User API

Import OmicVerse as:

import omicverse as ov

This page is auto-generated from @register_function entries in the OmicVerse registry.

Public registry entries listed here: 414

Top-Level API

generate_reference_table

Generate a standardized reference table from adata.uns['REFERENCE_MANU'].

Settings

settings.cpu_gpu_mixed_init	Initialize CPU-GPU mixed mode for accelerated single-cell analysis.
settings.gpu_init	Initialize GPU mode with RAPIDS for accelerated single-cell analysis.

Data IO

io.load	Load serialized Python object from disk.
io.read	Read common omics file formats into AnnData or pandas DataFrame.
io.read_10x_h5	Read a 10x Genomics HDF5 matrix file.
io.read_10x_mtx	Read a 10x Genomics Matrix Market directory.
io.read_csv	Read a CSV / TSV file via pandas.read_csv with a mandatory duplicate-column scan on the raw header.
io.read_h5ad	Read an .h5ad file.
io.read_nanostring	Read Nanostring formatted dataset.
io.read_visium_hd	Read 10x Visium HD outputs with a single entry point.
io.read_visium_hd_bin	Read Visium HD bin-level output and attach spatial metadata.
io.read_visium_hd_seg	Read Visium HD cell-segmentation output and attach geometries + spatial metadata.
io.read_xenium	Read a 10x Xenium outs directory into an AnnData object.
io.save	Save Python object to file using pickle fallback strategy.
io.spatial.read_visium	Read 10x-Genomics-formatted Visium dataset.

Alignment

alignment.amplicon_16s_pipeline	Run the full 16S amplicon pipeline.
alignment.build_amplicon_anndata	Compose anndata.AnnData from vsearch stepwise outputs.
alignment.build_phylogeny	Build a phylogenetic tree end-to-end.
alignment.bulk_rnaseq_pipeline	Run a complete bulk RNA-seq pipeline from SRA accessions or local FASTQs.
alignment.count	Quantify expression matrices from FASTQ files via kb count.
alignment.cutadapt	Run cutadapt to remove amplicon PCR primers.
alignment.dada2_pipeline	Run pydada2 end-to-end and return an AnnData.
alignment.fastp	Run fastp QC.
alignment.fasttree	Run FastTree to infer a phylogenetic tree.
alignment.featureCount	Run featureCounts on BAM files.
alignment.fetch_rdp	Alias for fetch_sintax_ref('rdp_16s_v18', db_dir=...).
alignment.fetch_silva	Alias for fetch_sintax_ref('silva_16s_v123', db_dir=...).
alignment.fetch_sintax_ref	Download a SINTAX-formatted 16S reference FASTA.
alignment.fqdump	Convert SRA accessions to FASTQ.
alignment.mafft	Run MAFFT multiple sequence alignment.
alignment.parallel_fastq_dump	Download SRA data in parallel using parallel-fastq-dump.
alignment.prefetch	Prefetch SRA accessions with validation.
alignment.ref	Build kallisto index and transcript-to-gene mapping files via kb ref.
alignment.STAR	Run STAR alignment.

Preprocessing (pp)

