processGiotto

Wrapper for the different Giotto object processing functions

processGiotto(
  gobject,
  filter_params = list(),
  norm_params = list(),
  stat_params = list(),
  adjust_params = list(),
  verbose = TRUE
)

Arguments

gobject

giotto object

filter_params

additional parameters to filterGiotto

norm_params

additional parameters to normalizeGiotto

stat_params

additional parameters to addStatistics

adjust_params

additional parameters to adjustGiottoMatrix; set to NULL if not required

verbose

be verbose (default is TRUE)

Value

giotto object

Details

See filterGiotto, normalizeGiotto, addStatistics, and adjustGiottoMatrix. For more information about the different parameters in each step. If you do not provide them it will use the default values. If no adjustment is required, adjust_params must be set to NULL

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 : 3.12

processGiotto(
    gobject = g,
    adjust_params = list(covariate_columns = "leiden_clus")
)
#> 1. start filter step
#> completed 1: preparation
#> completed 2: subset expression data
#> completed 3: subset spatial locations
#> completed 4: subset cell metadata
#> completed 5: subset feature metadata
#> completed 6: subset spatial network(s)
#> completed 7: subsetted dimension reductions
#> completed 8: subsetted nearest network(s)
#> completed 9: subsetted spatial enrichment results
#> for cell
#> --> cell found back in polygon layer: cell
#> completed 10: subsetted spatial information data
#> 
#> Feature type:  rna 
#> Number of cells removed:  2  out of  624 
#> Number of feats removed:  93  out of  634 
#> 2. start normalization step
#> first scale feats and then cells
#> > normalized already exists and will be replaced with new values
#> Setting expression [cell][rna] normalized
#> > scaled already exists and will be replaced with new values
#> Setting expression [cell][rna] scaled
#> 3. start cell and gene statistics step
#> calculating statistics for "normalized" expression
#> feat statistics has already been applied once; overwriting
#> cells statistics has already been applied once; overwriting
#> 4. start adjusted matrix step
#> design matrix of interest not specified. Assuming a one-group experiment.
#> Setting expression [cell][rna] custom
#> An object of class giotto 
#> >Active spat_unit:  cell 
#> >Active feat_type:  rna 
#> dimensions    : 541, 622 (features, cells)
#> [SUBCELLULAR INFO]
#> polygons      : cell 
#> [AGGREGATE INFO]
#> expression -----------------------
#>   [cell][rna] raw normalized scaled custom
#> spatial locations ----------------
#>   [cell] raw
#> spatial networks -----------------
#>   [cell] Delaunay_network spatial_network
#> spatial enrichments --------------
#>   [cell][rna] cluster_metagene DWLS
#> dim reduction --------------------
#>   [cell][rna] pca custom_pca umap custom_umap tsne
#> nearest neighbor networks --------
#>   [cell][rna] sNN.pca custom_NN
#> attached images ------------------
#> images      : alignment image 
#> 
#> 
#> Use objHistory() to see steps and params used