To run this vignette you need to install all the necessary Python modules.
This can be done manually, see https://rubd.github.io/Giotto_site/articles/installation_issues.html#python-manual-installation
This can be done within R using our installation tools (installGiottoEnvironment), see https://rubd.github.io/Giotto_site/articles/tut0_giotto_environment.html for more information.
# to automatically save figures in save_dir set save_plot to TRUE
temp_dir = '~/Temp/'
myinstructions = createGiottoInstructions(save_dir = temp_dir,
save_plot = FALSE,
show_plot = F)minimum requirements:
- matrix with expression information (or path to)
- x,y(,z) coordinates for cells or spots (or path to)
# giotto object
expr_path = system.file("extdata", "visium_DG_expr.txt.gz", package = 'Giotto')
loc_path = system.file("extdata", "visium_DG_locs.txt", package = 'Giotto')
mini_visium <- createGiottoObject(raw_exprs = expr_path,
spatial_locs = loc_path,
instructions = myinstructions)How to work with Giotto instructions that are part of your Giotto
object:
- show the instructions associated with your Giotto object with
showGiottoInstructions
- change one or more instructions with
changeGiottoInstructions
- replace all instructions at once with
replaceGiottoInstructions
- read or get a specific giotto instruction with
readGiottoInstructions
Of note, the python path can only be set once in an R session. See the
reticulate package for more information.
# show instructions associated with giotto object (mini_visium)
showGiottoInstructions(mini_visium)
## 1. read image
png_path = system.file("extdata", "deg_image.png", package = 'Giotto')
mg_img = magick::image_read(png_path)
## 2. test and modify image alignment
mypl = spatPlot(mini_visium, return_plot = T, point_alpha = 0.8)
orig_png = createGiottoImage(gobject = mini_visium, mg_object = mg_img, name = 'image',
xmax_adj = 450, xmin_adj = 550,
ymax_adj = 200, ymin_adj = 200)
mypl_image = addGiottoImageToSpatPlot(mypl, orig_png)
mypl_image
## 3. add images to Giotto object ##
image_list = list(orig_png)
mini_visium = addGiottoImage(gobject = mini_visium,
images = image_list)
showGiottoImageNames(mini_visium)
# explore gene and cell distribution
filterDistributions(mini_visium, detection = 'genes')
filterDistributions(mini_visium, detection = 'cells')
filterCombinations(mini_visium,
expression_thresholds = c(1),
gene_det_in_min_cells = c(20, 20, 50, 50),
min_det_genes_per_cell = c(100, 200, 100, 200))
# filter and normalize
mini_visium <- filterGiotto(gobject = mini_visium,
expression_threshold = 1,
gene_det_in_min_cells = 50,
min_det_genes_per_cell = 100,
expression_values = c('raw'),
verbose = T)
mini_visium <- normalizeGiotto(gobject = mini_visium, scalefactor = 6000, verbose = T)
mini_visium <- addStatistics(gobject = mini_visium)
mini_visium <- calculateHVG(gobject = mini_visium)
mini_visium <- runPCA(gobject = mini_visium)
screePlot(mini_visium, ncp = 30)
plotPCA(gobject = mini_visium)
mini_visium <- runUMAP(mini_visium, dimensions_to_use = 1:10)
plotUMAP(gobject = mini_visium)
mini_visium <- runtSNE(mini_visium, dimensions_to_use = 1:10)
plotTSNE(gobject = mini_visium)
mini_visium <- createNearestNetwork(gobject = mini_visium, dimensions_to_use = 1:10, k = 15)
mini_visium <- doLeidenCluster(gobject = mini_visium, resolution = 0.4, n_iterations = 1000)
pDataDT(mini_visium)
# visualize UMAP cluster results
plotUMAP(gobject = mini_visium, cell_color = 'leiden_clus', show_NN_network = T, point_size = 2.5)
# visualize UMAP and spatial results
spatDimPlot(gobject = mini_visium, cell_color = 'leiden_clus', spat_point_shape = 'voronoi')
# heatmap and dendrogram
showClusterHeatmap(gobject = mini_visium, cluster_column = 'leiden_clus')
showClusterDendrogram(mini_visium, h = 0.5, rotate = T, cluster_column = 'leiden_clus')
scran_markers = findMarkers_one_vs_all(gobject = mini_visium,
method = 'scran',
expression_values = 'normalized',
cluster_column = 'leiden_clus')
# violinplot
topgenes_scran = scran_markers[, head(.SD, 2), by = 'cluster']$genes
violinPlot(mini_visium, genes = topgenes_scran, cluster_column = 'leiden_clus',
strip_text = 10, strip_position = 'right')
# metadata heatmap
topgenes_scran = scran_markers[, head(.SD, 6), by = 'cluster']$genes
plotMetaDataHeatmap(mini_visium, selected_genes = topgenes_scran,
metadata_cols = c('leiden_clus'))
clusters_cell_types = c('Gfap_cells', 'Tbr1_cells', 'Tcf7l2_cells', 'Wfs1_cells', 'Nptxr_cells')
names(clusters_cell_types) = 1:5
mini_visium = annotateGiotto(gobject = mini_visium, annotation_vector = clusters_cell_types,
cluster_column = 'leiden_clus', name = 'cell_types')
# check new cell metadata
pDataDT(mini_visium)
# visualize annotations
spatDimPlot(gobject = mini_visium, cell_color = 'cell_types', spat_point_size = 3, dim_point_size = 3)Here we will use known markers for different mouse brain cell types to identify which cell types are enriched in the individual spots or identified clusters.
