omicverse.space.Tangram

omicverse.space.Tangram#

class omicverse.space.Tangram(adata_sc, adata_sp, clusters='', marker_size=100, gene_to_lowercase=False)[source]#

Tangram spatial deconvolution class for cell type mapping.

Tangram is a method for integrating single-cell RNA sequencing (scRNA-seq) data with spatial transcriptomics data. It enables: 1. Mapping cell types from scRNA-seq to spatial locations 2. Deconvolving cell type proportions in spatial spots 3. Imputing gene expression in spatial data 4. Analyzing spatial organization of cell types

The method works by: 1. Identifying marker genes for each cell type 2. Training a mapping model using these markers 3. Projecting cell type annotations to spatial coordinates 4. Optionally imputing full gene expression profiles

Parameters:

  • adata_sc (AnnData) – Single-cell reference AnnData with cell-type labels.

  • adata_sp (AnnData) – Spatial AnnData to receive projected cell-type information.

  • clusters (str, default='') – Cell-type column name in adata_sc.obs.

  • marker_size (int, default=100) – Number of marker genes selected per cell type for mapping.

  • gene_to_lowercase (bool, default=False) – Whether to lowercase gene names before matching genes across datasets.

  • Attributes –

    adata_sc: AnnData

    Single-cell RNA-seq data with: - Gene expression matrix in X - Cell type annotations in obs[clusters]

    adata_sp: AnnData

    Spatial transcriptomics data with: - Gene expression matrix in X - Spatial coordinates in obsm[‘spatial’]

    clusters: str

    Column name in adata_sc.obs containing cell type labels

    markers: list

    Selected marker genes used for mapping

    ad_map: AnnData

    Mapping results after training

  • Examples – 

    >>> import scanpy as sc
>>> import omicverse as ov
>>> # Load data
>>> adata_sc = sc.read_h5ad("sc_data.h5ad")
>>> adata_sp = sc.read_visium("spatial_data")
>>> # Initialize Tangram
>>> tangram = ov.space.Tangram(
...     adata_sc=adata_sc,
...     adata_sp=adata_sp,
...     clusters='cell_type'
... )
>>> # Train model
>>> tangram.train(mode='clusters', num_epochs=500)
>>> # Project cell types
>>> adata_spatial = tangram.cell2location()

__init__(adata_sc, adata_sp, clusters='', marker_size=100, gene_to_lowercase=False)[source]#

Initialize Tangram spatial deconvolution object.

This method sets up the Tangram analysis by: 1. Checking package installation 2. Processing input data 3. Identifying marker genes 4. Preparing data for mapping

Parameters:

  • adata_sc (AnnData) – Single-cell reference AnnData used as source profiles.

  • adata_sp (AnnData) – Spatial AnnData used as target tissue map.

  • clusters (str, default='') – Cell-type annotation column in adata_sc.obs.

  • marker_size (int, default=100) – Number of top-ranked marker genes selected per cell type.

  • gene_to_lowercase (bool, default=False) – Whether to lowercase gene names before intersection.

  • Notes –

    • Automatically filters genes present in at least one cell

    • Identifies marker genes using scanpy’s rank_genes_groups

    • Prepares data structures for Tangram mapping

    • Adds reference annotation to both AnnData objects

Methods

__init__(adata_sc, adata_sp[, clusters, ...])

Initialize Tangram spatial deconvolution object.

cell2location([annotation_list])

Project cell type annotations to spatial coordinates.

check_tangram()

Check if Tangram package is installed.

impute([ad_map, ad_sc])

Impute gene expression in spatial data using trained model.

train([mode, num_epochs, device])

Train the Tangram spatial mapping model.
Contents