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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

STcomm R package can be easily installed from Github using devtools:

devtools::install_github("Vanjia-lee/STcomm")

Quick Guide to Getting Started with stComm

Firstly, rctd4weights function will help you tidy the confident weights for every pixel after RCTD analysis.

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.

# By calculating Pearson correlation coefficient based on the RCTD object colocal_ctps1<- cellColocation(rctd_obj, rctd_mode='multi', conf=TRUE,
method='pcc', pcc=0.06, pval=0.05, padj=0.05)
# By calculating Pearson correlation coefficient based on the data.frame of RCTD weights for each pixelcolocal_ctps2<- cellColocation(rctd_df, method='pcc', pcc=0.06, pval=0.05, padj=0.05)
# By calculating Jaccard similarity coefficient based on the RCTD objectcolocal_ctps3<- cellColocation(rctd_obj, rctd_mode='multi', conf=TRUE, method='jac', jac=0.05)
# By calculating Jaccard similarity coefficient based on the RCTD objectcolocal_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 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 reference 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's exact test. st_comm function can help you characterize confident spatially resolved cell-cell interaction with the tissue organization.

# load the CellChat object for the refrence single cell transcriptomic datacellchat<- 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 objectnet.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)

About

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.

Resources

Readme
GPL-3.0 license
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2 stars

