gene_interpolation: Spatial interpolation of gene expression

Performs spatial interpolation ("kriging") of transformed gene counts

gene_interpolation(
  x = NULL,
  genes = "top",
  top_n = 10,
  samples = NULL,
  ngrid = 10000,
  REML = F,
  cores = NULL
)

Arguments

x: an STlist with transformed RNA counts
genes: a vector of gene names or 'top'. If 'top' (default), interpolation of the 10 genes (top_n default) with highest standard deviation in each ST sample is estimated.
top_n: an integer indicating how many top genes to perform interpolation. Default is 10.
samples: the spatial samples for which interpolations will be performed. If NULL (Default), all samples are interpolated.
ngrid: an integer indicating the number of point to predict. Default is 10000, resulting in a grid of 100 x 100 points. Larger numbers provide more resolution, but computing time is longer.
REML: wheter to use restricted maximum likelihood to estimate spatial covariance parameters. Default is FALSE. Setting to TRUE might speed up interpolation, but some issue have been found.
cores: integer indicating the number of cores to use during parallelization. If NULL, the function uses half of the available cores at a maximum. The parallelization uses parallel::mclapply and works only in Unix systems.

Value

x a STlist including spatial interpolations.

Details

This function takes an STlist and a vector of gene names and generates spatial interpolation of gene expression values via "kriging". If genes='top', then the 10 genes (default) with the highest standard deviation for each ST sample are interpolated. The resulting interpolations can be visualized via the STplot_interpolation function

Examples

# Using included melanoma example (Thrane et al.)
library('spatialGE')
data_files <- list.files(system.file("extdata", package="spatialGE"), recursive=T, full.names=T)
count_files <- grep("counts", data_files, value=T)
coord_files <- grep("mapping", data_files, value=T)
clin_file <- grep("thrane_clinical", data_files, value=T)
melanoma <- STlist(rnacounts=count_files[c(1,2)], spotcoords=coord_files[c(1,2)], samples=clin_file) # Only first two samples
#> Warning: Sample ST_mel2_rep1 was not found among the count/coordinate files.
#> Warning: Sample ST_mel2_rep2 was not found among the count/coordinate files.
#> Warning: Sample ST_mel3_rep1 was not found among the count/coordinate files.
#> Warning: Sample ST_mel3_rep2 was not found among the count/coordinate files.
#> Warning: Sample ST_mel4_rep1 was not found among the count/coordinate files.
#> Warning: Sample ST_mel4_rep2 was not found among the count/coordinate files.
#> Found matrix data
#> Matching gene expression and coordinate data...
#> Converting counts to sparse matrices
#> Completed STlist!
#> 
melanoma <- transform_data(melanoma)
melanoma <- gene_interpolation(melanoma, genes=c('MLANA', 'COL1A1'), samples='ST_mel1_rep2')
#> Gene interpolation started.
#> Gene interpolation completed in 0.04 min.
#> 
kp = STplot_interpolation(melanoma, genes=c('MLANA', 'COL1A1'))
#> Kriging for subject MLANA in sample ST_mel1_rep1 is not present in STlist
#> Kriging for subject COL1A1 in sample ST_mel1_rep1 is not present in STlist
ggpubr::ggarrange(plotlist=kp)