detectSpatialPatterns#

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Last Updated: Mar 11, 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’