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https://github.com/gpenglab/STcomm


https://github.com/gpenglab/STcomm

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README 

        
---

output: github_document

---



```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

```



# STcomm



Welcome to STcomm, an R package to illustrate the spatially resolved cell 

interactions by combined the spatial cellular colocalization with their enriched ligand-receptor co-expression patterns inferred from both spatial and single-cell transcriptomic data.



## Installation



You can install the development version of STcomm like so:



``` r

# FILL THIS IN! HOW CAN PEOPLE INSTALL YOUR DEV PACKAGE?

```



## Quick Guide to Getting Started with stComm



Firstly, `rctd4weights` function will help you tidy the confident weights for 

every pixel after RCTD analysis.



```r

library(STcomm)

rctd.multi <- rctd4weight(rctd_obj, rctd_mode = 'multi', conf = TRUE)

```



Secondly, `cellColocation` function will help you quantify the colocalization 

of cell type pairs within spots by calculating Pearson correlation coefficient 

or Jaccard similarity coefficient based on cell type composition predicted by 

RCTD.



```r

# By calculating Pearson correlation coefficient based on the RCTD object  

colocal_ctps1 <- cellColocation(rctd_obj, rctd_mode = 'multi', 

                                method = 'pcc', pcc = 0.06, 

                                pval = 0.05, padj = 0.05)



# By calculating Pearson correlation coefficient based on the data.frame of RCTD 

confident weights (with conf = TRUE when RCTD) for each pixel

colocal_ctps2 <- cellColocation(rctd_df, method = 'pcc', 

                               pcc = 0.06, pval = 0.05, padj = 0.05)



# By calculating Jaccard similarity coefficient based on the RCTD object

colocal_ctps3 <- cellColocation(rctd_obj, rctd_mode = 'multi', 

                                method = 'jac', jac = 0.05)



# By calculating Jaccard similarity coefficient based on the data.frame of RCTD 

confident weights (with conf = TRUE when RCTD) for each pixel

colocal_ctps4 <- cellColocation(rctd_df, method = 'jac', jac = 0.05)

```



Then, you can identify significant co-occurrence cell type groups belonging to 

the same spot from the cell type colocalization network. 



Thirdly, based on the spatial data, you can obtain significantly co-expressed 

Ligand-Receptor (LR) pairs for spatially co-localized cell types by performing 

Fisher's exact test on binarized co-localized cell type pairs and co-expressed 

LR pairs at spot level. Next, you calculate significant communication between 

LR pairs in co-localized cell type pairs based on the refrence single cell 

transcriptomic data. Finally, to get high confidence and spatial aware cell-cell communication, you can keep only spatially relevant communication information 

based on the above Fisher exact significancy.`st_comm` function can help you 

characterize confident spatially resolved cell-cell interaction with the tissue 

organization.



```r

# load the CellChat object for the refrence single cell transcriptomic data

cellchat <- readRDS(cellchat)

st_net <- st_comm(st_obj, weights.df = rctd_multi, ctpairs = colocal_ctps1, cellchat = cellchat)



# or you would like to prepare a data.frame tidyed frome the CellChat object

net.df <- subsetCommunication(cellchat)

net.df$ct_pairs <- paste0(net.df$source, "_", net.df$target)

st_net <- st_comm(st_obj, weights.df = rctd_multi, ctpairs = colocal_ctps1, cellchat = net.df)

```