library(Seurat)## Attaching SeuratObject
library(SpaTrio)
library(ggsci)
library(ggplot2)
library(readr)
library(reshape2)
library(dplyr)##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
We collected single-cell multi-omics data and spatial transcriptome data of human breast cancer. Datasets were obtained from Zenodo data repository and Gene Expression Omnibus
spatial_object <- readRDS("data/Human_breast_cancer/spatial_object.rds")
multi_object <- readRDS("data/Human_breast_cancer/multi_object.rds")dim(multi_object)## [1] 27719 6543
dim(spatial_object)## [1] 19237 357
UMAP projection of input single-cell multi-omics data colored by the RNA annotation.
major_col<-c("T-cells"= "#BC3C29FF",
"PVL" = "#0072B5FF" ,
"Endothelial" = "#E18727FF" ,
"CAFs" = "#20854EFF" ,
"Myeloid" = "#7876B1FF" ,
"Plasmablasts" = "#6F99ADFF",
"B-cells" = "#FFDC91FF")
multi_object$celltype_major=factor(multi_object$celltype_major,levels = rev(names(sort(table(multi_object$celltype_major)))))
Idents(multi_object)<-multi_object$celltype_major
DimPlot(multi_object,cols = major_col)
Spatial plot of input ST data colored by pathological regions.
class_level<-c("Invasive cancer","Invasive cancer + lymphocytes","Lymphocytes","Stroma","Artefact","Uncertain")
class_col<-c("Invasive cancer"= "#3B4992FF",
"Invasive cancer + lymphocytes" = "#EE0000FF" ,
"Lymphocytes" = "#008B45FF" ,
"Stroma" = "#631879FF" ,
"Artefact" = "#008280FF" ,
"Uncertain" = "#BB0021FF" )
spatial_object$classification=factor(spatial_object$classification,levels = class_level)
Idents(spatial_object)<-spatial_object$classification
sdplot(spatial_object,pt.size.factor = 4,image.alpha = 1)+scale_fill_manual(values = class_col)## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
We enter the following data into SpaTrio, which will build spatial maps of single cells
- Gene expression count matrix of cells and spots (multi_rna.csv & spatial_rna.csv)
- Cluster/Cell-type information of cells and spots (multi_meta.csv & spatial_meta.csv)
- Low-dimensional representation of protein assay (emb.csv)
- Spatial position coordinates of spots (pos.csv)
spatrio(spatrio_path="/home/yph/SpaTrio-main",
py_path="/home/yph/anaconda3/envs/spatrio/bin/python",
input_path="data/Human_breast_cancer",
output_path="data/Human_breast_cancer",
top_num =15)## [1] "Using the Python interpreter with a path of /home/yph/anaconda3/envs/spatrio/bin/python"
Read in output and create single-cell spatial multi-omics data reconstructed by SpaTrio.
output <- read_csv("data/Human_breast_cancer/output.csv")## New names:
## Rows: 5355 Columns: 10
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (4): spot, cell, spot_type, cell_type dbl (6): ...1, value, x, y, Cell_xcoord,
## Cell_ycoord
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`
pred<-subset(multi_object,cell=output$cell)
coordinate = output[,c("Cell_xcoord","Cell_ycoord")]
coordinate<-as.data.frame(coordinate)
colnames(coordinate)<-c("x","y")
rownames(coordinate)<-output$cell
pred@images[["slice1"]]=NULL
pred@images$image = new(
Class = 'SlideSeq',
assay = "RNA",
key = "image_",
coordinates = coordinate)
output<-as.data.frame(output)
rownames(output)<-output$cell
pred<-AddMetaData(pred, output[,2:10])
# Modify protein name
adt_count<-as.data.frame(pred@assays$ADT@counts)
adt_count$adt<-rownames(adt_count)
adt_count$adt<-gsub(".1","",adt_count$adt,fixed=T)
adt_count$adt<-gsub(".2","",adt_count$adt,fixed=T)
adt_count<-aggregate(x=adt_count[,1:c(ncol(adt_count)-1)], by=list(adt_count$adt),sum)
index<-adt_count$Group.1
adt_count<-adt_count[,2:ncol(adt_count)]
rownames(adt_count)<-index
pred[['ADT']]<-CreateAssayObject(adt_count)Spatial plot of single-cell spatial multi-omics data reconstructed by SpaTrio.
