SpaTalk

📚 Documentation

Full documentation: https://zaoqu-liu.github.io/SpaTalk/

Overview

SpaTalk is a computational framework for inferring spatially resolved cell-cell communications (CCIs) from spatial transcriptomics (ST) data. The method integrates graph network modeling and knowledge graph approaches to reconstruct ligand-receptor-target signaling networks between spatially proximal cells.

Key Features

• Cell-type Deconvolution: Non-negative linear model (NNLM) for decomposing spot-based ST data into single-cell resolution
• Spatial Mapping: Integration of scRNA-seq reference data with spatial coordinates
• Graph-based CCI Inference: Knowledge graph modeling of ligand-receptor-downstream pathway interactions
• Statistical Validation: Permutation-based significance testing for identified communications

Supported Platforms

Platform	Resolution	Examples
Single-cell ST	Cellular	STARmap, MERFISH, seqFISH+
Spot-based ST	Multi-cellular	10x Visium, Slide-seq, ST

Installation

From R-universe (Recommended)

install.packages("SpaTalk", repos = "https://zaoqu-liu.r-universe.dev")

From GitHub

# Install dependencies
install.packages("devtools")
devtools::install_github("linxihui/NNLM")

# Install SpaTalk
devtools::install_github("Zaoqu-Liu/SpaTalk")

System Requirements

• R ≥ 4.0.0
• Platform: Windows, macOS, Linux
• Dependencies: Seurat (≥3.0.0), NNLM, Matrix, Rcpp

Quick Start

1. Create SpaTalk Object

library(SpaTalk)

# For spot-based ST data
obj <- createSpaTalk(
  st_data = st_counts,      # Gene × Spot count matrix
  st_meta = st_coordinates, # Data frame with 'spot', 'x', 'y'
  species = "Human",        # "Human" or "Mouse"
  if_st_is_sc = FALSE,
  spot_max_cell = 10        # Expected cells per spot
)

2. Cell-type Deconvolution

# Using built-in NNLM deconvolution
obj <- dec_celltype(
  obj, 
  sc_data = sc_counts,       # scRNA-seq reference
  sc_celltype = cell_labels  # Cell type annotations
)

3. Infer Cell-Cell Communications

# Load curated databases
data(lrpairs)   # Ligand-receptor pairs from CellTalkDB
data(pathways)  # KEGG/Reactome pathways + AnimalTFDB

# Filter LR pairs with downstream targets
obj <- find_lr_path(obj, lrpairs, pathways)

# Infer CCIs between cell types
obj <- dec_cci(obj, 
  celltype_sender = "Macrophage",
  celltype_receiver = "Fibroblast"
)

# Or infer all pairwise CCIs
obj <- dec_cci_all(obj)

4. Access Results

# Significant LR pairs with scores
head(obj@lrpair)

# Downstream TF activity
head(obj@tf)

Integrated Databases

SpaTalk incorporates curated biological knowledge from:

Database	Content	Species
CellTalkDB	3,398 human / 2,033 mouse LR pairs	Human, Mouse
KEGG	Signaling pathways	Human, Mouse
Reactome	Pathway interactions	Human, Mouse
AnimalTFDB	Transcription factors	Human, Mouse

Citation

If you use SpaTalk in your research, please cite:

Shao, X., Li, C., Yang, H., Lu, X., Liao, J., Qian, J., Wang, K., Cheng, J., Yang, P., Chen, H., Xu, X., & Fan, X. (2022). Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk. Nature Communications, 13, 4429. https://doi.org/10.1038/s41467-022-32111-8

@article{shao2022spatalk,
  title={Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk},
  author={Shao, Xin and Li, Chengyu and Yang, Haihong and Lu, Xiaoyan and Liao, Jie and Qian, Jingyang and Wang, Kai and Cheng, Junyun and Yang, Penghui and Chen, Huajun and Xu, Xiao and Fan, Xiaohui},
  journal={Nature Communications},
  volume={13},
  pages={4429},
  year={2022},
  doi={10.1038/s41467-022-32111-8}
}

License

This project is licensed under the GPL-3.0 License.

Maintainer

Zaoqu Liu (GitHub: @Zaoqu-Liu)

Original development by ZJUFanLab

---