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
#> checking default envname 'giotto_env'
#> a system default python environment was found
#> Using python path:
#> "/usr/share/miniconda/envs/giotto_env/bin/python"
objHistory(g)
#> Steps and parameters used:
#> c(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")c(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")c(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")c(gobject = "mini_visium", expression_values = "normalized", detection_threshold = "0", return_gobject = "TRUE", verbose = "TRUE")c(gobject = "mini_visium", expression_values = "normalized", detection_threshold = "0", return_gobject = "TRUE", verbose = "TRUE")c(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")c(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", ... = "")c(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", ... = "")c(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", ... = "")c(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", ... = "")c(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")c(`dimensions used` = "dimensions: sdimx and sdimy", method = "deldir", `maximum distance threshold` = "auto", `name of spatial network` = "Delaunay_network")c(`k neighbours` = "10", `dimensions used` = "all", `maximum distance threshold` = "400", `name of spatial network` = "spatial_network")c(gobject = "mini_visium", expression_values = "normalized", feat_clusters = "cluster_genes", stat = "mean", name = "cluster_metagene", return_gobject = "TRUE")c(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", ... = "")c(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", ... = "")c(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", ... = "")c(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")c(`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")c(`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")c(`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")