pred$celltype_major=factor(pred$celltype_major,levels = rev(names(sort(table(pred$celltype_major)))))
Idents(pred)<-pred$celltype_major
major_col<-c("T-cells"= "#BC3C29FF",
"PVL" = "#0072B5FF" ,
"Endothelial" = "#E18727FF" ,
"CAFs" = "#20854EFF" ,
"Myeloid" = "#7876B1FF" ,
"Plasmablasts" = "#6F99ADFF",
"B-cells" = "#FFDC91FF")
sdplot(pred,pt.size.factor = 5,image.alpha = 1)+scale_fill_manual(values = major_col)+NoLegend()## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
Histogram of cell proportions
pred$spot_type=factor(pred$spot_type,levels = class_level)
cell.prop<-as.data.frame(prop.table(table(pred$celltype_major, pred$spot_type),2))
colnames(cell.prop)<-c("cluster","sample","proportion")
cell.prop<-cell.prop[cell.prop$sample%in%c("Invasive cancer","Invasive cancer + lymphocytes","Lymphocytes","Stroma"),]
ggplot(cell.prop,aes(x=sample,y=proportion,fill=cluster))+geom_bar(stat="identity",position="fill",width = 0.9)+theme_bw(base_rect_size=1.5)+theme(panel.grid =element_blank())+FontSize(x.title = 15, y.title = 15,y.text=15,x.text=15)+theme(legend.text = element_text(size=15))+theme(legend.key = element_rect(color = NA, fill = NA),legend.key.size = unit(1, "cm"))+labs(x = "Sample", y = "Proportion")+guides(fill=guide_legend(title=NULL))+ theme(axis.text.x = element_text(color ="black",angle = 45,hjust = 1),axis.text.y = element_text(color ="black"))+scale_fill_manual(values = major_col)
T cells play an important role in the tumor microenvironment, and occupy a high proportion of cells in most areas, so we first focus on T cells for analysis.
Idents(pred)<-pred$celltype_major
tcell<-subset(pred,ident="T-cells")
Idents(tcell)<-tcell$spot_type
adt<-subset(tcell,ident=c("Invasive cancer + lymphocytes","Invasive cancer","Lymphocytes","Stroma"))
adt@reductions[["apca"]]=NULL
adt@reductions[["pca"]]=NULL
DefaultAssay(adt)<-"ADT"
VariableFeatures(adt)<-rownames(adt)
adt<-NormalizeData(adt, normalization.method = "CLR", margin = 2)%>%ScaleData()%>%
RunPCA(npcs = 10,reduction.name = "apca")%>%FindNeighbors(reduction = "apca", dims = 1:10)%>%
RunUMAP(reduction = "apca",dims = 1:10)## Normalizing across cells
## Centering and scaling data matrix
## PC_ 1
## Positive: KLRG1, HER2, OX40, TIM-3, CD1C, CCR6, CCR4, CRTH2, TCRgd, CD62L
## CD34, CD133, FAS, CD11b, CXCR5, LFA-3, CD163, GITR, CD66b, CD28
## CXCR3, CD14, TACTILE, CCR7, PD-L2, ICOS, CR2, EGFR, BTLA, CD10
## Negative: B7-H4, CD45, CD127, CD40, CD3, PD-1, CD4, CD15, IgG1, CD45RO
## PD-L1, CD86, CD8a, TIGIT, CD19, CD45RA, CD2, TCRab, CD69, CD57
## CD48, CD44, IgG2a, EPCAM, 2B4, CD1d, CD103, MHCII, CD80, CD49a
## PC_ 2
## Positive: CD2, CD48, CD5, TCRab, CD69, CD4, CD39, CD45RO, CD3, PD-1
## ICOS, CD49d, CD1d, FAS, CD27, TCR-V24-J18, MHCII, CD44, CD45, CD103
## CD49f, CCR6, CD73, CD127, CD49a, OX40, KLRG1, GITR, CD141, Thy-1
## Negative: CD45RA, CTLA-4, IgG2a, CD8a, PD-L1, IgG1, CD14, IgG2b, CD15, TIGIT
## CD19, CD40, CD86, CD11b, CD25, PDGFRA, PD-L2, B7-H4, CD24, c-KIT
## ICOSL, EGFR, CCR7, CD66b, BTLA, TCRVa7-2, HVEM, CD163, CD34, CR2
## PC_ 3
## Positive: CD103, 2B4, CD49a, CD69, CD57, MHCII, CD45RA, CD8a, CD49d, CD11C
## CD2, NKG2D, CD49b, CD39, CD24, CD48, CD31, Thy-1, CD44, CD38
## CD86, ICOS, CD27, PD-L1, EPCAM, CD25, PDGFRb, IgG2a, CD11b, PDGFRA
## Negative: CD19, CD4, CD45RO, CD28, CD3, CD5, CD127, CD1d, PD-1, CD49f
## CD1C, TIM-3, KLRG1, FAS-L, OX40, CR2, HER2, CCR7, BTLA, CCR4
## CD62L, CCR6, CD40, LFA-3, 4-1BB, TIGIT, TACTILE, TCRgd, CD138, CD133
## PC_ 4
## Positive: Thy-1, MHCII, CD24, CD11C, CD49b, CD31, CD49f, CCR6, CD19, CD1d
## CD127, B7H3, CD3, CD4, Podoplanin, CD45RO, CD34, CD45, EPCAM, RANKL
## TCRgd, CD49a, KLRG1, PD-1, CD73, B7-H4, CD38, CR2, CD123, OX40
## Negative: CD5, CD2, ICOS, 4-1BB, CD45RA, CD57, CCR5, CXCR5, FAS, TCRab
## CD69, PDGFRA, TIGIT, NKP46, CD25, TCR-V24-J18, Nectin-2, CD86, CD80, MCAM
## CD163, CD40LG, IgG2a, FAS-L, 4-1BBL, PDGFRb, CD27, BTLA, ICOSL, CD103
## PC_ 5
## Positive: ICOS, MHCII, CD39, CD5, TIGIT, CD49b, CD25, CTLA-4, CD38, CD45RA
## FAS, Thy-1, CD86, GITR, PD-L1, TCRab, CD40LG, FAS-L, IgG1, CD24
## CD11C, CD10, 4-1BB, CD40, EPCAM, EGFR, CD27, CD80, CXCR5, HVEM
## Negative: NKG2D, CD48, 2B4, CD1d, CD31, CD49a, CD8a, ICOSL, NKP46, TLR4
## c-KIT, TCRVa7-2, CD49f, CD66b, IgG2a, CD57, CD127, CD138, BTLA, TCRgd
## TIM-3, CD73, HER2, CD141, CD123, KLRG1, CD1C, CD45, CD69, CD163
## Warning: Cannot add objects with duplicate keys (offending key: PC_), setting
## key to 'apca_'
## Computing nearest neighbor graph
## Computing SNN
## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session
## 19:03:40 UMAP embedding parameters a = 0.9922 b = 1.112
## 19:03:40 Read 1431 rows and found 10 numeric columns
## 19:03:40 Using Annoy for neighbor search, n_neighbors = 30
## 19:03:40 Building Annoy index with metric = cosine, n_trees = 50
## 0% 10 20 30 40 50 60 70 80 90 100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 19:03:40 Writing NN index file to temp file /tmp/RtmpzK0XZa/file735d6502c397
## 19:03:40 Searching Annoy index using 1 thread, search_k = 3000
## 19:03:41 Annoy recall = 100%
## 19:03:41 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 19:03:42 Initializing from normalized Laplacian + noise (using irlba)
## 19:03:42 Commencing optimization for 500 epochs, with 54236 positive edges
## 19:03:43 Optimization finished
adt$xcoord<-adt@images$image@coordinates$x
adt$ycoord<-adt@images$image@coordinates$y
DefaultAssay(adt)<-"ADT"
feature_select<-rownames(adt)
module_list<-spatrio_resonance(adt,
sigma=110,
assay='ADT',
feature_select=feature_select,
correlation='pearson',
maxK=8,
k=6,
min_avg_con=0.6,
min_avg_cor=0.6,
min_featuer=5,
max_featuer=100,
smooth = "reduction",
reduction = "umap",
smooth_k = 10)## Feature filtering...
