STARmap mouse cortex
Source:vignettes/mouse_starmap_cortex_200917.Rmd
mouse_starmap_cortex_200917.Rmd#> Warning: This tutorial was written with Giotto version 0.3.6.9042, your version
#> is 1.0.3.This is a more recent version and results should be reproduciblelibrary(Giotto) # 1. set working directory results_folder = '/path/to/directory/' # 2. set giotto python path # set python path to your preferred python version path # set python path to NULL if you want to automatically install (only the 1st time) and use the giotto miniconda environment python_path = NULL if(is.null(python_path)) { installGiottoEnvironment() }
Dataset explanation
Wang et al. created a 3D spatial expression dataset consisting of 28 genes from 32,845 single cells in a visual cortex volume using the STARmap technology.
The STARmap data to run this tutorial can be found here. Alternatively you can use the getSpatialDataset to automatically download this dataset like we do in this example.
Dataset download
# download data to working directory # if wget is installed, set method = 'wget' getSpatialDataset(dataset = 'starmap_3D_cortex', directory = results_folder, method = 'wget')
Part 1: Giotto global instructions and preparations
## instructions allow us to automatically save all plots into a chosen results folder instrs = createGiottoInstructions(show_plot = FALSE, save_plot = TRUE, save_dir = results_folder, python_path = python_path) expr_path = paste0(results_folder, "STARmap_3D_data_expression.txt") loc_path = paste0(results_folder, "STARmap_3D_data_cell_locations.txt")
part 2: Create Giotto object & process data
## create STAR_test <- createGiottoObject(raw_exprs = expr_path, spatial_locs = loc_path, instructions = instrs) ## filter raw data # pre-test filter parameters filterDistributions(STAR_test, detection = 'genes', save_param = list(save_name = '2_a_distribution_genes'))
filterDistributions(STAR_test, detection = 'cells', save_param = list(save_name = '2_b_distribution_cells'))
filterCombinations(STAR_test, expression_thresholds = c(1, 1,2), gene_det_in_min_cells = c(20000, 20000, 30000), min_det_genes_per_cell = c(10, 20, 25), save_param = list(save_name = '2_c_distribution_filters'))
# filter STAR_test <- filterGiotto(gobject = STAR_test, gene_det_in_min_cells = 20000, min_det_genes_per_cell = 20) ## normalize STAR_test <- normalizeGiotto(gobject = STAR_test, scalefactor = 10000, verbose = T) STAR_test <- addStatistics(gobject = STAR_test) STAR_test <- adjustGiottoMatrix(gobject = STAR_test, expression_values = c('normalized'), batch_columns = NULL, covariate_columns = c('nr_genes', 'total_expr'), return_gobject = TRUE, update_slot = c('custom')) ## visualize # 3D spatPlot3D(gobject = STAR_test, point_size = 2, save_param = list(save_name = '2_d_spatplot_3D'))
part 3: dimension reduction
STAR_test <- calculateHVG(gobject = STAR_test, method = 'cov_groups', zscore_threshold = 0.5, nr_expression_groups = 3, save_param = list(save_name = '3_a_HVGplot', base_height = 5, base_width = 5))
# too few highly variable genes # genes_to_use = NULL is the default and will use all genes available STAR_test <- runPCA(gobject = STAR_test, genes_to_use = NULL, scale_unit = F,method = 'factominer') signPCA(STAR_test, save_param = list(save_name = '3_b_signPCs'))
STAR_test <- runUMAP(STAR_test, dimensions_to_use = 1:8, n_components = 3, n_threads = 4) plotUMAP_3D(gobject = STAR_test, save_param = list(save_name = '3_c_UMAP'))
part 4: cluster
## sNN network (default) STAR_test <- createNearestNetwork(gobject = STAR_test, dimensions_to_use = 1:8, k = 15) ## Leiden clustering STAR_test <- doLeidenCluster(gobject = STAR_test, resolution = 0.2, n_iterations = 100, name = 'leiden_0.2') plotUMAP_3D(gobject = STAR_test, cell_color = 'leiden_0.2',show_center_label = F, save_param = list(save_name = '4_a_UMAP'))
part 5: co-visualize
spatDimPlot3D(gobject = STAR_test, cell_color = 'leiden_0.2', save_param = list(save_name = '5_a_spatDimPlot'))
part 6: differential expression
markers = findMarkers_one_vs_all(gobject = STAR_test, method = 'gini', expression_values = 'normalized', cluster_column = 'leiden_0.2', min_expr_gini_score = 2, min_det_gini_score = 2, min_genes = 5, rank_score = 2) markers[, head(.SD, 2), by = 'cluster'] # violinplot violinPlot(STAR_test, genes = unique(markers$genes), cluster_column = 'leiden_0.2', strip_position = "right", save_param = list(save_name = '6_a_violinplot'))
# cluster heatmap plotMetaDataHeatmap(STAR_test, expression_values = 'scaled', metadata_cols = c('leiden_0.2'), save_param = list(save_name = '6_b_metaheatmap'))
part 7: cell-type annotation
