Print and return giotto object history
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 : 'giotto_env'
#> python version : 3.10
objHistory(g)
#> Steps and parameters used:
#> <0_normalize>
#> gobject : mini_visium
#> expression_values : raw
#> norm_methods : standard
#> library_size_norm : TRUE
#> scalefactor : 6000
#> log_norm : TRUE
#> log_offset : 1
#> logbase : 2
#> scale_feats : TRUE
#> scale_cells : TRUE
#> scale_order : first_feats
#> theta : 100
#> update_slot : scaled
#> verbose : T
#> <1_subset>
#> gobject : gobject
#> spat_unit : spat_unit
#> feat_type : feat_type
#> cell_ids : selected_cell_ids
#> feat_ids : selected_feat_ids
#> poly_info : poly_info
#> spat_unit_fsub : spat_unit_fsub
#> feat_type_ssub : feat_type_ssub
#> verbose : verbose
#> toplevel_params : 2
#> <2_filter>
#> gobject : mini_visium
#> expression_values : raw
#> expression_threshold : 1
#> feat_det_in_min_cells : 5
#> min_det_feats_per_cell : 20
#> spat_unit_fsub : :all:
#> feat_type_ssub : :all:
#> tag_cells : FALSE
#> tag_cell_name : tag
#> tag_feats : FALSE
#> tag_feats_name : tag
#> verbose : T
#> <3_feat_stats>
#> gobject : mini_visium
#> expression_values : normalized
#> detection_threshold : 0
#> return_gobject : TRUE
#> verbose : TRUE
#> <4_cell_stats>
#> gobject : mini_visium
#> expression_values : normalized
#> detection_threshold : 0
#> return_gobject : TRUE
#> verbose : TRUE
#> <5_hvf>
#> gobject : mini_visium
#> expression_values : normalized
#> method : cov_groups
#> reverse_log_scale : FALSE
#> logbase : 2
#> expression_threshold : 0
#> nr_expression_groups : 20
#> zscore_threshold : 1.5
#> HVFname : hvf
#> difference_in_cov : 0.1
#> var_threshold : 1.5
#> set_seed : TRUE
#> seed_number : 1234
#> save_param : list()
#> default_save_name : HVFplot
#> return_gobject : TRUE
#> verbose : TRUE
#> <6_pca>
#> gobject : mini_visium
#> expression_values : normalized
#> reduction : cells
#> return_gobject : TRUE
#> center : TRUE
#> scale_unit : TRUE
#> ncp : 100
#> method : irlba
#> method_params : BiocParallel::SerialParam()
#> rev : FALSE
#> set_seed : TRUE
#> seed_number : 1234
#> verbose : TRUE
#> ... :
#> <7_umap>
#> gobject : mini_visium
#> expression_values : normalized
#> reduction : cells
#> dim_reduction_to_use : pca
#> dimensions_to_use : 1:10
#> return_gobject : TRUE
#> n_neighbors : 40
#> n_components : 2
#> n_epochs : 400
#> min_dist : 0.01
#> n_threads : NA
#> spread : 5
#> set_seed : TRUE
#> seed_number : 1234
#> verbose : TRUE
#> toplevel_params : 2
#> ... :
#> <8_tsne>
#> gobject : mini_visium
#> expression_values : normalized
#> reduction : cells
#> dim_reduction_to_use : pca
#> dimensions_to_use : 1:10
#> return_gobject : TRUE
#> dims : 2
#> perplexity : 30
#> theta : 0.5
#> do_PCA_first : FALSE
#> set_seed : TRUE
#> seed_number : 1234
#> verbose : TRUE
#> ... :
#> <9_nn_network>
#> gobject : mini_visium
#> type : sNN
#> dim_reduction_to_use : pca
#> dimensions_to_use : 1:5
#> expression_values : normalized
#> return_gobject : TRUE
#> k : 10
#> minimum_shared : 5
#> top_shared : 3
#> verbose : TRUE
#> ... :
#> <10_cluster>
#> gobject : mini_visium
#> name : leiden_clus
#> nn_network_to_use : sNN
#> network_name : sNN.pca
#> resolution : 0.1
#> weight_col : weight
#> partition_type : RBConfigurationVertexPartition
#> n_iterations : 1000
#> return_gobject : TRUE
#> set_seed : TRUE
#> seed_number : 1234