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GitHub - SuoLab-GZLab/STcomm: 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. · GitHub
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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
STcomm R package can be easily installed from Github using devtools:
devtools::install_github("Vanjia-lee/STcomm")
Quick Guide to Getting Started with stComm
Firstly, rctd4weights function will help you tidy the confident
weights for every pixel after RCTD analysis.
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.
# By calculating Pearson correlation coefficient based on the RCTD object colocal_ctps1<- cellColocation(rctd_obj, rctd_mode='multi', conf=TRUE,
method='pcc', pcc=0.06, pval=0.05, padj=0.05)
# By calculating Pearson correlation coefficient based on the data.frame of RCTD weights for each pixelcolocal_ctps2<- cellColocation(rctd_df, method='pcc', pcc=0.06, pval=0.05, padj=0.05)
# By calculating Jaccard similarity coefficient based on the RCTD objectcolocal_ctps3<- cellColocation(rctd_obj, rctd_mode='multi', conf=TRUE, method='jac', jac=0.05)
# By calculating Jaccard similarity coefficient based on the RCTD objectcolocal_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 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 reference 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's exact test. st_comm function can help you
characterize confident spatially resolved cell-cell interaction with the
tissue organization.
# load the CellChat object for the refrence single cell transcriptomic datacellchat<- 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 objectnet.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)
About
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.
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
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GitHub - SuoLab-GZLab/STcomm: 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. · GitHub
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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
STcomm R package can be easily installed from Github using devtools:
devtools::install_github("Vanjia-lee/STcomm")
Quick Guide to Getting Started with stComm
Firstly, rctd4weights function will help you tidy the confident
weights for every pixel after RCTD analysis.
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.
# By calculating Pearson correlation coefficient based on the RCTD object colocal_ctps1<- cellColocation(rctd_obj, rctd_mode='multi', conf=TRUE,
method='pcc', pcc=0.06, pval=0.05, padj=0.05)
# By calculating Pearson correlation coefficient based on the data.frame of RCTD weights for each pixelcolocal_ctps2<- cellColocation(rctd_df, method='pcc', pcc=0.06, pval=0.05, padj=0.05)
# By calculating Jaccard similarity coefficient based on the RCTD objectcolocal_ctps3<- cellColocation(rctd_obj, rctd_mode='multi', conf=TRUE, method='jac', jac=0.05)
# By calculating Jaccard similarity coefficient based on the RCTD objectcolocal_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 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 reference 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's exact test. st_comm function can help you
characterize confident spatially resolved cell-cell interaction with the
tissue organization.
# load the CellChat object for the refrence single cell transcriptomic datacellchat<- 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 objectnet.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)
About
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.
Resources
Readme
GPL-3.0 license
Activity
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Stars
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Report repository
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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
STcomm R package can be easily installed from Github using devtools:
devtools::install_github("Vanjia-lee/STcomm")
Quick Guide to Getting Started with stComm
Firstly, rctd4weights function will help you tidy the confident
weights for every pixel after RCTD analysis.
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.
# By calculating Pearson correlation coefficient based on the RCTD object colocal_ctps1<- cellColocation(rctd_obj, rctd_mode='multi', conf=TRUE,
method='pcc', pcc=0.06, pval=0.05, padj=0.05)
# By calculating Pearson correlation coefficient based on the data.frame of RCTD weights for each pixelcolocal_ctps2<- cellColocation(rctd_df, method='pcc', pcc=0.06, pval=0.05, padj=0.05)
# By calculating Jaccard similarity coefficient based on the RCTD objectcolocal_ctps3<- cellColocation(rctd_obj, rctd_mode='multi', conf=TRUE, method='jac', jac=0.05)
# By calculating Jaccard similarity coefficient based on the RCTD objectcolocal_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 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 reference 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's exact test. st_comm function can help you
characterize confident spatially resolved cell-cell interaction with the
tissue organization.
# load the CellChat object for the refrence single cell transcriptomic datacellchat<- 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 objectnet.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)
About
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.
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
2 stars
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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
STcomm R package can be easily installed from Github using devtools:
devtools::install_github("Vanjia-lee/STcomm")
Quick Guide to Getting Started with stComm
Firstly, rctd4weights function will help you tidy the confident
weights for every pixel after RCTD analysis.
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.
# By calculating Pearson correlation coefficient based on the RCTD object colocal_ctps1<- cellColocation(rctd_obj, rctd_mode='multi', conf=TRUE,
method='pcc', pcc=0.06, pval=0.05, padj=0.05)
# By calculating Pearson correlation coefficient based on the data.frame of RCTD weights for each pixelcolocal_ctps2<- cellColocation(rctd_df, method='pcc', pcc=0.06, pval=0.05, padj=0.05)
# By calculating Jaccard similarity coefficient based on the RCTD objectcolocal_ctps3<- cellColocation(rctd_obj, rctd_mode='multi', conf=TRUE, method='jac', jac=0.05)
# By calculating Jaccard similarity coefficient based on the RCTD objectcolocal_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 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 reference 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's exact test. st_comm function can help you
characterize confident spatially resolved cell-cell interaction with the
tissue organization.
# load the CellChat object for the refrence single cell transcriptomic datacellchat<- 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 objectnet.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)
About
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.
Resources
Readme
GPL-3.0 license
Activity
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Stars
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Report repository
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GitHub - SuoLab-GZLab/STcomm: 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. · GitHub
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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
STcomm R package can be easily installed from Github using devtools:
devtools::install_github("Vanjia-lee/STcomm")
Quick Guide to Getting Started with stComm
Firstly, rctd4weights function will help you tidy the confident
weights for every pixel after RCTD analysis.
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.
# By calculating Pearson correlation coefficient based on the RCTD object colocal_ctps1<- cellColocation(rctd_obj, rctd_mode='multi', conf=TRUE,
method='pcc', pcc=0.06, pval=0.05, padj=0.05)
# By calculating Pearson correlation coefficient based on the data.frame of RCTD weights for each pixelcolocal_ctps2<- cellColocation(rctd_df, method='pcc', pcc=0.06, pval=0.05, padj=0.05)
# By calculating Jaccard similarity coefficient based on the RCTD objectcolocal_ctps3<- cellColocation(rctd_obj, rctd_mode='multi', conf=TRUE, method='jac', jac=0.05)
# By calculating Jaccard similarity coefficient based on the RCTD objectcolocal_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 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 reference 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's exact test. st_comm function can help you
characterize confident spatially resolved cell-cell interaction with the
tissue organization.
# load the CellChat object for the refrence single cell transcriptomic datacellchat<- 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 objectnet.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)
About
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.
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GitHub - SuoLab-GZLab/STcomm: 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. · GitHub
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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
STcomm R package can be easily installed from Github using devtools:
devtools::install_github("Vanjia-lee/STcomm")
Quick Guide to Getting Started with stComm
Firstly, rctd4weights function will help you tidy the confident
weights for every pixel after RCTD analysis.
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.
# By calculating Pearson correlation coefficient based on the RCTD object colocal_ctps1<- cellColocation(rctd_obj, rctd_mode='multi', conf=TRUE,
method='pcc', pcc=0.06, pval=0.05, padj=0.05)
# By calculating Pearson correlation coefficient based on the data.frame of RCTD weights for each pixelcolocal_ctps2<- cellColocation(rctd_df, method='pcc', pcc=0.06, pval=0.05, padj=0.05)
# By calculating Jaccard similarity coefficient based on the RCTD objectcolocal_ctps3<- cellColocation(rctd_obj, rctd_mode='multi', conf=TRUE, method='jac', jac=0.05)
# By calculating Jaccard similarity coefficient based on the RCTD objectcolocal_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 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 reference 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's exact test. st_comm function can help you
characterize confident spatially resolved cell-cell interaction with the
tissue organization.
# load the CellChat object for the refrence single cell transcriptomic datacellchat<- 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 objectnet.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)
About
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.
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
2 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages