Getting Started with Spatial Transcriptomics Analysis

Spatial transcriptomics adds a crucial dimension to single-cell analysis: location. Here’s how to get started.

What is Spatial Transcriptomics?

Unlike scRNA-seq, spatial methods preserve tissue architecture. You can see:

→ Where cells are located

→ How they interact with neighbors

→ Tissue-level organization

The Basic Workflow

1. Load Your Data

import scanpy as sc
import squidpy as sq

adata = sq.read.visium('path/to/data/')

2. Quality Control

sc.pp.calculate_qc_metrics(adata, inplace=True)
sc.pl.spatial(adata, color='total_counts')

3. Preprocessing

Same as scRNA-seq:

sc.pp.normalize_total(adata)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata)

4. Spatial Analysis

This is where it gets interesting:

# Neighborhood enrichment
sq.gr.spatial_neighbors(adata)
sq.gr.nhood_enrichment(adata, cluster_key='cluster')

# Spatial autocorrelation
sq.gr.spatial_autocorr(adata, genes=['gene1', 'gene2'])

Key Concepts

→ Neighborhood enrichment — Which cell types cluster together?

→ Spatial autocorrelation — Is gene expression spatially organized?

→ Ligand-receptor analysis — Cell-cell communication in space

Spatial transcriptomics is the future. Have you tried it? Share your experience in the comments!