## Using all features...
## Data smoothing
## Smoothing with reduction...
## Loading required package: foreach
## Loading required package: iterators
## Loading required package: parallel
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## Merging results ..
##
## Time Elapsed: 13.7620165348053 secs
## Calculating with 99 features...
## Data scaling...
## Centering and scaling data matrix
## Spatially weighted correlation calculating...
## Consensus clustering...
## end fraction
## clustered
## clustered
## clustered
## clustered
## clustered
## clustered
## clustered
## Feature module calculating...
## [1] 5
spatrio_heatmap(module_list,ann_cols = pal_d3()(length(module_list$group)),border_color=NA)## $Group
## Module1 Module2
## "#1F77B4FF" "#FF7F0EFF"
adt<-spatrio_score(adt,module_list,nbin = 5,ctrl = 5,clean=T)
module_df=data.frame(module=NULL,motif=NULL)
for (i in names(module_list$group)) {
module_sub<-data.frame(module=i,motif=module_list$group[[i]])
module_df <-rbind(module_df,module_sub)
}
data=data.frame(Module1=adt$Module1,Module2=adt$Module2,cluster=adt$spot_type)
my_comparisons <- list(c("Stroma", "Invasive cancer + lymphocytes"),
c("Lymphocytes", "Invasive cancer + lymphocytes"),
c("Invasive cancer", "Invasive cancer + lymphocytes")
)
p1<-spatrio_boxplot(data,x="cluster",
y="Module1",
ylab = "ADT module score",
title = "Module1",
compare = my_comparisons,
cols=class_col[c("Stroma","Lymphocytes","Invasive cancer", "Invasive cancer + lymphocytes")],
x_level = c("Stroma","Lymphocytes","Invasive cancer", "Invasive cancer + lymphocytes"),vjust = 0.5)+
theme(axis.text.x = element_text(color ="black",angle = 45,hjust = 1))
p2<-spatrio_boxplot(data,x="cluster",
y="Module2",
ylab = "ADT module score",
title = "Module2",
compare = my_comparisons,
cols=class_col[c("Stroma","Lymphocytes","Invasive cancer", "Invasive cancer + lymphocytes")],
x_level = c("Stroma","Lymphocytes","Invasive cancer", "Invasive cancer + lymphocytes"),vjust = 0.5)+
theme(axis.text.x = element_text(color ="black",angle = 45,hjust = 1))
CombinePlots(list(p1,p2))## Warning: CombinePlots is being deprecated. Plots should now be combined using
## the patchwork system.
sfplot(adt, grep('Module', colnames(adt@meta.data), value=T),stroke = NA,pt.size.factor = 8,option = "D",max.cutoff="q90")## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Warning: CombinePlots is being deprecated. Plots should now be combined using
## the patchwork system.
Understanding cellular interactions in the tumor microenvironment can provide insights into tumor initiation, growth, metastasis, and drug resistance. Therefore, we selected the invasive cancer+lymphocytes area and performed spatial cellular multi-modal interactions analysis.
