filter Giotto object based on expression threshold
filterGiotto(
gobject,
spat_unit = NULL,
feat_type = NULL,
expression_values = c("raw", "normalized", "scaled", "custom"),
expression_threshold = 1,
feat_det_in_min_cells = 100,
min_det_feats_per_cell = 100,
spat_unit_fsub = ":all:",
feat_type_ssub = ":all:",
all_spat_units = NULL,
all_feat_types = NULL,
poly_info = NULL,
tag_cells = FALSE,
tag_cell_name = "tag",
tag_feats = FALSE,
tag_feats_name = "tag",
verbose = TRUE
)giotto object
character. spatial unit. If more than one is provided then the first will be filtered, the filtering results will be applied across the other spat_units provided
character. feature type. If more than one is provided then the first will be filtered, the filtering results will be applied across the other feat_types provided.
expression values to use
threshold to consider a gene expressed
minimum # of cells that need to express a feature
minimum # of features that need to be detected in a cell
character vector. (default = ':all:') limit features to remove results to selected spat_units
character vector. (default = ':all:') limit cells to remove results to selected feat_types
deprecated. Use spat_unit_fsub = ":all:"
deprecated. Use feat_type_ssub = ":all:"
polygon information to use
tag filtered cells in metadata vs. remove cells
column name for tagged cells in metadata
tag features in metadata vs. remove features
column name for tagged features in metadata
verbose
giotto object
The function filterCombinations can be used to
explore the effect of different parameter values.
Please note that this function filters data in a predefined order, features,
then cells.
After filtering in this order, certain features may be left over in the
metadata with a corresponding number of cells which is less than that of
the threshold value of cells,
feat_det_in_min_cells. This behavior is explained in detail here:
https://github.com/drieslab/Giotto/issues/500#issuecomment-1396083446
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"
filterGiotto(g)
#> 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
#> 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
#> 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