pp.anndata_to_CPU	Migrate AnnData objects from GPU back to CPU memory after analysis.
pp.anndata_to_GPU	Migrate AnnData objects to GPU memory for accelerated processing.
pp.binary_search	Infer a size factor from one normalized expression vector.
pp.champ	Pick the modularity-stablest Leiden partition via CHAMP (Weir et al. 2017).
pp.filter_cells	Filter cell outliers based on counts and numbers of genes expressed.
pp.filter_genes	Filter genes based on number of cells or counts.
pp.highly_variable_features	Select highly variable features (HVF/HVG) for downstream modeling.
pp.highly_variable_genes	Annotate highly variable genes (Satija 2015 / Zheng 2017 / Stuart 2019).
pp.identify_robust_genes	Identify robust genes for downstream HVG selection.
pp.leiden	leiden clustering
pp.log1p	Log-transform expression values with log(1 + x).
pp.louvain	Run Louvain clustering on the precomputed kNN graph.
pp.mde	Run MDE (Minimum Distortion Embedding) from a latent representation.
pp.neighbors	Compute a neighborhood graph of observations [McInnes18].
pp.normalize_pearson_residuals	Normalize a count matrix using analytic Pearson residuals (Lause 2021).
pp.pca	Performs Principal Component Analysis (PCA) on the data stored in a scanpy AnnData object.
pp.preprocess	Preprocesses the AnnData object adata using either a scanpy or a pearson residuals workflow for size normalization and highly variable genes (HVGs) selection, and calculates signature scores if necessary.
pp.qc	Perform quality control on a dictionary of AnnData objects.
pp.recover_counts	Given log-normalized gene expression data, recover the raw read/UMI counts by inferring the unknown size factors.
pp.regress	Regress out technical covariates (mito_perc, nUMIs) from each gene.
pp.regress_and_scale	Scale the regressed layer and store it as a new analysis layer.
pp.remove_cc_genes	Remove cell-cycle-correlated genes from highly_variable_features.
pp.scale	Scale the input AnnData object.
pp.score_genes_cell_cycle	Score cell cycle phases using predefined or custom gene sets.
pp.scrublet	Predict cell doublets using Scrublet with optional GPU acceleration.
pp.scrublet_simulate_doublets	Simulate synthetic doublets from random cell pairs.
pp.select_hvf_pegasus	Select highly variable features with the Pegasus strategy.
pp.sude	SUDE (Scalable Unsupervised Dimensionality reduction via Embedding) dimensionality reduction.
pp.tsne	Compute t-SNE coordinates for cells, dispatching by ov.settings.mode.
pp.umap	Compute UMAP embedding, dispatching to the best backend for ov.settings.mode.

Single-cell (single)

single.Annotation	Unified single-cell annotation manager for cell-type labeling.
single.AnnotationRef	Reference-based label transfer helper for single-cell annotation.
single.auto_resolution	Pick the most reproducible Leiden resolution via null-adjusted bootstrap-ARI (Lange, Roth, Braun & Buhmann, Neural Computation 2004).
single.autoResolution	Pick the most reproducible Leiden resolution via null-adjusted bootstrap-ARI (Lange, Roth, Braun & Buhmann, Neural Computation 2004).
single.batch_correction	Run batch-effect correction for single-cell data integration.
single.CellOntologyMapper	Map free-text cell-type annotations to the Cell Ontology (CL) via NLP.
single.CellVote	Ensemble cell-type annotation manager with multiple backends.
single.cNMF	Consensus NMF workflow wrapper for robust gene-program discovery.
single.convert_human_to_mouse_network	Convert a human-symbol interaction network to mouse symbols.
single.cosg	Identify cluster-specific marker genes with COSG.
single.cytotrace2	Predict developmental potency with CytoTRACE2.
single.DCT	Differential cell-type abundance testing wrapper.