Idents(pred)<-pred$spot_type
object<-subset(pred,ident="Invasive cancer + lymphocytes")
Idents(object)<-object$celltype_major
# Transfer feature id of protein assay
library(readr)
id <- read_csv("data/Human_breast_cancer/id.csv")## New names:
## Rows: 99 Columns: 3
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (2): orig, transfer dbl (1): ...1
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`
count<-object@assays$ADT@counts
count<-as.data.frame(count)
adt_name<-rownames(count)
for (i in 1:nrow(count)) {
count$adt[i]<-id$transfer[id$orig==rownames(count)[i]][1]
}
count<-aggregate(x=count[,1:c(ncol(count)-1)], by=list(count$adt),sum)
index<-count$Group.1
count<-count[,2:ncol(count)]
rownames(count)<-index
count<-as.matrix(count)
object@assays$ADT<-CreateAssayObject(count)
object@assays[["ADT"]]@key<-"adt_"
# Extract major cell subpopulations
Idents(object)<-object$celltype_major
adt<-subset(object,idents = c("T-cells","Myeloid","Endothelial","PVL"))
DefaultAssay(adt)<-"ADT"
VariableFeatures(adt)<-rownames(adt)
adt<-NormalizeData(adt, normalization.method = "CLR", margin = 2)%>%ScaleData()%>%RunPCA(npcs = 10,reduction.name = "apca")%>%RunUMAP(dims = 1:10,reduction="apca")## Normalizing across cells
## Centering and scaling data matrix
## PC_ 1
## Positive: VTCN1, FUT4, IL7R, CD274, PTPRC, IGHG1, CD86, CD40, CD4, PDCD1
## CD14, TIGIT, CD19, CD3D, CTLA4, IGHG2, CD8A, ITGAX, CD44, HLA-A
## CD48, ITGA4, CD38, CD244, B3GAT1, ITGAM, THBD, CD2, IL3RA, CD69
## Negative: TCRgd, CCR6, TNFRSF4, CCR4, SELL, TIM3, ERBB2, PTGDR2, KLRG1, CD163
## FAS, TNFRSF18, CXCR3, CD96, CXCR5, CR2, EGFR, KIT, CCR7, TNFRSF14
## CD27, BTLA, TCRVa7-2, NCR1, SDC1, TNFSF9, PDCD1LG2, CD58, PDGFRB, ICOSLG
## PC_ 2
## Positive: HLA-A, ITGAX, ITGA4, CD44, CD38, ITGAM, TCR-V24-J18, CD48, CD80, CD276
## CD69, CD244, THBD, EPCAM, CCR5, NECTIN2, CD58, PROM1, ENTPD1, ITGAE
## TCRab, FASLG, ICOSLG, CD14, CD86, CD2, THY1, PDPN, KLRK1, PDCD1LG2
## Negative: IGHG2, CTLA4, IL2RA, CD19, TIGIT, CD8A, CD28, IGHG1, IL7R, PDCD1
## BTLA, CD34, KIT, PTGDR2, VTCN1, TCRVa7-2, CR2, CD96, TIM3, CCR7
## SELL, ERBB2, CCR4, CD3D, KLRG1, FUT4, NCR1, CEACAM8, CD24, TNFRSF4
## PC_ 3
## Positive: CD2, CD5, CD3D, CD69, PTPRC, TCRab, CD48, CD8A, CD4, ICOS
## PDCD1, CD244, CD28, ITGAE, CD1D, CCR5, CD27, KLRK1, CD24, TCR-V24-J18
## FAS, CD19, TIGIT, TCRVa7-2, TNFRSF18, TLR4, TNFRSF9, CD96, SDC1, ITGA4
## Negative: PECAM1, ITGA6, NT5E, ENTPD1, CD34, ITGA2, NECTIN2, THBD, IL3RA, THY1
## MCAM, ITGA1, CD276, CD40, TNFSF11, PDPN, B3GAT1, IGHG1, CD274, MME
## LAG3, CD58, VTCN1, CEACAM8, IGHG2, HLA-A, CD38, CCR7, PDCD1LG2, ICOSLG
## PC_ 4
## Positive: TCRab, CD2, CD5, ITGA2, CD3D, NT5E, CD1D, THY1, ITGA1, THBD
## NECTIN2, CD69, IL3RA, KLRK1, PDCD1, LAG3, ITGAE, CD27, MCAM, B3GAT1
## PTPRC, TCR-V24-J18, CD8A, FASLG, ICOSLG, CD4, TLR4, CD48, ICOS, CD34
## Negative: CD14, CD274, ITGAM, CD86, CD40LG, CD80, IGHG2, CTLA4, ITGAX, CD40
## PROM1, HLA-A, IL2RA, TNFSF11, CCR7, IGHG1, CCR5, CD58, EPCAM, CXCR5
## KIT, PDCD1LG2, CD24, CD38, FUT4, CD44, TIGIT, CD163, FAS, PDGFRA
