Identify marker feats for all clusters in a one vs all manner based on gini detection and expression scores.
findGiniMarkers_one_vs_all(
gobject,
feat_type = NULL,
spat_unit = NULL,
expression_values = c("normalized", "scaled", "custom"),
cluster_column,
subset_clusters = NULL,
min_expr_gini_score = 0.5,
min_det_gini_score = 0.5,
detection_threshold = 0,
rank_score = 1,
min_feats = 4,
min_genes = NULL,
verbose = TRUE
)giotto object
feature type
spatial unit
feat expression values to use
clusters to use
selection of clusters to compare
filter on minimum gini coefficient on expression
filter on minimum gini coefficient on detection
detection threshold for feat expression
rank scores for both detection and expression to include
minimum number of top feats to return
deprecated, use min_feats
be verbose
data.table with marker feats
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"
findGiniMarkers_one_vs_all(g, cluster_column = "leiden_clus")
#> start with cluster 1start with cluster 2start with cluster 3start with cluster 4start with cluster 5start with cluster 6start with cluster 7
#> feats cluster expression expression_gini detection detection_gini
#> <char> <char> <num> <num> <num> <num>
#> 1: Slc17a8 1 0.2878205 0.2150880844 0.08024691 0.198534543
#> 2: Ncf2 1 0.2851795 0.1413891924 0.09876543 0.146040902
#> 3: Cdhr4 1 0.1245718 0.1221149303 0.04320988 0.105617978
#> 4: Slitrk6 1 0.4873898 0.1096027412 0.16049383 0.117139334
#> 5: Ipcef1 1 1.9056033 0.0806429249 0.51851852 0.075086690
#> ---
#> 1612: Baiap2 7 3.2529838 0.0030647011 0.70000000 0.008125429
#> 1613: Vstm2a 7 2.1500628 0.0110355110 0.60000000 0.001758182
#> 1614: Ppp1r1a 7 4.3554185 0.0005621340 0.90000000 0.002878733
#> 1615: Lmo3 7 2.5245086 0.0011044706 0.60000000 0.011664899
#> 1616: Epha5 7 2.2749732 0.0004963358 0.60000000 0.001758182
#> expression_rank detection_rank comb_score comb_rank
#> <num> <num> <num> <int>
#> 1: 1.0 1.0 4.270241e-02 1
#> 2: 1.0 1.0 2.064861e-02 2
#> 3: 1.0 1.0 1.289753e-02 3
#> 4: 1.0 1.0 1.283879e-02 4
#> 5: 1.0 1.0 6.055210e-03 12
#> ---
#> 1612: 0.1 0.1 2.490201e-07 629
#> 1613: 0.1 0.1 1.940244e-07 631
#> 1614: 1.0 0.1 1.618234e-07 632
#> 1615: 0.1 0.1 1.288354e-07 633
#> 1616: 0.1 0.1 8.726488e-09 634