spark

Compute spatially expressed genes with SPARK method

spark(
  gobject,
  spat_loc_name = "raw",
  feat_type = NULL,
  spat_unit = NULL,
  percentage = 0.1,
  min_count = 10,
  expression_values = "raw",
  num_core = 5,
  covariates = NULL,
  return_object = c("data.table", "spark"),
  ...
)

Arguments

gobject

giotto object

spat_loc_name

name for spatial locations

feat_type

feature type

spat_unit

spatial unit

percentage

The percentage of cells that are expressed for analysis

min_count

minimum number of counts for a gene to be included

expression_values

type of values to use (raw by default)

num_core

number of cores to use

covariates

The covariates in experiments, i.e. confounding factors/batch effect. Column name of giotto cell metadata.

return_object

type of result to return (data.table or spark object)

...

Additional parameters to the spark.vc function

Value

data.table with SPARK spatial genes results or the SPARK object

Details

This function is a wrapper for the method implemented in the SPARK package: SPARK package:

1. CreateSPARKObject create a SPARK object from a giotto object

2. spark.vc Fits the count-based spatial model to estimate the parameters, see spark.vc for additional parameters

3. spark.test Testing multiple kernel matrices

Publication: doi:10.1101/810903

Examples

g <- GiottoData::loadGiottoMini("visium")
#> 1. read Giotto object
#> 2. read Giotto feature information
#> 3. read Giotto spatial information
#> 3.1 read Giotto spatial shape information
#> 3.2 read Giotto spatial centroid information
#> 3.3 read Giotto spatial overlap information
#> 4. read Giotto image information
#> 
#> checking default envname 'giotto_env'
#> a system default python environment was found
#> Using python path:
#>  "/usr/bin/python3"

spark(g)
#> Error: package 'SPARK' is not yet installed
#> 
#>  To install:
#> devtools::install_github("xzhoulab/SPARK")