, when reproducing `t_via` or `t_via_velo`, or when picking the right `velo_weight` for velocity-driven topology.
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    Omicverse Bulk Celltype Deconvolution · omicverse bundle

    Cell-type composition deconvolution of bulk RNA-seq using a single-cell reference. Wraps `ov.bulk.Deconvolution` with four interchangeable backends — TAPE, Scaden, BayesPrism, OmicsTweezer — under one constructor / one `.deconvolution(method=...)` call. Use when inferring cell-type fractions from bulk samples given a paired scRNA-seq atlas, when reproducing `t_decov_bulk`, or when comparing deep-learning vs. Bayesian deconvolution methods on the same cohort.
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    Omicverse Single Cell Foundation Model · omicverse bundle

    Cell embedding, cell-type annotation, batch integration, and (where supported) perturbation prediction with single-cell foundation models — scGPT, Geneformer, scFoundation, UCE, CellPLM. Driven by the unified `ov.llm.SCLLMManager` interface; one object handles model loading, inference, and (optional) fine-tuning.
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    Omicverse Cross Modal Celltype Transfer · omicverse bundle

    Transfer cell-type labels from a reference AnnData to a query AnnData with OmicVerse weighted KNN over a shared embedding. Use when converting OmicVerse cross-modal annotation notebooks into a reusable skill, when labeling an ATAC query from an RNA reference, or when you need the weighted_knn_trainer / weighted_knn_transfer workflow plus optional visualization.
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    Omicverse Microbiome 16s Amplicon Dada2 · omicverse bundle

    16S rRNA amplicon analysis from raw FASTQs to a samples × ASVs AnnData with 7-rank SINTAX taxonomy, plus the canonical alpha / beta / ordination / DA stack. Use when running the vsearch-or-DADA2 amplicon pipeline (`ov.alignment.amplicon_16s_pipeline`), when ingesting an existing OTU/ASV count table with `build_amplicon_anndata`, or when computing Shannon / Bray-Curtis / PCoA / Wilcoxon-DA on the resulting AnnData.
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    Omicverse Single Cell Batch Integration · omicverse bundle

    Run OmicVerse single-cell batch integration as a reusable, triggerable skill after preprocessing is already complete. Use when choosing a batch correction backend such as harmony, combat, scanorama, scVI, CellANOVA, or Concord on a preprocessed AnnData with batch labels, or when benchmarking integrated embeddings from one of those backends.
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    Omicverse Single Cell Sctour Trajectory · omicverse bundle

    Run the OmicVerse sctour trajectory branch on raw-count single-cell AnnData. Use when adapting the scTour part of an OmicVerse trajectory notebook, or when you need sctour pseudotime, latent space, or vector-field outputs instead of the diffusion_map, slingshot, or palantir branches.
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    Omicverse Single Cell Cellmatch Ontology · omicverse bundle

    Map free-text cell-type annotations to the Cell Ontology (CL) via NLP-based sentence-transformer matching, with optional LLM-driven abbreviation expansion and Cell Taxonomy taxonomy enrichment. Use when standardising author-shorthand cell-type names to canonical CL terms (e.g. 'TIL-1' → 'tissue-resident memory CD8+ T cell, CL:0000625'), when running cross-cohort label harmonisation, or when reproducing `t_cellmatch`.
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    Omicverse Single Cell Cellvote Consensus · omicverse bundle

    Multi-annotator consensus for single-cell labels via `ov.single.CellVote`. Combine labels from any subset of SCSA / gpt4celltype / GPTBioInsightor / scMulan / PopV per cluster; resolve disagreement either by LLM arbitration (online) or local-majority voting (offline). Output is `obs['CellVote_celltype']`. Use when you have multiple annotators on the same AnnData and need a single consensus label, or when reproducing `t_cellvote_pbmc3k`.
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    Omicverse Single Cell Clustering Backends · omicverse bundle

    Run and compare OmicVerse single-cell clustering backends as a reusable, triggerable skill. Use when choosing between Leiden, Louvain, scICE, or GMM Gaussian mixture clustering on a prepared AnnData embedding, or when adapting a related OmicVerse clustering notebook into a repeatable workflow.
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    Omicverse Single Cell Liana Communication · omicverse bundle

    LIANA+ ligand-receptor inference on single-cell AnnData via `ov.single.run_liana`, plus the OmicVerse cell-cell communication (CCC) plotting stack with `ov.pl.ccc_heatmap` and `ov.single.to_comm_adata`. Use when computing ligand-receptor scores from a labeled AnnData (`bulk_labels` / `cell_type` / etc.), when post-processing LIANA results into a CommAnnData for pathway-aware visualisation, or when reproducing `t_ccc_liana`.
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    Omicverse Single Cell Metatime Annotation · omicverse bundle

