Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

https://github.com/ZJUFanLab/SpaTalk

Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data
https://github.com/ZJUFanLab/SpaTalk

cell-cell-communication cell-cell-interaction graph-network knowledge-graph ligand-receptor-interaction single-cell spatial-data-analysis spatial-transcriptomics spatially-resolved-transcriptomics

Last synced: 4 months ago
JSON representation

Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data

Host: GitHub
URL: https://github.com/ZJUFanLab/SpaTalk
Owner: ZJUFanLab
License: gpl-3.0
Created: 2022-03-29T11:07:05.000Z (about 2 years ago)
Default Branch: main
Last Pushed: 2023-06-30T11:21:45.000Z (12 months ago)
Last Synced: 2024-01-14T18:15:57.752Z (5 months ago)
Topics: cell-cell-communication, cell-cell-interaction, graph-network, knowledge-graph, ligand-receptor-interaction, single-cell, spatial-data-analysis, spatial-transcriptomics, spatially-resolved-transcriptomics
Language: R
Homepage: https://github.com/multitalk/awesome-cell-cell-communication
Size: 9.71 MB
Stars: 43
Watchers: 2
Forks: 12
Open Issues: 3
Metadata Files:
- Readme: README.md
- License: LICENSE.md

Lists

awesome-single-cell - SpaTalk - [R] - [SpaTalk](https://www.nature.com/articles/s41467-022-32111-8) is a cell-cell communication inference method for either single-cell or spot-based spatially resolved transcriptomic data, e.g., STARmap, MERFISH, seqFISH, Slide-seq, 10X Visium. (Software packages / Spatial transcriptomics)
awesome-cell-cell-communication - SpaTalk - [R]- SpaTalk is a spatially resolved cell-cell communication inference method relying on the graph network and knowledge graph to model ligand-receptor-target signaling network for either single-cell or spot-based spatially resolved transcriptomic data, e.g., STARmap, MERFISH, seqFISH, Slide-seq, 10X Visium. (Uncategorized / Uncategorized)

README

        # SpaTalk

[![R >4.0](https://img.shields.io/badge/R-%3E%3D4.0-brightgreen)](https://www.r-project.org/) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.6809147.svg)](https://doi.org/10.5281/zenodo.6809147) [![jupyter](https://img.shields.io/badge/Jupyter--notebook-SpaTalk--tutorial-yellow?logo=jupyter)](https://github.com/multitalk/awesome-cell-cell-communication/blob/main/method/SpaTalk.ipynb) [![DOI](https://img.shields.io/badge/DOI-10.1038%2Fs41467--022--32111--8-yellowgreen)](https://www.nature.com/articles/s41467-022-32111-8)

### A cell-cell communication inference approach for spatially resolved transcriptomic data



[Spatially resolved transcriptomics (ST)](https://pubmed.ncbi.nlm.nih.gov/32505359/) provides the informative details of genes and retained the crucial spatial information, which have enabled the uncovering of spatial architecture in intact organs, shedding light on the spatially resolved [cell-cell communications](https://pubmed.ncbi.nlm.nih.gov/32435978/) mediating tissue homeostasis, development and disease. However, inference of cell-cell communications for ST data remains a great challenge. Here, we present SpaTalk, a spatially resolved cell-cell communication inference method relying on the [graph network](https://pubmed.ncbi.nlm.nih.gov/34500471/) and [knowledge graph](https://www.sciencedirect.com/science/article/pii/S1570826820300585) to model ligand-receptor-target signaling network between the spatially proximal cells, which were decomposed from the ST data through the non-negative linear model and spatial mapping between single-cell RNA-seq and ST data. SpaTalk is a reliable method that can help scientists uncover the spatially resolved cell-cell communications for either single-cell or spot-based ST data universally, providing new insights into the understanding of spatial cellular dynamics in tissues.

# Install

- install dependent packages `devtools` and [`NNLM`](https://github.com/linxihui/NNLM)

```

> install.packages(pkgs = 'devtools')

> devtools::install_github('linxihui/NNLM')

```

- then install SpaTalk

```

> devtools::install_github('ZJUFanLab/SpaTalk')

# or download the repository as ZIP

> devtools::install_local("/path/to/SpaTalk-main.zip")

```

# Usage

SpaTalk method consists of two components, wherein the first is to dissect the cell-type composition of ST data and the second is to infer the spatially resolved cell-cell communications over the decomposed single-cell ST data. Classification and description of SpaTalk functions are shown in the __[wiki page](https://github.com/ZJUFanLab/SpaTalk/wiki)__ and __[tutorial](https://raw.githack.com/multitalk/awesome-cell-cell-communication/main/method/tutorial.html)__

- ### Cell-type decomposition to reconstruct single-cell ST atlas with known cell types

```

# st_data: A matrix containing counts of st data

# st_meta: A data.frame containing x and y

# sc_data: A matrix containing counts of scRNA-seq data as the reference

# sc_celltype:  A character containing the cell types for scRNA-seq data

> obj <- createSpaTalk(st_data, st_meta, species)

> 

> obj

An object of class SpaTalk 

996 genes across 509 spots (0 lrpair)

> 

> obj <- dec_celltype(obj, sc_data, sc_celltype)

