Deconvolution with spacedeconv — deconvolute • spacedeconv

Perform cell type deconvolution with spacedeconv.

Usage

deconvolute(
  spatial_obj,
  signature = NULL,
  single_cell_obj = NULL,
  cell_type_col = "cell_ontology_class",
  method = NULL,
  batch_id_col = NULL,
  assay_sc = "counts",
  assay_sp = "counts",
  return_object = TRUE,
  verbose = FALSE,
  ...
)

Arguments

spatial_obj: A SpatialExperiment
signature: Gene Expression Signature
single_cell_obj: A SingleCellExperiment
cell_type_col: Column name of the single_cell_obj where the cell type can be found
method: Deconvolution Method to use, see deconvolution_methods() for a full list of available methods
batch_id_col: column name of batch ids in single cell object
assay_sc: which single cell assay to use for computation
assay_sp: which spatial assay to use for computation
return_object: Return an Object or result Table, TRUE = Object
verbose: display more detailed information
...: Further parameters passed to the selected deconvolution method

Value

The deconvolution result as a table

Examples

# more examples can be found in the documentation website
data("spatial_data_2")

spatial_data_2 <- spacedeconv::preprocess(spatial_data_2)
#> ── spacedeconv ─────────────────────────────────────────────────────────────────
#> ℹ testing parameter
#> ✔ parameter OK [44ms]
#> 
#> ℹ Removing 85 observations with umi count below threshold
#> ✔ Removed 85 observations with umi count below threshold [639ms]
#> 
#> ℹ Removing 13418 variables with all zero expression
#> Warning: There are 13 mitochondrial genes present. Consider removing them.
#> ✔ Removed 13418 variables with all zero expression [631ms]
#> 
#> ℹ Removing duplicated genes
#> ✔ Removed duplicated genes [48ms]
#> 
#> ℹ Checking for ENSEMBL Identifiers
#> ! Warning: ENSEMBL identifiers detected in gene names
#> ℹ Checking for ENSEMBL Identifiers

#> ℹ Consider using Gene Names for first-generation deconvolution tools
#> ℹ Checking for ENSEMBL Identifiers

#> ✔ Finished Preprocessing [6ms]
#> 
spatial_data_2 <- spacedeconv::normalize(spatial_data_2)
#> ── spacedeconv ─────────────────────────────────────────────────────────────────
#> ℹ testing parameter
#> ✔ parameter OK [12ms]
#> 
#> ℹ Normalizing using cpm
#> ✔ Finished normalization using cpm [2.3s]
#> 
#> ℹ Please note the normalization is stored in an additional assay

deconvolution <- spacedeconv::deconvolute(
  spatial_obj = spatial_data_2,
  method = "estimate",
)
#> ── spacedeconv ─────────────────────────────────────────────────────────────────
#> ℹ testing parameter
#> ✔ parameter OK [15ms]
#> 
#> 
#> 
#> ── Spatial 
#> Assays: "counts" and "cpm"
#> Genes: 23173
#> → without expression: 0 (0%)
#> Spots: 1225
#> Spots under tissue: 1225 (100%)
#> Median Genes Per Spot: 4795
#> → without expression: 0 (0%)
#> Umi count range: 536 - 64683
#> Spots with UMI count below 500: 0 (0%)
#> ✔ Rownames set
#> ✔ Colnames set
#> ℹ deconvoluting
#> Error in loadNamespace(x): there is no package called ‘immunedeconv’
#> ✖ deconvoluting [163ms]
#>