    Tumor microenvironment (TME) cell-state annotation via the pretrained MetaTiME meta-components (Yi et al. 2023). Three-step workflow on a batch-corrected AnnData - over-cluster at high Leiden resolution, score against pretrained MeCs, write MetaTiME / Major_MetaTiME labels into obs. Use when annotating tumor scRNA-seq cohorts, when running MetaTiME on top of an scVI / Harmony-integrated embedding, or when reproducing `t_metatime`.
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    Omicverse Single Cell Monocle2 Trajectory · omicverse bundle

    Monocle2-style single-cell trajectory analysis on AnnData via the `ov.single.Monocle` class - DDRTree pseudotime + branch detection + per-gene differential test + BEAM branch-dependent gene discovery, plus the unified `ov.pl.trajectory` / `ov.pl.trajectory_overlay` / `ov.pl.trajectory_tree` plotters and the shared pseudotime visualisations (`branch_streamplot`, `dynamic_heatmap`, `dynamic_trends`). Use when fitting a Monocle2 trajectory on an annotated AnnData, when deriving branch-aware gene trends with `dynamic_features`, or when reproducing `t_traj_monocle2`.
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    Omicverse Bulk Metabol Pathway Multifactor · omicverse bundle

    Pathway interpretation, multi-factor designs, differential correlation, MOFA multi-omics, and the MTBLS1 real-data case study on a preprocessed metabolomics AnnData. Use when you need MSEA ORA / GSEA pathway enrichment with KEGG IDs, multi-factor ANOVA-SCA / mixed-model / MEBA designs, DGCA differential correlation networks, MOFA+ joint factorization across metabolomics + RNA-seq, or a worked end-to-end T2D study from MetaboLights ingestion to biomarker panel.
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    Omicverse Single Cell Lda Topic Clustering · omicverse bundle

    Run OmicVerse single-cell LDA topic clustering with the MIRA backend as a reusable, triggerable skill. Use when fitting topic models on count-like AnnData, choosing between in-memory and on-disk execution, or converting topic assignments into hard cluster labels and RFC-based labels.
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    Omicverse Single Cell Trajectory Inference · omicverse bundle

    Run or adapt OmicVerse single-cell trajectory inference on cluster-ready AnnData. Use when converting OmicVerse trajectory notebooks into a reusable skill, or when choosing the diffusion_map, slingshot, palantir, PAGA, or Palantir branch-selection branches for developmental ordering and lineage summaries.
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    Omicverse Bulk Metabol Untargeted Lipidomics · omicverse bundle

    Two adjacent LC-MS workflows on AnnData — (1) untargeted metabolomics with m/z-based peak annotation, mummichog pathway inference and adduct-ppm matching, and (2) lipidomics with LIPID MAPS shorthand parsing, lipid-class aggregation, and LION term enrichment. Use when converting `t_metabol_04_untargeted` or `t_metabol_05_lipidomics` into a reusable skill, when the input feature IDs encode `m/z`/`RT`, or when the var_names look like `PC 34:1` / `Cer d18:1/24:0` / `TAG 54:3`.
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    Single Cell Cnmf Program Discovery · omicverse bundle

    OmicVerse Single-Cell NMF / cNMF Program Discovery
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    Omicverse Single Cell Differential Abundance · omicverse bundle

    Run OmicVerse single-cell differential abundance or compositional analysis as a reusable, triggerable skill. Use when comparing cell-type abundance between conditions in AnnData, choosing between scCODA, milopy, and milo backends, or adapting a related notebook into a repeatable DCT workflow.
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    Omicverse Single Cell Differential Expression · omicverse bundle

    Run OmicVerse single-cell differential expression analysis as a reusable, triggerable skill. Use when comparing conditions inside one or more cell types in AnnData, choosing between Wilcoxon, t-test, and memento backends, or adapting a related notebook into a repeatable DEG workflow.
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    Omicverse Single Cell Cellphonedb Communication · omicverse bundle

    Analyze single-cell cell-cell communication with OmicVerse CellPhoneDB and CellChat-style visualization. Use when converting an OmicVerse CellPhoneDB notebook into a reusable skill, when running CellPhoneDB ligand-receptor analysis on annotated AnnData, or when choosing pathway aggregation, layout, signaling-role, and bubble-plot branches for downstream communication summaries.
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