return a revised Seurat object by adding three columns named "is.SVGs", "order.SVGs" and "adjusted.pval.SVGs" in the meta.features of default Assay.
Arguments
seu
an object of class "Seurat".
nfeatures
a positive integer, means how many spatially variable genes to be chosen. If there are less than 2000 features in seu, then all features are identified.
covariates
a covariate matrix named control variable matrix whose number of rows is equal to the number of columns of seu.
preHVGs
a positive integer, the number of highly variable genes selected for speeding up computation of SPARK-X in selecting spatially variable features.
num_core
an optional positive integer, specify the cores used for identifying the SVGs in parallel.
verbose
an optional logical value, whether output the related information.
Details
Nothing
References
Zhu, J., Sun, S., Zhou, X.: Spark-x: non-parametric modeling enables scalable and robust detection of spatialexpression patterns for large spatial transcriptomic studies. Genome Biology 22(1), 1-25 (2021)