#> <11_delaunay_spatial_network>
#> dimensions used : dimensions: sdimx and sdimy
#> method : deldir
#> maximum distance threshold : auto
#> name of spatial network : Delaunay_network
#> <12_spatial_network>
#> k neighbours : 10
#> dimensions used : all
#> maximum distance threshold : 400
#> name of spatial network : spatial_network
#> <13_create_metafeat>
#> gobject : mini_visium
#> expression_values : normalized
#> feat_clusters : cluster_genes
#> stat : mean
#> name : cluster_metagene
#> return_gobject : TRUE
#> <14_pca>
#> gobject : mini_visium
#> expression_values : normalized
#> reduction : cells
#> name : custom_pca
#> feats_to_use : my_spatial_genes
#> return_gobject : TRUE
#> center : TRUE
#> scale_unit : TRUE
#> ncp : 100
#> method : irlba
#> method_params : BiocParallel::SerialParam()
#> rev : FALSE
#> set_seed : TRUE
#> seed_number : 1234
#> verbose : TRUE
#> ... :
#> <15_umap>
#> gobject : mini_visium
#> expression_values : normalized
#> reduction : cells
#> dim_reduction_to_use : pca
#> dim_reduction_name : custom_pca
#> dimensions_to_use : 1:20
#> name : custom_umap
#> return_gobject : TRUE
#> n_neighbors : 40
#> n_components : 2
#> n_epochs : 400
#> min_dist : 0.01
#> n_threads : NA
#> spread : 5
#> set_seed : TRUE
#> seed_number : 1234
#> verbose : TRUE
#> toplevel_params : 2
#> ... :
#> <16_nn_network>
#> gobject : mini_visium
#> type : sNN
#> dim_reduction_to_use : pca
#> dim_reduction_name : custom_pca
#> dimensions_to_use : 1:20
#> expression_values : normalized
#> name : custom_NN
#> return_gobject : TRUE
#> k : 5
#> minimum_shared : 5
#> top_shared : 3
#> verbose : TRUE
#> ... :
#> <17_cluster>
#> gobject : mini_visium
#> name : custom_leiden
#> nn_network_to_use : sNN
#> network_name : custom_NN
#> resolution : 0.15
#> weight_col : weight
#> partition_type : RBConfigurationVertexPartition
#> n_iterations : 1000
#> return_gobject : TRUE
#> set_seed : TRUE
#> seed_number : 1234
#> <18_spatial_deconvolution>
#> method used : DWLS
#> deconvolution name : DWLS
#> expression values : normalized
#> logbase : 2
#> cluster column used : leiden_clus
#> number of cells per spot : 50
#> used cut off : 2
#> <19_spatial_deconvolution>
#> method used : DWLS
#> deconvolution name : DWLS
#> expression values : normalized
#> logbase : 2
#> cluster column used : leiden_clus
#> number of cells per spot : 50
#> used cut off : 2
#> <20_spatial_deconvolution>
#> method used : DWLS
#> deconvolution name : DWLS
#> expression values : normalized
#> logbase : 2
#> cluster column used : leiden_clus
#> number of cells per spot : 50
#> used cut off : 2
objHistory(g, summarized = TRUE)
#> Processing steps:
#> 0_normalize
#> 1_subset
#> 2_filter
#> name info: tag tag
#> 3_feat_stats
#> 4_cell_stats
#> 5_hvf
#> name info: hvf HVFplot
#> 6_pca
#> 7_umap
#> 8_tsne
#> 9_nn_network
#> 10_cluster
#> name info: leiden_clus sNN.pca
#> 11_delaunay_spatial_network
#> name info: Delaunay_network
#> 12_spatial_network
#> name info: spatial_network
#> 13_create_metafeat
#> name info: cluster_metagene
#> 14_pca
#> name info: custom_pca
#> 15_umap
#> name info: custom_pca custom_umap
#> 16_nn_network
#> name info: custom_pca custom_NN
#> 17_cluster
#> name info: custom_leiden custom_NN
#> 18_spatial_deconvolution
#> name info: DWLS
#> 19_spatial_deconvolution
#> name info: DWLS
#> 20_spatial_deconvolution
#> name info: DWLS