spMOCA is a spatially informed statistical model developed to infer gene co-expression networks across spatial locations in a biologically meaningful way. spMOCA builds upon the principle that gene expression in spatial transcriptomics data is influenced by both spatial dependency and gene-gene interaction. Using a count matrix as input, spMOCA integrates these elements to yield a more accurate and nuanced understanding of gene co-expression networks with spatial contexts. spMOCA employs an efficient optimization algorithm for estimating gene-gene correlation, which is scalable to datasets with tens of thousands of spatial locations and tens of thousands of genes, surpassing the capabilities of existing methods.
You can install the released version of spMOCA from Github with the following code, for more installation details or solutions that might solve related issues (specifically MacOS system) see the link.
- R version >= 4.2.2.
- R packages: Matrix, ggplot2, dplyr, sf, stats, reshape2, gtools, RcppArmadillo, Rcpp, tidyverse
# install devtools if necessary
install.packages('devtools')
# install the spMOCA package
devtools::install_github('YMa-lab/spMOCA')
# load package
library(spMOCA)
The R package has been installed successfully on Operating systems:
- MAC: Sequoia 15.0.1
- Linux: RedHat7
All feedback, bug reports and suggestions are warmly welcomed! Please make sure to raise issues with a detailed and reproducible exmple and also please provide the output of your sessionInfo() in R!
Details in Tutorial