single.DEG	Differential gene-expression testing wrapper for single-cell datasets.
single.download_cellphonedb_database	Download CellPhoneDB database with fallback URLs.
single.download_cl	📥 Download Cell Ontology file from multiple sources with automatic fallback
single.Drug_Response	Predict drug sensitivity from single-cell transcriptomes using CaDRReS models.
single.dynamic_features	Fit GAM-based pseudotime trends for one or more datasets or groups.
single.factor_correlation	Score MOFA-factor enrichment across annotated groups.
single.factor_exact	Add MOFA latent factors from model file into adata.obs.
single.Fate	Adaptive ridge-regression framework for pseudotime-associated gene discovery.
single.find_markers	Find marker genes for each cluster / group in single-cell data.
single.format_liana_results	Format LIANA results into the communication AnnData expected by ov.pl.ccc_*.
single.gene_trends	Fit and visualize smooth feature trends along pseudotime.
single.generate_scRNA_report	Generate a MultiQC-style HTML report for single-cell RNA-seq analysis.
single.geneset_aucell	Calculate the AUC-ell score for a given gene set.
single.get_celltype_marker	Get marker genes for each cluster/cell type.
single.get_cluster_celltype	Resolve one final cell type for each cluster with LLM calls.
single.get_markers	Extract top marker genes from rank_genes_groups results.
single.get_obs_value	Transfer per-cell annotations/statistics to metacells.
single.get_weights	Extract feature loadings for one factor from a MOFA model.
single.GLUE_pair	Pair RNA and ATAC cells using GLUE latent embeddings and neighbor matching.
single.gptcelltype	Annotate cluster cell types with a remote LLM service.
single.gptcelltype_local	Annotate cell types with a local instruction-tuned LLM.
single.lazy	Run a one-click single-cell analysis pipeline with resumable steps.
single.load_human_prior_interaction_network	Load one of the packaged human prior interaction networks.
single.MetaCell	Unified metacell wrapper with dispatchable backends.
single.MetaTiME	MetaTiME wrapper for tumor microenvironment cell-state annotation.
single.Monocle	Monocle2-style single-cell trajectory analysis.
single.mouse_hsc_nestorowa16	Load Nestorowa16 mouse HSC reference data used by CEFCON.
single.pathway_aucell	Calculate the area under the curve (AUC) for a set of pathways in an AnnData object.
single.pathway_aucell_enrichment	Enrich cell annotations with pathway activity scores using the AUC-ell method.
single.pathway_enrichment	Perform pathway enrichment analysis on gene expression data.
single.pathway_enrichment_plot	Visualize the pathway enrichment analysis results as a heatmap.
single.plot_metacells	Plot metacell centroids on a given embedding axis.
single.pyCEFCON	CEFCON workflow wrapper for driver-regulator discovery.
single.pyMOFA	Train MOFA models for latent factor discovery across multiple omics layers.
single.pyMOFAART	Load pretrained MOFA models for downstream factor interpretation.
single.pySCSA	Automated cell-type annotation using SCSA marker-enrichment scoring.
single.pySIMBA	SIMBA wrapper for single-cell batch integration and graph-embedding construction.
single.pyTOSICA	TOSICA wrapper for pathway-informed transformer-based cell-type annotation.
single.run_cellphonedb_v5	Run CellPhoneDB statistical analysis with automatic database download
single.run_liana	Run LIANA ligand-receptor inference on an AnnData object.
single.scanpy_cellanno_from_dict	Add cell type annotation from dict to anndata object.
single.SCENIC
single.TrajInfer	Trajectory inference class for single-cell data analysis.
single.Velo	RNA velocity analysis wrapper for directional cell-state transition inference.

