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Concepts

Understanding spatial transcriptomics analysis.

What is Spatial Transcriptomics?

Spatial transcriptomics measures gene expression while preserving the physical location of cells in tissue. Unlike standard single-cell RNA sequencing, it tells you where cells are, not just what they express.

Key insight: Location matters. A tumor cell behaves differently depending on whether it's surrounded by immune cells or fibroblasts.

Core Analysis Types

Spatial Domains

What it does: Groups tissue regions based on similar gene expression patterns.

When to use: First step after preprocessing. Identifies tissue architecture like tumor regions, immune infiltrates, or tissue layers.

How to choose:

  • If your data includes informative histology images, prefer a histology-aware method.
  • If your data is high-resolution single-cell spatial data, prefer graph or deep-learning domain methods.
  • If you want a quick baseline, use clustering-first exploration.

See Methods Reference for the full supported domain methods and defaults.

Cell Type Annotation vs Deconvolution

These two concepts are often confused. Here's the difference:

Annotation Deconvolution
Output "This spot is T cells" "This spot is 60% T cells, 30% macrophages, 10% fibroblasts"
Best for Single-cell resolution data Spot-based data (Visium)
Assumption One cell type per spot Multiple cell types per spot

Rule of thumb:

  • Xenium, MERFISH, CosMx: Use annotation (single-cell resolution)
  • Visium, Slide-seq: Use deconvolution (multiple cells per spot)

Cell Communication

What it does: Identifies which cell types are "talking" to each other through ligand-receptor interactions.

Key concept: Cell A expresses a ligand (signal molecule), Cell B expresses the receptor. If they're spatially close, they may be communicating.

Species matters: Use the correct database:

  • Human: liana_resource="consensus"
  • Mouse: liana_resource="mouseconsensus"

RNA Velocity

What it does: Predicts future cell states by comparing spliced vs unspliced RNA.

Key insight: If a gene has more unspliced RNA, it's being upregulated. If more spliced, it's being downregulated. This tells you the "direction" cells are moving.

Requirement: Your data must have spliced and unspliced layers (from velocyto, kallisto, or STARsolo).

Choosing Methods

Deconvolution

Use deconvolution when a spot contains multiple cell types and you want proportions rather than a single label.

How to choose:

  • start with a fast method for exploration
  • move to a slower, stronger method for final figures
  • prefer methods with explicit spatial modeling if tissue structure matters to the question

See Methods Reference for the full deconvolution method list, defaults, and requirements.

Annotation

Use annotation when your platform already has single-cell resolution or when you want one dominant label per cell/spot.

How to choose:

  • use transfer methods when you have a strong matching reference
  • use marker-based methods when marker genes are well established
  • use automated methods for quick initial labeling, then validate biologically

See Methods Reference for supported annotation methods and exact requirements.

Spatial Statistics

Spatial statistics answer different spatial questions.

How to choose:

  • use global autocorrelation when you want one summary statistic for a gene
  • use local hotspot methods when you want to locate spatially enriched regions
  • use neighborhood or co-occurrence analyses when your question is about cell-type organization rather than gene-level spatial patterning

See Methods Reference for the full analysis-type matrix and required inputs.

Understanding Results

Interpreting Deconvolution

Good deconvolution results show:

  • Cell type proportions sum to ~1.0 per spot
  • Known tissue structure is visible (e.g., epithelium vs stroma)
  • Proportions correlate with histology

Warning signs:

  • One cell type dominates everywhere (>80%)
  • Proportions don't match expected tissue composition
  • Results change dramatically with different methods

Interpreting Spatial Statistics

Moran's I interpretation:

  • I > 0: Clustered (similar values near each other)
  • I ~ 0: Random
  • I < 0: Dispersed (dissimilar values near each other)

p-value: Tests if pattern is significant vs random.

Common Pitfalls

1. Skipping Preprocessing

Most analyses fail because preprocessing wasn't run. Always preprocess first:

"Preprocess the data"

2. Wrong Species Parameter

Cell communication analysis depends on species-specific resources. Use the resource that matches the organism, especially for mouse data.

See Methods Reference for the canonical species and resource settings.

3. Expecting Single-Cell Resolution from Visium

Visium spots contain 1-10 cells. Use deconvolution to estimate proportions, not annotation to assign types.

4. Using GPU Methods Without GPU

Methods like Cell2location are 10-100x slower without GPU. Either:

  • Set use_gpu=False explicitly
  • Use CPU-friendly alternatives (FlashDeconv, RCTD)

Workflow Patterns

Standard Discovery Workflow

Load → Preprocess → Domains → Markers → Visualize

Best for: Initial exploration of new dataset.

Reference-Based Workflow

Load spatial → Load reference → Preprocess both → Deconvolve → Communicate

Best for: When you have matching single-cell reference data.

Publication Workflow

Load → Preprocess → Domains → Deconvolve → Statistics → Communication → Velocity

Best for: Comprehensive analysis for publication.