## PC_ 5
## Positive: ITGA1, THY1, MCAM, CD14, ICOSLG, PDGFRB, SDC1, PDGFRA, CTLA4, PDCD1LG2
## PTPRC, CD8A, CEACAM8, TNFSF9, TNFRSF14, EGFR, TLR4, KIT, CD276, TIGIT
## CR2, TCRVa7-2, NCR1, CD86, ITGA2, IL3RA, CD58, CD80, BTLA, PDPN
## Negative: CD40, MME, PECAM1, CD1C, IL7R, ENTPD1, PDCD1, KLRG1, ITGA6, TNFRSF4
## B3GAT1, NT5E, TCRgd, CCR6, ERBB2, IGHG1, CD2, TCRab, CXCR3, CD274
## THBD, CD5, VTCN1, CD34, CD3D, CD48, TIM3, CCR4, CD1D, NECTIN2
## Warning: Cannot add objects with duplicate keys (offending key: PC_) setting
## key to original value 'APC_'
## 19:04:00 UMAP embedding parameters a = 0.9922 b = 1.112
## 19:04:00 Read 669 rows and found 10 numeric columns
## 19:04:00 Using Annoy for neighbor search, n_neighbors = 30
## 19:04:00 Building Annoy index with metric = cosine, n_trees = 50
## 0% 10 20 30 40 50 60 70 80 90 100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 19:04:00 Writing NN index file to temp file /tmp/RtmpzK0XZa/file735d4917a933
## 19:04:00 Searching Annoy index using 1 thread, search_k = 3000
## 19:04:01 Annoy recall = 100%
## 19:04:01 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 19:04:01 Initializing from normalized Laplacian + noise (using irlba)
## 19:04:01 Commencing optimization for 500 epochs, with 22348 positive edges
## 19:04:02 Optimization finished
feature_select<-rownames(adt)
adt$xcoord<-adt@images$image@coordinates$x
adt$ycoord<-adt@images$image@coordinates$y
module_list<-spatrio_resonance(adt,
sigma=110,
assay='ADT',
feature_select=feature_select,
correlation='pearson',
maxK=8,
k=8,
min_avg_con=0.3,
min_avg_cor=0.3,
min_featuer=5,
max_featuer=200,
smooth = "reduction",
reduction = "umap",
smooth_k = 10,
min_pct_cutoff = 0.1)## Feature filtering...
## Filtering features...
## Data smoothing
## Smoothing with reduction...
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## Merging results ..
##
## Time Elapsed: 13.4321479797363 secs
## Calculating with 96 features...
## Data scaling...
## Centering and scaling data matrix
## Spatially weighted correlation calculating...
## Consensus clustering...
## end fraction
## clustered
## clustered
## clustered
## clustered
## clustered
## clustered
## clustered
## Feature module calculating...
## [1] 5
load("~/spatrio_data/tutorial/data/Human_steatosis_liver/lrpairs.rda")
load("~/spatrio_data/tutorial/data/Human_steatosis_liver/pathways.rda")
obj<-gene_cci_analysis(adt,
assay = "RNA",
species = "Human",
xcoord = adt@images$image@coordinates$x,
ycoord = adt@images$image@coordinates$y,
celltype = adt$celltype_major,
lrpairs = lrpairs,
pathways = pathways)## Checking input data
## Begin to filter lrpairs and pathways
## ***Done***
## Note: there are 4 cell types and 12 pair-wise cell pairs
## Begin to find LR pairs
df<-gene_cci_plot(obj,
#celltype_receiver = c("Endothelial","Myeloid","PVL","T-cells"),
celltype_pair = c("Endothelial_T-cells","Myeloid_T-cells","PVL_T-cells","PVL_Myeloid","Myeloid_PVL","T-cells_PVL"),
lr_pair = strsplit("HLA-A_CD3D,HLA-B_CD3D,HLA-C_CD3D,HLA-A_CD3G,MIF_CXCR4,APOE_TREM2,PDGFB_PDGFRB,CXCL16_ADRA2A,CALM2_EGFR,TGFB1_SDC2",split = ",")[[1]],