Bulk RNA-seq (bulk)

bulk.batch_correction	Perform batch effect correction using ComBat algorithm.
bulk.Deconvolution	Bulk RNA-seq deconvolution class for inferring cell-type fractions from single-cell references.
bulk.geneset_enrichment	Perform pathway enrichment analysis using Enrichr-compatible gene-set libraries.
bulk.geneset_plot	Plot enrichment results as a bubble plot.
bulk.geneset_plot_multi	Plot multiple enrichment result tables in a unified dot-clustermap panel.
bulk.Matrix_ID_mapping	Map gene IDs in the input data to gene symbols using a reference table.
bulk.pyDEG	Differential-expression analysis helper for bulk RNA-seq count tables.
bulk.pyGSEA	Gene Set Enrichment Analysis (GSEA) wrapper for ranked gene lists.
bulk.pyPPI	Protein-protein interaction (PPI) analysis wrapper based on STRING.
bulk.pyTCGA	TCGA (The Cancer Genome Atlas) data analysis module.
bulk.pyWGCNA	Weighted Gene Co-expression Network Analysis.
bulk.readWGCNA	Load a previously saved WGCNA object from disk.
bulk.string_interaction	Analyze protein-protein interaction network using STRING database.

Metabolomics (metabol)

metabol.aggregate_by_class	Collapse the matrix to class-level totals.
metabol.annotate_lipids	Parse each var_name as a lipid and add lipid_class / total_carbons / total_db columns to adata.var.
metabol.annotate_peaks	Map a list of m/z peaks to candidate KEGG compounds via adduct search.
metabol.anova	Per-metabolite test across 3+ groups.
metabol.asca	ASCA — ANOVA-Simultaneous Component Analysis (Smilde 2005).
metabol.asca_variance_bar	Horizontal bars of per-effect variance-explained fractions.
metabol.biomarker_panel	Nested-CV evaluation of a multi-metabolite biomarker panel.
metabol.blank_filter	Drop features whose sample-mean intensity isn't at least ``ratio``× the blank-mean intensity.
metabol.corr_network	Pairwise metabolite correlation network within a single condition.
metabol.corr_network_plot	Draw an edge DataFrame as a NetworkX spring-layout plot.
metabol.cv_filter	Drop features with coefficient-of-variation above cv_threshold.
metabol.dgca	Differential correlation between two groups.
metabol.dgca_class_bar	Bar chart of DC-class counts (+/+, +/0, +/-, -/+, ...).
metabol.differential	Run a univariate two-group test across all metabolites.
metabol.drift_correct	Correct systematic signal drift using LOESS regression on QC samples.
metabol.fetch_chebi_compounds	Build a compound master table from ChEBI's flat-file TSVs.
metabol.fetch_hmdb_from_name	Resolve a metabolite name → HMDB / KEGG / ChEBI / PubChem CID.
metabol.fetch_kegg_pathways	Fetch the full KEGG compound→pathway map via KEGG REST.
metabol.fetch_lion_associations	Fetch the full LION lipid↔ontology associations.
metabol.impute	Impute missing values (NaN / 0) in adata.X.
metabol.lion_enrichment	LION-style over-representation for lipid classes / properties.
metabol.load_pathways	Return {pathway_name: [kegg_id, ...]} — the pathway database used by msea_ora() / msea_gsea() / mummichog_basic().
metabol.map_ids	Resolve metabolite names to external database IDs.
metabol.meba	Per-feature Hotelling T-squared for two-group time-course comparison.
metabol.mixed_model	Per-feature statsmodels.MixedLM fit.
metabol.msea_gsea	GSEA-style ranked enrichment via gseapy.prerank.
metabol.msea_ora	Over-representation analysis via Fisher's exact test.
metabol.mummichog_basic	Pure-Python mummichog — pathway enrichment from m/z peaks.
metabol.normalize	Normalize each sample (row) of adata.X to correct for dilution.
metabol.opls_da	Orthogonal Projection to Latent Structures — Discriminant Analysis.
metabol.parse_lipid	Parse a LIPID MAPS-shorthand lipid name.
metabol.pathway_bar	Horizontal bar chart of pathway enrichment p-values.
metabol.pathway_dot	Dot plot of pathway enrichment — the de-facto standard figure.
metabol.plsda	Partial Least Squares Discriminant Analysis (wraps sklearn PLS).
metabol.pyMetabo	Lifecycle class for a metabolomics analysis.
metabol.read_lcms	Load an LC-MS peak table with m/z/RT feature IDs into AnnData.
metabol.read_metaboanalyst	Load a MetaboAnalyst-format CSV into AnnData.
metabol.read_wide	Load a generic wide (samples × metabolites) table into AnnData.
metabol.roc_feature	Per-feature AUC for a binary class.
metabol.run_mofa	Train MOFA+ on sample-aligned metabolomics + other-omics views.
metabol.s_plot	OPLS-DA S-plot: p(cov) vs p(corr), i.e. covariance vs correlation between each feature and the predictive component.
metabol.sample_qc	Hotelling T-squared + DModX sample-level outlier detection.
metabol.sample_qc_plot	Scatter of Hotelling T² vs DModX with critical-value lines.
metabol.serrf	SERRF — QC-based Random Forest drift correction (Fan 2019).
metabol.transform	Apply a feature-level transformation to adata.X.
metabol.vip_bar	Horizontal bar chart of top-top_n VIP metabolites.
metabol.volcano	Metabolomics volcano plot — log2FC vs -log10(padj) (or pvalue).

