README.md

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

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.

``` {r, eval=FALSE} 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
``` {r, eval=FALSE}
library(CSSN)
example(CSSNEst)

Remarks

• If you have any questions regarding this package, please contact Jinge Yu at [e-mail].

jingeyu/CSSN documentation built on Jan. 9, 2022, 7:35 p.m.