runDWLSDeconv

Function to perform DWLS deconvolution based on single cell expression data

runDWLSDeconv(
  gobject,
  spat_unit = NULL,
  feat_type = NULL,
  expression_values = c("normalized"),
  logbase = 2,
  cluster_column = "leiden_clus",
  sign_matrix,
  n_cell = 50,
  cutoff = 2,
  name = NULL,
  return_gobject = TRUE
)

Arguments

gobject

giotto object

spat_unit

spatial unit

feat_type

feature type

expression_values

expression values to use

logbase

base used for log normalization

cluster_column

name of cluster column

sign_matrix

sig matrix for deconvolution

n_cell

number of cells per spot

cutoff

cut off (default = 2)

name

name to give to spatial deconvolution results, default = DWLS

return_gobject

return giotto object

Value

giotto object or deconvolution results

See also

https://github.com/dtsoucas/DWLS for the DWLS bulk deconvolution method, and doi:10.1186/s13059-021-02362-7 for spatialDWLS, the spatial implementation used here.

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
#> python already initialized in this session
#>  active environment : '/usr/bin/python3'
#>  python version : 3.10
#> checking default envname 'giotto_env'
#> a system default python environment was found
#> Using python path:
#>  "/usr/bin/python3"
x <- findMarkers_one_vs_all(g,
    cluster_column = "leiden_clus", min_feats = 20
)
#> using 'Scran' to detect marker feats. If used in published
#>       research, please cite: Lun ATL, McCarthy DJ, Marioni JC (2016).
#>       'A step-by-step workflow for low-level analysis of single-cell RNA-seq
#>       data with Bioconductor.'
#>       F1000Res., 5, 2122. doi: 10.12688/f1000research.9501.2. 
#> start with cluster  1start with cluster  2start with cluster  3start with cluster  4start with cluster  5start with cluster  6start with cluster  7
sign_gene <- x$feats

sign_matrix <- matrix(rnorm(length(sign_gene) * 8, mean = 10),
    nrow = length(sign_gene)
)
rownames(sign_matrix) <- sign_gene
colnames(sign_matrix) <- paste0("celltype_", unique(x$cluster))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent

runDWLSDeconv(gobject = g, sign_matrix = sign_matrix)
#> Error: package 'quadprog' is not yet installed
#> 
#>  To install:
#> install.packages(c("quadprog"))