Microbiome (micro)

micro.Alpha	Compute and store per-sample alpha-diversity metrics on adata.obs.
micro.attach_tree	Attach a phylogenetic tree to adata.uns[store_key].
micro.Beta	Compute sample × sample distance matrices.
micro.clr	CLR transform: log(x_i) - mean(log(x)) per sample (post pseudo-count).
micro.collapse_taxa	Collapse ASVs to a taxonomic rank.
micro.combine_studies	Stitch a list of per-study AnnDatas into a single cross-cohort table.
micro.DA	Per-feature differential abundance across sample groups.
micro.fetch_franzosa_ibd_2019	Download + parse the Franzosa et al. 2019 paired IBD dataset.
micro.filter_by_prevalence	Filter rare features by prevalence.
micro.ilr	ILR transform — orthonormal coordinate system after closure removal.
micro.meta_da	Per-study DA + inverse-variance meta-analysis.
micro.MMvec	MMvec (Morton et al. 2019) in ~80 lines of PyTorch.
micro.Ordinate	Reduce a sample × sample distance matrix to 2-D / 3-D coords.
micro.paired_cca	Run sklearn CCA on the paired tables.
micro.paired_spearman	Rank correlation between every (microbe, metabolite) pair.
micro.plot_embedding_biplot	Biplot of microbe + metabolite embeddings in the MMvec latent space.
micro.plot_mmvec_training	Training (and validation) loss curve for a fitted MMvec.
micro.rarefy	Rarefy counts to a common depth.
micro.simulate_paired	Build a paired microbe + metabolite cohort with planted producer pairs.

Spatial transcriptomics (space)

space.bin2cell	Aggregate binned Visium signals into cell-level profiles.
space.Cal_Spatial_Net	Construct spatial neighbor networks for spatial integration.
space.calculate_gene_signature	Build a marker-gene signature table for each cell type in a reference scRNA-seq dataset.
space.CAST	CAST (Cell Annotation for Spatial Transcriptomics) embedding for multiple spatial samples.
space.cellcharter	Run a minimal CellCharter workflow on a spatial AnnData object.
space.CellLoc	SpatRio CellLoc class for probabilistic cell localization.
space.CellMap	SpatRio CellMap class for mapping single cells to spatial coordinates.
space.clusters	Perform clustering analysis on spatial transcriptomics data using multiple methods.
space.create_communication_anndata	Build a CellChat-style communication AnnData from commot outputs.
space.crop_space_visium	Crop Visium spatial data to a specific region of interest.
space.Deconvolution	Spatial deconvolution pipeline that aligns scRNA-seq references with spatial transcriptomics.
space.GASTON	GASTON spatial depth estimation and clustering.
space.map_spatial_auto	Automatically map and align spatial transcriptomics data.
space.map_spatial_manual	Manually adjust spatial transcriptomics data alignment.
space.merge_cluster	Merge clusters based on hierarchical clustering of their representation.
space.moranI	Compute Moran's I spatial autocorrelation for gene expression.
space.nmf_tissue_zones	Discover tissue zones via NMF on a per-spot cell-abundance matrix.
space.pySpaceFlow	SpaceFlow spatial flow analysis class.
space.pySTAGATE	A class representing the PyTorch implementation of STAGATE (Spatial Transcriptomics Analysis using Graph Attention autoEncoder).
space.pySTAligner	STAligner for spatial transcriptomics data integration.
space.read_visium_10x	Read and standardize 10x Visium data with bin2cell-compatible loader.
space.rotate_space_visium	Rotate Visium spatial data image and coordinates by a specified angle.
space.salvage_secondary_labels	Merge primary and secondary segmentation labels.
space.spatial_autocorr	Compute spatial autocorrelation statistics for gene expression.
space.spatial_neighbors	Build a spatial neighborhood graph from coordinates stored in adata.obsm.
space.STT	Spatial Transition Tensor (STT) analysis class.
space.svg	Identify spatially variable genes using multiple methods.
space.sync_visium_hd_seg_geometries	Synchronize adata.uns["spatial"][sample]["geometries"] with current adata.obs_names.
space.Tangram	Tangram spatial deconvolution class for cell type mapping.
space.update_classification_from_database	Update communication interaction annotations from commot database metadata.
space.visium_10x_hd_cellpose_expand	Expand segmentation labels from nuclei to nearby bins.
space.visium_10x_hd_cellpose_gex	Run expression-image segmentation and map labels back to spatial bins.
space.visium_10x_hd_cellpose_he	Convert Visium 10x data to cell-level data.