```

- ### Inference of cell-cell communication and ligand-receptor-target network in space

```

# obj: SpaTalk object containg ST and scRNA-seq data

# lrpairs: A data.frame of the system data containing ligand-receptor pairs

# pathways: A data.frame of the system data containing gene-gene interactions and pathways

# celltype_sender

# celltype_receiver

> obj <- find_lr_path(object = obj, lrpairs = lrpairs, pathways = pathways)

> obj <- dec_cci(obj, celltype_sender, celltype_receiver)

```

OR

```

> obj <- dec_cci_all(object)

```

# Note

[![CellTalkDB v1.0](https://img.shields.io/badge/CellTalkDB-v1.0-blueviolet)](http://tcm.zju.edu.cn/celltalkdb/) [![KEGG pathway](https://img.shields.io/badge/KEGG-pathway-ff69b4)](https://www.kegg.jp/kegg/pathway.html) [![Reactome pathway](https://img.shields.io/badge/Reactome-pathway-brightgreen)](https://reactome.org/) [![AnimalTFDB v3.0](https://img.shields.io/badge/AnimalTFDB-v3.0-yellowgreen)](http://bioinfo.life.hust.edu.cn/AnimalTFDB/#!/)

SpaTalk uses the ligand-receptor interactions (LRIs) from [`CellTalkDB`](http://tcm.zju.edu.cn/celltalkdb/), pathways from [`KEGG`](https://www.kegg.jp/kegg/pathway.html) and [`Reactome`](https://reactome.org/), and transcrptional factors (TFs) from [`AnimalTFDB`](http://bioinfo.life.hust.edu.cn/AnimalTFDB/#!/) by default. In the current version:

- __SpaTalk can be applied to either [single-cell (vignette)](https://raw.githack.com/multitalk/awesome-cell-cell-communication/main/method/sc_tutorial.html) or [spot-based (vignette)](https://raw.githack.com/multitalk/awesome-cell-cell-communication/main/method/spot_tutorial.html) ST data__

- __SpaTalk allows to use custom [LRIs(wiki)](https://github.com/ZJUFanLab/SpaTalk/wiki/Use-customed-lrpairs), [pathways, and TFs database (wiki)](https://github.com/ZJUFanLab/SpaTalk/wiki/Use-customed-pathways)__

- __SpaTalk allows to use the parallel processing for `dec_celltype()`, `dec_cci()`, and `dec_cci_all()`__

- __SpaTalk allows to [use other deconvolution methods](https://github.com/ZJUFanLab/SpaTalk/wiki/Use-other-deconvolution-methods) followed by the inference of cell-cell communications__

  - RCTD, Seurat, SPOTlight, deconvSeq, stereoscope, cell2location, or other methods

- __SpaTalk allows to [directly infer cell-cell communications skiping deconvolution](https://github.com/ZJUFanLab/SpaTalk/wiki/Directly-infer-cell-cell-communication-skiping-deconvolution)__

- __SpaTalk can visualize [cell-type compositions (wiki)](https://github.com/ZJUFanLab/SpaTalk/wiki#visulization-cell-types) and [cell-cell communications (wiki)](https://github.com/ZJUFanLab/SpaTalk/wiki#visulization-cell-cell-communications)__

- LRIs and pathways can be download at[`data/`](https://github.com/ZJUFanLab/SpaTalk/tree/main/data) 

- Demo data can be download at[`inst/extdata/`](https://github.com/ZJUFanLab/SpaTalk/tree/main/inst/extdata)

__Please refer to the [tutorial vignette](https://raw.githack.com/multitalk/awesome-cell-cell-communication/main/method/tutorial.html) with demo data processing steps. Detailed functions see the [document](https://raw.githack.com/ZJUFanLab/SpaTalk/main/vignettes/SpaTalk.pdf)__

### News

- Fix the bug in `dec_celltype()` when using other deconvolution methods, [see issue 11](https://github.com/ZJUFanLab/SpaTalk/issues/11)

- Fix the bug in `dec_celltype()` when using `dec_result`, [see issue 30](https://github.com/ZJUFanLab/SpaTalk/issues/30)

- Fix the bug in `dec_cci()` and `dec_cci_all()`, [see issue 10](https://github.com/ZJUFanLab/SpaTalk/issues/10)

- Fix the bug in performing parallel functions, e.g., `invalid character indexing` see [issue 30](https://github.com/ZJUFanLab/SpaTalk/issues/30) and [issue 10](https://github.com/ZJUFanLab/SpaTalk/issues/10)

- __Allow to retain genes consistent with reference `sc_data` for reconstructed single-cell ST data [(wiki)](https://github.com/ZJUFanLab/SpaTalk/wiki/Retain-all-genes-in-the-reference)__

# About

SpaTalk was developed by Xin Shao. Should you have any questions, please contact Xin Shao at [email protected]

Please cite us as "Shao, X., et al. Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk. Nat Commun 13, 4429 (2022). https://doi.org/10.1038/s41467-022-32111-8"