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