Bulk-to-Single (bulk2single)

bulk2single.Bulk2Single	VAE-based bulk-to-single framework for reconstructing pseudo single cells from bulk RNA-seq.
bulk2single.bulk2single_plot_cellprop	Plot cell-type proportions in generated single-cell data.
bulk2single.bulk2single_plot_correlation	Plot correlation matrix between reference and generated single-cell data.
bulk2single.BulkTrajBlend	Integrate bulk and single-cell information to infer transitional cell-state trajectories.
bulk2single.Single2Spatial	Deep-learning mapper that projects single-cell profiles onto spatial coordinates.

Plotting (pl)

pl.add_density_contour	Add KDE-based density contours to an existing matplotlib plot.
pl.add_palue	Add p-value annotation with connecting line to a matplotlib plot.
pl.add_pie2spatial	Overlay per-spot pie charts of cell-type composition on a spatial map.
pl.add_streamplot	Overlay velocity streamlines on a low-dimensional embedding.
pl.branch_streamplot	Render a branch-aware pseudotime stream plot.
pl.bardotplot	Create a combined bar-and-dot summary plot by groups.
pl.boxplot	Create a boxplot with jittered points to visualize data distribution across categories.
pl.branch_streamplot	Render a branch-aware pseudotime stream plot.
pl.calculate_gene_density	Calculate weighted kernel density estimates for gene expression on 2D embeddings.
pl.ccc_heatmap	Plot communication matrices as heatmaps, dot plots, or bubble maps.
pl.ccc_network_plot	Plot cell-cell communication networks with multiple graph styles.
pl.ccc_stat_plot	Plot communication summaries, distributions, and pathway statistics.
pl.cell_cor_heatmap	Compute pairwise correlation/similarity between cell groups and plot as heatmap.
pl.CellChatViz	Visualization helper for CellPhoneDB cell-cell communication outputs.
pl.cellproportion	Plot cell proportion of each cell type in each visual cluster.
pl.complexheatmap	Generate a complex annotated heatmap using PyComplexHeatmap.
pl.contour	Overlay a KDE contour for selected clusters on embedding axes.
pl.ConvexHull	Plot the ConvexHull for a cluster in embedding.
pl.create_custom_colormap	Build a transparent-to-opaque LinearSegmentedColormap of a single colour.
pl.dotplot	Make a dot plot of the expression values of var_names.
pl.dynamic_heatmap	Plot dynamic feature trends along pseudotime, optionally by lineage.
pl.dynamic_trends	Plot GAM-fitted pseudotime trends for one or more genes.
pl.embedding	Scatter plot for user specified embedding basis (e.g. umap, pca, etc).
pl.embedding_adjust	Get locations of cluster median and adjust text labels accordingly.
pl.embedding_atlas	Render large-scale embeddings with Datashader.
pl.embedding_celltype	Plot embedding with celltype color by omicverse.
pl.embedding_density	Plot cluster-specific density on an existing embedding.
pl.feature_heatmap	Plot cell-level feature expression ordered by groups or metadata.
pl.ForbiddenCity	Forbidden City traditional-color palette utility.
pl.gen_mpl_labels	Add cluster labels at median positions in embedding plots with automatic text positioning.
pl.geneset_wordcloud	Build cluster-wise gene-set word clouds along pseudotime.
pl.group_heatmap	Plot grouped mean expression as a Marsilea heatmap.
pl.marker_heatmap	Create a dot plot heatmap showing marker gene expression using PyComplexHeatmap.
pl.markers_dotplot	Dot plot of marker genes — clean drop-in for rank_genes_groups_dotplot().
pl.palette	Returns the default OmicVerse color palette.
pl.plot1cell	Circular UMAP with metadata tracks.
pl.plot_cellproportion	Plot stacked bar chart showing cell type proportions across groups.
pl.plot_embedding_celltype	Create combined embedding plot with cell type legend and counts.
pl.plot_flowsig_network	Create a flowsig network visualization showing GEM modules and gene flows.
pl.plot_grouped_fractions	Plot grouped cell-fraction summaries as stacked bars.
pl.plot_pca_variance_ratio	Plot PCA variance ratio to determine optimal number of principal components.
pl.plot_set	Configure plotting settings for OmicVerse.
pl.plot_spatial	Create spatial plot from Visium data with color gradient and interpolation.
pl.plot_text_set	Format text for plotting by adding line breaks.
pl.rank_genes_groups_dotplot	Create a dot plot from rank_genes_groups results.
pl.single_group_boxplot	adata (AnnData object): The data object containing the information for plotting.
pl.tsne	Plot t-SNE embedding.
pl.umap	Plot UMAP embedding.
pl.venn	Create a Venn diagram to visualize set overlaps.
pl.violin	Enhanced violin plot compatible with omicverse's interface.
pl.volcano	Create a volcano plot for differential expression analysis.

