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CSSN: Recovering spatially-varying cell-specific gene co-expression networks for single-cell spatial expression data

The R package CSSN implements the two-step algorithm proposed by Jinge Yu and Xiangyu Luo that can recover spatially-varying cell-specific gene co-expression networks for single-cell spatial expression data. The name CSSN is short for "Cell-Specific Spatial Network." Functions in the package can provide estimates for gene co-expression networks of each cell and predict gene co-expression networks in a centroid location where cells are missing. CSSN can be installed in Windows, Linux, and Mac OS.

Prerequisites and Installation

  1. R version >= 3.6.
  2. R packages: pheatmap (>= 1.0.12), stats (>= 4.0.3)
  3. Install the package CSSN.
devtools::install_github("jingeyu/CSSN")

Example Code

The following shows an example that runs the main functions "CSSNEst" and "CSSNPredict" in our package.

library(CSSN)
#install.packages('ggplot2')
library(ggplot2)
#install.packages('pheatmap')
library(pheatmap)

#read example data
data(example_data)

# gene number
G <- nrow(X)
# cell number
n <- ncol(X)

#---- set spatial pattern manually----
pal <- c(rgb(221, 160, 221, maxColorValue = 255),
        rgb(0, 206, 209, maxColorValue = 255))
pal <- setNames(pal, c("1", "2"))

#-----Cell's Spatial Pattern------
cell.type <- as.vector(cell.info[,1])
gg <- ggplot(cell.info, aes(x = X, y = Y, col = as.factor(cell.type), shape = as.factor(cell.type)))
pl <- gg + geom_point(size = 2.5) +
 scale_color_manual(values = c(pal[1], pal[2])) +
 theme_bw()+
 theme(legend.text=element_text(size=20),
       axis.title.x=element_text(size=16),
       axis.title.y=element_text(size=16),
       axis.text.x = element_text(size = 12,face = "bold"),
       axis.text.y = element_text(size = 12,face = "bold")
 ) + labs(x = "H", y = "L") +
 guides(color = guide_legend(title = "Cell Type",
                             title.theme = element_text(size = 25),
                             override.aes = list(size = 5)
 ),
 shape = guide_legend(title = "Cell Type",
                      title.theme = element_text(size = 25),
                      override.aes = list(size = 5)))
ggsave("cell spatial.png", pl, width = 9, height = 12)


#----run CSSNEst--------
nu <- rep(2*G, n)
Result <- CSSNEst(X, cell.info, nu = nu, d = 0.1, m.info = 70, is.scale = TRUE, is.all = TRUE)
indx.cell <- c(1,3,7,10)
result <- CSSNEst(X, cell.info, nu = nu, d = 0.1, m.info = 70, is.scale = TRUE, is.all = FALSE, indx.cell = indx.cell, output.corr = TRUE)
#-----The first five cell's estimated gene co-expression networks-----
colors_func <- colorRampPalette(c('white', "black"))
colors <- colors_func(2)
filename <- paste0("Est_", 1:5, ".png")
for(i in 1:10){
 p2 <- pheatmap(Result[[i]],
                color = colors,
                legend_breaks = c(0,1),
                cluster_cols = F, cluster_rows = F,
                show_rownames = F, show_colnames = F,
                width = 3.3, height = 2.8,
                filename = filename[i]

 )
}
# Prediction
set.seed(1)
miss.num <- 5
miss.x <- runif(miss.num, min(cell.info[,2]), max(cell.info[,2]))
miss.y <- runif(miss.num, min(cell.info[,3]), max(cell.info[,3]))
miss.indx <- cbind(miss.x, miss.y)
pre <- CSSNPredict(Result, cell.info, miss.indx)

or you can simply run

library(CSSN)
example(CSSNEst)

Remarks

  • If you have any questions regarding this package, please contact Jinge Yu at yjgruc@ruc.edu.cn.