Paper: eisel, A. et al. Molecular Architecture of the Mouse Nervous System. Cell 174, 999-1014.e22 (2018).
## cell type signatures ##
## combination of all marker genes identified in Zeisel et al
sign_matrix_path = system.file("extdata", "sig_matrix.txt", package = 'Giotto')
brain_sc_markers = data.table::fread(sign_matrix_path) # file don't exist in data folder
sig_matrix = as.matrix(brain_sc_markers[,-1]); rownames(sig_matrix) = brain_sc_markers$Event
## enrichment tests
mini_visium = runSpatialEnrich(mini_visium,
sign_matrix = sig_matrix,
enrich_method = 'PAGE') #default = 'PAGE'
## heatmap of enrichment versus annotation (e.g. clustering result)
cell_types = colnames(sig_matrix)
plotMetaDataCellsHeatmap(gobject = mini_visium,
metadata_cols = 'leiden_clus',
value_cols = cell_types,
spat_enr_names = 'PAGE',
x_text_size = 8, y_text_size = 8)
enrichment_results = mini_visium@spatial_enrichment$PAGE
enrich_cell_types = colnames(enrichment_results)
enrich_cell_types = enrich_cell_types[enrich_cell_types != 'cell_ID']
## spatplot
spatCellPlot(gobject = mini_visium, spat_enr_names = 'PAGE',
cell_annotation_values = enrich_cell_types,
cow_n_col = 3,coord_fix_ratio = NULL, point_size = 1)Create a grid based on defined stepsizes in the x,y(,z) axes.
mini_visium <- createSpatialGrid(gobject = mini_visium,
sdimx_stepsize = 300,
sdimy_stepsize = 300,
minimum_padding = 50)
showGrids(mini_visium)
spatPlot(gobject = mini_visium, show_grid = T, point_size = 1.5)
# extract grid and associated metadata spots
annotated_grid = annotateSpatialGrid(mini_visium)
annotated_grid_metadata = annotateSpatialGrid(mini_visium,
cluster_columns = c('leiden_clus', 'cell_types', 'nr_genes'))
plotStatDelaunayNetwork(gobject = mini_visium, maximum_distance = 300)
mini_visium = createSpatialNetwork(gobject = mini_visium, minimum_k = 2, maximum_distance_delaunay = 400)
mini_visium = createSpatialNetwork(gobject = mini_visium, minimum_k = 2, method = 'kNN', k = 10)
showNetworks(mini_visium)
# visualize the two different spatial networks
spatPlot(gobject = mini_visium, show_network = T,
network_color = 'blue', spatial_network_name = 'Delaunay_network',
point_size = 2.5, cell_color = 'leiden_clus')
spatPlot(gobject = mini_visium, show_network = T,
network_color = 'blue', spatial_network_name = 'kNN_network',
point_size = 2.5, cell_color = 'leiden_clus')Identify spatial genes with 3 different methods:
- binSpect with kmeans binarization (default)
- binSpect with rank binarization
- silhouetteRank
Visualize top 4 genes per method.
