detectSpatialPatterns#

Last Updated: Nov 17, 2023

Description#

Identify spatial patterns through PCA on average expression in a spatial grid.

Usage#

detectSpatialPatterns(
  gobject,
  expression_values = c("normalized", "scaled", "custom"),
  spatial_grid_name = "spatial_grid",
  min_cells_per_grid = 4,
  scale_unit = F,
  ncp = 100,
  show_plot = T,
  PC_zscore = 1.5
)

Arguments#

`gobject`	giotto object
`expression_values`	expression values to use
`spatial_grid_name`	name of spatial grid to use (default = ‘spatial_grid’)
`min_cells_per_grid`	minimum number of cells in a grid to be considered
`scale_unit`	scale features
`ncp`	number of principal components to calculate
`show_plot`	show plots
`PC_zscore`	minimum z-score of variance explained by a PC

Details#

Steps to identify spatial patterns:

1. average gene expression for cells within a grid, see createSpatialGrid
1. perform PCA on the average grid expression profiles
3. convert variance of principlal components (PCs) to z-scores and select PCs based on a z-score threshold

Value#

spatial pattern object ‘spatPatObj’