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Composition-Based Methods

Niche definition: cell-type proportions in a spatial neighborhood.

These methods answer the question: what cell types co-localize in this tissue? They require pre-annotated cell types and spatial coordinates, then cluster cells based on the composition of their local neighborhoods.

Key Methods

CellCharter

  • Paper: Nature Genetics, 2024
  • Code: github.com/CSOgroup/cellcharter
  • Niche definition: GNN-learned embedding of cell-type composition at multiple spatial resolutions.
  • Key innovation: Handles multiple spatial scales simultaneously and works across both imaging-based and sequencing-based spatial platforms.
  • Strengths: Multi-resolution analysis, cross-platform compatibility, principled clustering via Gaussian mixture models.
  • Limitations: Requires cell-type annotations as input — niche quality is bounded by annotation quality.

UTAG

  • Paper: Nature Methods, 2022
  • Code: github.com/ElementoLab/utag
  • Niche definition: Message-passing on cell graphs combines each cell's expression with its spatial neighbors' expression.
  • Key innovation: Unsupervised — does not require cell-type annotations, though it uses expression rather than pure composition.
  • Note: Sits between composition-based and expression-based; listed here because it is commonly used for neighborhood detection.

CytoCommunity

  • Paper: Nature Methods, 2024
  • Code: github.com/huBioinfo/CytoCommunity
  • Niche definition: Supervised graph partitioning that uses condition labels (e.g., responder vs non-responder) to find disease-relevant cellular neighborhoods.
  • Key innovation: The only niche method that directly incorporates clinical outcome labels into niche discovery.
  • Strengths: Finds niches that differ between conditions, not just niches that exist.
  • Limitations: Requires condition labels — cannot discover niches in an unsupervised setting.

SpatialLDA

  • Paper: Genome Biology, 2022
  • Code: github.com/calico/spatial_lda
  • Niche definition: Topic modeling treats spatial regions as documents and cell types as words; niches are latent topics.
  • Key innovation: Probabilistic framework that allows cells to belong to multiple niche topics with different weights.
  • Strengths: Interpretable topic structure, handles mixed niches naturally.
  • Limitations: Sensitive to hyperparameter choices (number of topics, neighborhood size).

SOTIP

  • Paper: Nature Communications, 2023
  • Code: github.com/TencentAILabHealthcare/SOTIP
  • Niche definition: Optimal transport distances between spatial neighborhoods enable cross-sample niche comparison.
  • Key innovation: Designed for multi-sample, multi-condition analysis — compares niches across patients.
  • Strengths: Principled statistical framework for inter-sample niche comparison.

CNTools

When to Use Composition-Based Methods

Best for:

  • Tissues with well-characterized cell types and reliable annotations.
  • Questions about which cell types co-localize and how those patterns change across conditions.
  • Studies where the biological question is about cellular communities (e.g., tertiary lymphoid structures, tumor-immune interfaces).

Not ideal for:

  • Datasets without cell-type annotations (use expression-based methods instead).
  • Questions about what cells are doing rather than who is nearby (use communication-based methods).
  • Detecting sub-states within a cell type that depend on spatial context (use covariance or expression methods).