color="#9a6a9e",return_data = T)
head(df)## ligand receptor species celltype_sender celltype_receiver lr_co_exp_num
## 94 TGFB1 SDC2 Human T-cells PVL 9
## 412 CALM2 EGFR Human T-cells PVL 5
## 808 HLA-A CD3G Human Endothelial T-cells 48
## 1332 HLA-A CD3D Human Endothelial T-cells 65
## 1333 HLA-C CD3D Human Endothelial T-cells 65
## 1334 HLA-B CD3D Human Endothelial T-cells 65
## lr_co_ratio lr_co_ratio_pvalue score pair lr
## 94 0.3461538 0 0.8597321 T-cells_PVL TGFB1_SDC2
## 412 0.1923077 0 0.9999960 T-cells_PVL CALM2_EGFR
## 808 0.4615385 0 0.8473712 Endothelial_T-cells HLA-A_CD3G
## 1332 0.6250000 0 0.9901887 Endothelial_T-cells HLA-A_CD3D
## 1333 0.6250000 0 0.9901887 Endothelial_T-cells HLA-C_CD3D
## 1334 0.6250000 0 0.9901887 Endothelial_T-cells HLA-B_CD3D
gene_cci_plot(obj,
#celltype_receiver = c("Endothelial","Myeloid","PVL","T-cells"),
celltype_pair = c("Endothelial_T-cells","Myeloid_T-cells","PVL_T-cells","PVL_Myeloid","Myeloid_PVL","T-cells_PVL"),
lr_pair = strsplit("HLA-A_CD3D,HLA-B_CD3D,HLA-C_CD3D,HLA-A_CD3G,MIF_CXCR4,APOE_TREM2,PDGFB_PDGFRB,CXCL16_ADRA2A,CALM2_EGFR,TGFB1_SDC2",split = ",")[[1]],
color="#9a6a9e")
library(data.table)##
## Attaching package: 'data.table'
## The following objects are masked from 'package:dplyr':
##
## between, first, last
## The following objects are masked from 'package:reshape2':
##
## dcast, melt
#Read in data frame containing protein id and gene id information
id <- read_csv("data/Human_breast_cancer/id.csv")## New names:
## • `` -> `...1`
## Rows: 99 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): orig, transfer
## dbl (1): ...1
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#Read in data frame containing information of cell-cell interaction
lr<-fread("data/Human_breast_cancer/human_lr_pair.txt")
df = adt_cci_analysis(adt,input_list =module_list,id=id,lr=lr,celltype_pair =c("Myeloid_T-cells","PVL_T-cells","PVL_Myeloid","Myeloid_PVL","T-cells_PVL"),
lr_pair =strsplit("FASLG_FAS,HLA-A_ERBB2,CD40LG_CD40,CD40LG_ITGAM,ICOSLG_ICOS,CD58_CD2,PDCD1LG2_PDCD1,CD14_TLR4,CD274_PDCD1,HLA-A_CD3D",split = ",")[[1]],mode = "2" )
head(df)## lr_pair ligand_gene_symbol receptor_gene_symbol sender_module
## 13 ICOSLG_ICOS ICOSLG ICOS k2
## 17 FASLG_FAS FASLG FAS k2
## 3 CD14_TLR4 CD14 TLR4 k3
## 6 CD40LG_ITGAM CD40LG ITGAM k2
## 18 CD58_CD2 CD58 CD2 k2
## 7 CD40LG_CD40 CD40LG CD40 k2
## reciever_module sender reciever ligand_exp receptor_exp score
## 13 k2 PVL T-cells 0.3123419 0.7903744 0.08023383
## 17 k2 T-cells PVL 0.1538620 3.6166253 0.12046015
## 3 k2 Myeloid T-cells 6.7263379 0.1777011 0.17654701
## 6 k3 PVL Myeloid 0.1586290 9.0196013 0.19315720
## 18 k5 PVL T-cells 1.7886164 0.9344921 0.20877205
## 7 k6 PVL Myeloid 0.1586290 14.5374608 0.24522317
## pair lr
## 13 PVL_T-cells ICOSLG_ICOS
## 17 T-cells_PVL FASLG_FAS
## 3 Myeloid_T-cells CD14_TLR4
## 6 PVL_Myeloid CD40LG_ITGAM
## 18 PVL_T-cells CD58_CD2
## 7 PVL_Myeloid CD40LG_CD40
adt_cci_plot(df,mode="2",color = "#cb6377")