SVP uses the distance between cells and cells, features and features, cells and features in the space of MCA to build nearest neighbor graph, then uses random walk with restart algorithm to calculate the activity score of gene sets (such as cell marker genes, kegg pathway, go ontology, gene modules, transcription factor or miRNA target sets, reactome pathway, …), which is then further weighted using the hypergeometric test results from the original expression matrix. To detect the spatially or single cell variable gene sets or (other features) and the spatial colocalization between the features accurately, SVP provides some global and local spatial autocorrelation method to identify the spatial variable features. SVP is developed based on SingleCellExperiment class, which can be interoperable with the existing computing ecosystem.
Shuangbin Xu and Guangchuang Yu
School of Basic Medical Sciences, Southern Medical University
The development version from github:
if (!requireNamespace("remotes", quietly=TRUE))
install.packages("remotes")
remotes::install_github("xiangpin/SVP")To enhance performance, it is strongly recommended to connect your R
BLAS library with the
OpenBLAS library for matrix
calculations. This can be accomplished using the
ropenblas package. Or you can
install OpenBLAS and link the
library to R BLAS library by
ln -s your_openblas_installed_path_libopenblas.so your_R_install_path_libRblas.so
manually.
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