Datasets

datasets.bhattacherjee	Processed single-cell data PFC adult mice under cocaine self-administration.
datasets.blobs	Gaussian Blobs dataset.
datasets.bm	The BM dataset used in http://pklab.med.harvard.edu/velocyto/notebooks/R/SCG71.nb.html
datasets.bone_marrow	The bone marrow dataset used in
datasets.burczynski06	Bulk data with conditions ulcerative colitis (UC) and Crohn's disease (CD).
datasets.chromaffin	The chromaffin dataset used in http://pklab.med.harvard.edu/velocyto/notebooks/R/chromaffin2.nb.html
datasets.cite_seq	Placeholder for CITE-seq dataset loader.
datasets.create_mock_dataset	Create a mock single-cell dataset for testing statistical functions.
datasets.decov_bulk_covid_bulk	COVID-19 PBMC bulk data from Decov et al. 2020.
datasets.decov_bulk_covid_single	COVID-19 PBMC single-cell data from Decov et al. 2020.
datasets.dentate_gyrus	The Dentate Gyrus dataset used in velocyto-team/velocyto-notebooks.
datasets.dentate_gyrus_scvelo	The Dentate Gyrus dataset used in theislab/scvelo_notebooks.
datasets.download_data	Download a dataset file to local storage.
datasets.download_data_requests	Download data with custom headers to reduce HTTP 403 failures.
datasets.get_adata	Download example data to local folder.
datasets.gillespie	TODO: add data here
datasets.haber	The Haber dataset used in velocyto-team/velocyto-notebooks
datasets.hematopoiesis	Processed dataset originally from https://pitt.box.com/v/hematopoiesis-processed.
datasets.hematopoiesis_raw	Processed dataset originally from https://pitt.box.com/v/hematopoiesis-processed.
datasets.hg_forebrain_glutamatergic	The hgForebrainGlutamatergic dataset used in velocyto-team/velocyto-notebooks
datasets.hl60	TODO: add data here
datasets.human_tfs	Download human transcription factors.
datasets.krumsiek11	Simulated myeloid progenitors (Krumsiek et al. 2011).
datasets.moignard15	Hematopoiesis in early mouse embryos (Moignard et al. 2015).
datasets.multi_brain_5k	Processed dataset originally from https://pitt.box.com/v/hematopoiesis-processed.
datasets.nascseq	TODO: add data here
datasets.pancreas_cellrank	The pancreas cellrank dataset used in theislab/scvelo_notebooks.
datasets.pancreatic_endocrinogenesis	Pancreatic endocrinogenesis.
datasets.paul15	Development of Myeloid Progenitors (Paul et al. 2015).
datasets.pbmc3k	Load PBMC 3k dataset from URL.
datasets.pbmc8k	PBMC 8k dataset from 10x Genomics.
datasets.sc_ref_Lymph_Node	SC reference data for Lymph Node.
datasets.sceu_seq_organoid	Download organoid dataset from Battich, et al (2020) via a figshare link.
datasets.sceu_seq_rpe1	Download rpe1 dataset from Battich, et al (2020) via a figshare link.
datasets.scifate	TODO: add data here
datasets.scnt_seq_neuron_labeling	The neuron splicing data is from Qiu, et al (2020).
datasets.scnt_seq_neuron_splicing	The neuron splicing data is from Qiu, et al (2020).
datasets.scslamseq	TODO: add data here
datasets.seqfish	SeqFish dataset from 10x Genomics.
datasets.toggleswitch	Simulated toggleswitch data.
datasets.zebrafish	The zebrafish is from Saunders, et al (2019).

