Update README.md

README.md

@@ -2,7 +2,7 @@
 ### A cell-cell communication inference approach for spatially resolved transcriptomic data
 <img src='https://github.com/ZJUFanLab/SpaTalk/blob/main/img/SpaTalk.svg'>
 
-[Spatially resolved transcriptomics (ST)]() 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]() 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]() and [knowledge graph]() 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.
+[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.
 
 # <a name='devtools'>Install</a>
 [![R >4.0](https://img.shields.io/badge/R-%3E%3D4.0-brightgreen)](https://www.r-project.org/) <a href='#devtools'>![installed with devtools](https://img.shields.io/badge/installed%20with-devtools-blue)</a> 
@@ -19,7 +19,7 @@ or
 install.packages(pkgs = 'SpaTalk-1.0.tar.gz',repos = NULL, type = "source")
 ```
 # 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]()
+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)__
 - ### Cell-type decomposition to reconstruct single-cell ST atlas with known cell types
 ```
 # object: SpaTalk object containg ST data
@@ -43,8 +43,8 @@ dec_cci(object, celltype_sender, celltype_receiver)
 
 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)]() or [spot-based (vignette)]() ST data__
-- __SpaTalk allows to use custom [LRIs](), [pathways, and TFs database (vignette)]()__
+- __SpaTalk can be applied to either [single-cell (vignette)](https://raw.githack.com/ZJUFanLab/SpaTalk/main/vignettes/sc_tutorial.html) or [spot-based (vignette)](https://raw.githack.com/ZJUFanLab/SpaTalk/main/vignettes/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 can visualize [cell-type compositions (wiki)](https://github.com/ZJUFanLab/SpaTalk/wiki/SpaTalk-functions#visulization-cell-types) and [cell-cell communications (wiki)](https://github.com/ZJUFanLab/SpaTalk/wiki/SpaTalk-functions#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)