## general cell types clusters_cell_types_cortex = c('excit','excit','excit', 'inh', 'excit', 'other', 'other', 'other', 'inh', 'inh') names(clusters_cell_types_cortex) = c(1:10) STAR_test = annotateGiotto(gobject = STAR_test, annotation_vector = clusters_cell_types_cortex, cluster_column = 'leiden_0.2', name = 'general_cell_types') plotMetaDataHeatmap(STAR_test, expression_values = 'scaled', metadata_cols = c('general_cell_types'), save_param = list(save_name = '7_a_metaheatmap'))
## detailed cell types clusters_cell_types_cortex = c('L5','L4','L2/3', 'PV', 'L6', 'Astro', 'Olig1', 'Olig2', 'Calretinin', 'SST') names(clusters_cell_types_cortex) = c(1:10) STAR_test = annotateGiotto(gobject = STAR_test, annotation_vector = clusters_cell_types_cortex, cluster_column = 'leiden_0.2', name = 'cell_types') plotUMAP_3D(STAR_test, cell_color = 'cell_types', point_size = 1.5,show_center_label = F, save_param = list(save_name = '7_b_UMAP'))
plotMetaDataHeatmap(STAR_test, expression_values = 'scaled', metadata_cols = c('cell_types'), custom_cluster_order = c("Calretinin", "SST", "L4", "L2/3", "PV", "L5", "L6", "Astro", "Olig2", "Olig1"), save_param = list(save_name = '7_c_metaheatmap'))
part 8: co-visualize cell types
# create consistent color code mynames = unique(pDataDT(STAR_test)$cell_types) mycolorcode = Giotto:::getDistinctColors(n = length(mynames)) names(mycolorcode) = mynames spatDimPlot3D(gobject = STAR_test, cell_color = 'cell_types',show_center_label = F, save_param = list(save_name = '8_a_spatdimplot'))
part 9: visualize gene expression
dimGenePlot3D(STAR_test, expression_values = 'scaled', genes = "Rorb", genes_high_color = 'red', genes_mid_color = 'white', genes_low_color = 'darkblue', save_param = list(save_name = '9_a_dimGenePlot'))
spatGenePlot3D(STAR_test, expression_values = 'scaled', genes = "Rorb", show_other_cells = F, genes_high_color = 'red', genes_mid_color = 'white', genes_low_color = 'darkblue', save_param = list(save_name = '9_b_spatGenePlot'))
dimGenePlot3D(STAR_test, expression_values = 'scaled', genes = "Pcp4", genes_high_color = 'red', genes_mid_color = 'white', genes_low_color = 'darkblue', save_param = list(save_name = '9_c_dimGenePlot'))
spatGenePlot3D(STAR_test, expression_values = 'scaled', genes = "Pcp4", show_other_cells = F, genes_high_color = 'red', genes_mid_color = 'white', genes_low_color = 'darkblue', save_param = list(save_name = '9_d_spatGenePlot'))
dimGenePlot3D(STAR_test, expression_values = 'scaled', genes = "Cux2", genes_high_color = 'red', genes_mid_color = 'white', genes_low_color = 'darkblue', save_param = list(save_name = '9_e_dimGenePlot'))
spatGenePlot3D(STAR_test, expression_values = 'scaled', genes = "Cux2", show_other_cells = F, genes_high_color = 'red', genes_mid_color = 'white', genes_low_color = 'darkblue', save_param = list(save_name = '9_f_spatGenePlot'))
dimGenePlot3D(STAR_test, expression_values = 'scaled', genes = "Ctgf", genes_high_color = 'red', genes_mid_color = 'white', genes_low_color = 'darkblue', save_param = list(save_name = '9_g_dimGenePlot'))
spatGenePlot3D(STAR_test, expression_values = 'scaled', genes = "Ctgf", show_other_cells = F, genes_high_color = 'red', genes_mid_color = 'white', genes_low_color = 'darkblue', save_param = list(save_name = '9_h_spatGenePlot'))
part 10: virtual cross section
STAR_test <- createSpatialNetwork(gobject = STAR_test, delaunay_method = 'delaunayn_geometry') STAR_test = createCrossSection(STAR_test,method="equation", equation=c(0,1,0,600), extend_ratio = 0.6)
insertCrossSectionSpatPlot3D(STAR_test, cell_color = 'cell_types', axis_scale = 'cube', point_size = 2, cell_color_code = mycolorcode)
insertCrossSectionGenePlot3D(STAR_test, expression_values = 'scaled', axis_scale = "cube", genes = "Slc17a7")
crossSectionPlot(STAR_test, point_size = 2, point_shape = "border", cell_color = "cell_types",cell_color_code = mycolorcode, save_param = list(save_name = '10_a_crossSectionPlot'))
crossSectionPlot3D(STAR_test, point_size = 2, cell_color = "cell_types", cell_color_code = mycolorcode,axis_scale = "cube", save_param = list(save_name = '10_b_crossSectionPlot3D'))
crossSectionGenePlot(STAR_test, genes = "Slc17a7", point_size = 2,point_shape = "border", cow_n_col = 1.5, expression_values = 'scaled', save_param = list(save_name = '10_c_crossSectionGenePlot'))
crossSectionGenePlot3D(STAR_test, point_size = 2, genes = c("Slc17a7"), expression_values = 'scaled', save_param = list(save_name = '10_d_crossSectionGenePlot3D'))