External Integrations (external)

external.GraphST

# Author: Yahui Long # File Name: __init__.py # Description:

Utilities (utils)

utils.biocontext.call_tool	Call any BioContext MCP tool by name.
utils.biocontext.get_ensembl_id	Convert gene symbol to Ensembl ID.
utils.biocontext.get_fulltext	Get full text from Europe PMC.
utils.biocontext.get_uniprot_id	Get UniProt accession ID from protein symbol.
utils.biocontext.list_tools	List all available BioContext tools.
utils.biocontext.query_alphafold	Query AlphaFold DB for predicted protein structure.
utils.biocontext.query_cell_ontology	Query Cell Ontology for cell type terms.
utils.biocontext.query_chebi	Query ChEBI for chemical entities.
utils.biocontext.query_efo	Query EFO for disease ontology terms.
utils.biocontext.query_go	Query Gene Ontology terms for a gene.
utils.biocontext.query_hpa	Query Human Protein Atlas for tissue-level expression.
utils.biocontext.query_interpro	Query InterPro for protein domains.
utils.biocontext.query_opentargets	Query Open Targets via GraphQL.
utils.biocontext.query_panglaodb	Query PanglaoDB for cell type marker genes.
utils.biocontext.query_reactome	Query Reactome pathway database.
utils.biocontext.query_string	Query STRING for protein-protein interactions.
utils.biocontext.query_uniprot	Query UniProt protein information.
utils.biocontext.search_clinical_trials	Search ClinicalTrials.gov by condition.
utils.biocontext.search_drugs	Search FDA drug database.
utils.biocontext.search_interpro	Search InterPro entries.
utils.biocontext.search_literature	Search Europe PMC for biomedical literature.
utils.biocontext.search_preprints	Search bioRxiv or medRxiv preprints.
utils.biocontext.search_pride	Search PRIDE proteomics repository.
utils.cal_paga	Compute a PAGA graph with optional velocity/time priors.
utils.cluster	Run a selected clustering backend on single-cell data.
utils.convert2gene_id	Convert official gene symbols to Ensembl gene IDs using pyensembl.
utils.convert2gene_symbol	Convert Ensembl gene IDs to official gene symbols using pyensembl.
utils.convert2symbol	Convert Ensembl IDs in adata.var_names to official gene symbols.
utils.convert_adata_for_rust	Rewrite an AnnData object as an .h5ad file that ov.read(..., backend='rust') can open.
utils.convert_to_pandas	Convert Rust-backed dataframe-like objects to pandas.DataFrame.
utils.download_CaDRReS_model	Download pretrained CaDRReS model parameter/output files.
utils.download_GDSC_data	Download GDSC expression and drug mask tables.
utils.download_geneid_annotation_pair	Download gene ID annotation mapping files for various organisms.
utils.download_pathway_database	Download pathway and gene set databases for enrichment analysis.
utils.download_tosica_gmt	Download curated GMT files used by TOSICA workflows.
utils.geneset_prepare	Load and prepare gene sets from GMT/TXT files for enrichment analysis.
utils.get_gene_annotation	Annotate adata.var by merging with gene-level GTF attributes.
utils.gtf_to_pair_tsv	Convert GTF file to gene ID mapping pairs TSV format.
utils.LDA_topic	Latent Dirichlet Allocation (LDA) topic modeling for single-cell data using MIRA.
utils.load_metabolights	Load a Metabolights study into a samples × metabolites AnnData.
utils.mde	Util to run pymde.preserve_neighbors() for visualization of scvi-tools embeddings.
utils.plot_paga	Plot PAGA graph and optional embedding-level annotations.
utils.refine_label	Refine labels with neighborhood majority voting.
utils.retrieve_layers	Retrieve previously stored X matrix from adata.uns and restore to adata.X.
utils.roe	Compute the Ro/e (observed/expected) cell-type enrichment matrix.
utils.store_layers	Store the X matrix of AnnData in adata.uns for later retrieval.
utils.symbol2id	Convert gene symbols in adata.var_names to Ensembl gene IDs.
utils.weighted_knn_trainer	Trains a weighted KNN classifier on train_adata.
utils.weighted_knn_transfer	Annotates query_adata cells with an input trained weighted KNN classifier.
utils.wrap_dataframe	Wrap a Rust-backed dataframe-like object with a pandas-style interface.