km_spatialgenes = binSpect(mini_visium)
spatGenePlot(mini_visium, expression_values = 'scaled',
genes = km_spatialgenes[1:4]$genes,
point_shape = 'border', point_border_stroke = 0.1,
show_network = F, network_color = 'lightgrey', point_size = 2.5,
cow_n_col = 2)
rank_spatialgenes = binSpect(mini_visium, bin_method = 'rank')
spatGenePlot(mini_visium, expression_values = 'scaled',
genes = rank_spatialgenes[1:4]$genes,
point_shape = 'border', point_border_stroke = 0.1,
show_network = F, network_color = 'lightgrey', point_size = 2.5,
cow_n_col = 2)
silh_spatialgenes = silhouetteRank(gobject = mini_visium) # TODO: suppress print output
spatGenePlot(mini_visium, expression_values = 'scaled',
genes = silh_spatialgenes[1:4]$genes,
point_shape = 'border', point_border_stroke = 0.1,
show_network = F, network_color = 'lightgrey', point_size = 2.5,
cow_n_col = 2)Identify robust spatial co-expression patterns using the spatial
network or grid and a subset of individual spatial genes.
1. calculate spatial correlation scores
2. cluster correlation scores
# 1. calculate spatial correlation scores
ext_spatial_genes = km_spatialgenes[1:100]$genes
spat_cor_netw_DT = detectSpatialCorGenes(mini_visium,
method = 'network', spatial_network_name = 'Delaunay_network',
subset_genes = ext_spatial_genes)
# 2. cluster correlation scores
spat_cor_netw_DT = clusterSpatialCorGenes(spat_cor_netw_DT, name = 'spat_netw_clus', k = 8)
heatmSpatialCorGenes(mini_visium, spatCorObject = spat_cor_netw_DT, use_clus_name = 'spat_netw_clus')
netw_ranks = rankSpatialCorGroups(mini_visium, spatCorObject = spat_cor_netw_DT, use_clus_name = 'spat_netw_clus')
top_netw_spat_cluster = showSpatialCorGenes(spat_cor_netw_DT, use_clus_name = 'spat_netw_clus',
selected_clusters = 6, show_top_genes = 1)
cluster_genes_DT = showSpatialCorGenes(spat_cor_netw_DT, use_clus_name = 'spat_netw_clus', show_top_genes = 1)
cluster_genes = cluster_genes_DT$clus; names(cluster_genes) = cluster_genes_DT$gene_ID
mini_visium = createMetagenes(mini_visium, gene_clusters = cluster_genes, name = 'cluster_metagene')
spatCellPlot(mini_visium,
spat_enr_names = 'cluster_metagene',
cell_annotation_values = netw_ranks$clusters,
point_size = 1.5, cow_n_col = 3)
hmrf_folder = paste0(temp_dir,'/','11_HMRF/')
if(!file.exists(hmrf_folder)) dir.create(hmrf_folder, recursive = T)
# perform hmrf
my_spatial_genes = km_spatialgenes[1:100]$genes
HMRF_spatial_genes = doHMRF(gobject = mini_visium,
expression_values = 'scaled',
spatial_genes = my_spatial_genes,
spatial_network_name = 'Delaunay_network',
k = 8,
betas = c(28,2,2),
output_folder = paste0(hmrf_folder, '/', 'Spatial_genes_brain/SG_top100_k8_scaled'))
# check and select hmrf
for(i in seq(28, 30, by = 2)) {
viewHMRFresults2D(gobject = mini_visium,
HMRFoutput = HMRF_spatial_genes,
k = 8, betas_to_view = i,
point_size = 2)
}
mini_visium = addHMRF(gobject = mini_visium,
HMRFoutput = HMRF_spatial_genes,
k = 8, betas_to_add = c(28),
hmrf_name = 'HMRF')
giotto_colors = getDistinctColors(8)
names(giotto_colors) = 1:8
spatPlot(gobject = mini_visium, cell_color = 'HMRF_k8_b.28',
point_size = 3, coord_fix_ratio = 1, cell_color_code = giotto_colors)
viewer_folder = paste0(temp_dir, '/', 'Mouse_cortex_viewer')
# select annotations, reductions and expression values to view in Giotto Viewer
exportGiottoViewer(gobject = mini_visium, output_directory = viewer_folder,
factor_annotations = c('cell_types',
'leiden_clus',
'HMRF_k8_b.28'),
numeric_annotations = 'total_expr',
dim_reductions = c('umap'),
dim_reduction_names = c('umap'),
expression_values = 'scaled',
expression_rounding = 3,
overwrite_dir = T)