inst/images/human_cycif_PDAC/human_cycif_PDAC_200601.md

In RubD/Giotto_site: Spatial Single-Cell Transcriptomics Toolbox

CyCIF PDAC

• Giotto global instructions
• part 1: Data input
• part 2: Create Giotto object & process data
• part 3: dimension reduction
• part 4: cluster
• part 5: visualize spatial and expression space
• part 6: cell type marker gene detection
• part 7: cell type annotation
• 8. spatial grid
• 9. spatial network
• 12. cell-cell preferential proximity
• part X: analyses for paper

The CyCIF data to run this tutorial can be found here

Giotto global instructions

# this example was tested with Giotto v.0.3.5
library(Giotto)

## create instructions
## instructions allow us to:
## - automatically save all plots into a chosen results folder, see also showSaveParameters()
## - set your own python path if preferred (Giotto can automatically create a miniconda environment with all python modules installed)
instrs = createGiottoInstructions(save_dir = '/your/results/path/')

part 1: Data input

We will re-analyze the PDAC data generated by tissue-CyCIF as explained in the paper by Lin et al.

Pdac_data = data.table::fread('/path/to/rawdata_Figure7&8_PDAC.csv')
Pdac_data[, cell_ID := paste0('cell_', 1:nrow(Pdac_data))]

# locations
pdac_locations = Pdac_data_filter[,.(Xt, -Yt)]
# expression
pdac_expression = as.matrix(Pdac_data[,c(9:15,18:31)]); rownames(pdac_expression) = Pdac_data$cell_ID
# metadata
pdac_metadat = Pdac_data[,c(1:8,16:17,32:39)]

part 2: Create Giotto object & process data

pdac_test <- createGiottoObject(raw_exprs = t(pdac_expression), 
                                spatial_locs = pdac_locations,
                                instructions = instrs,
                                cell_metadata = pdac_metadat)

## normalize & adjust
pdac_test <- normalizeGiotto(gobject = pdac_test, scalefactor = 10000, verbose = T)
pdac_test <- addStatistics(gobject = pdac_test)

## visualize original annotations ##
spatPlot(gobject = pdac_test, point_size = 0.5, coord_fix_ratio = 1, cell_color = 'frame',  background_color = 'black', legend_symbol_size = 3)

spatPlot(gobject = pdac_test, point_size = 0.3, coord_fix_ratio = 1, cell_color = 'COL', background_color = 'black', legend_symbol_size = 3)

spatPlot(gobject = pdac_test, point_size = 0.3, coord_fix_ratio = 1, cell_color = 'ROW', background_color = 'black', legend_symbol_size = 3)

## add external histology information
pdac_metadata = pDataDT(pdac_test)
pancreas_frames = c(1:6, 27:31, 15:19, 40:44)
PDAC_frames = c(23:26, 35:37, 51:52, 64:65, 77)
small_intestines_frames = c(49:50, 63, 75:76, 88:89, 100:103, 112:116, 125:129, 137:140)

# detailed histology
hist_info = ifelse(pdac_metadata$frame %in% pancreas_frames, 'pancr', 
                   ifelse(pdac_metadata$frame %in% PDAC_frames, 'PDAC',
                          ifelse(pdac_metadata$frame %in% small_intestines_frames, 'small_intest', 'other')))
pdac_test = addCellMetadata(pdac_test, new_metadata = hist_info)

spatPlot(gobject = pdac_test, point_size = 0.3, coord_fix_ratio = 1, cell_color = 'hist_info',
         background_color = 'black', legend_symbol_size = 3)

# coarse histology
hist_info2 = ifelse(pdac_metadata$frame %in% pancreas_frames, 'pancr', 
                    ifelse(pdac_metadata$frame %in% small_intestines_frames, 'small_intest','PDAC'))
pdac_test = addCellMetadata(pdac_test, new_metadata = hist_info2)

spatPlot(gobject = pdac_test, point_size = 0.3, coord_fix_ratio = 1, cell_color = 'hist_info2',
         background_color = 'black', legend_symbol_size = 3, point_border_stroke = 0.001)

add and align image:

1. read image with magick
2. modify image optional (e.g. flip axis, negate, change background, …)
3. test if image is aligned

# read
png_path = '/path/to/CyCIF_data/PDAC/canvas.png'
mg_img = magick::image_read(png_path)

# flip/flop (convert x and y axes)
mg_img = magick::image_flip(mg_img)
mg_img = magick::image_flop(mg_img)

# negate image
mg_img2 = magick::image_negate(mg_img)


## align image ##
# 1. create spatplot
mypl = spatPlot(gobject = pdac_test, point_size = 0.3, coord_fix_ratio = NULL, cell_color = 'hist_info2',
       legend_symbol_size = 3, point_border_stroke = 0.001,
       save_plot = F, return_plot = T)
# 2.create giotto image and make adjustments (xmax_adj, xmin_adj, ...)
hist_png = createGiottoImage(gobject = pdac_test, mg_object = mg_img2, name = 'image_hist',
                                 xmax_adj = 5000, xmin_adj = 2500, ymax_adj = 1500, ymin_adj = 1500)
# 3. add giotto image to spatplot to check alignment
mypl_image = addGiottoImageToSpatPlot(mypl, hist_png)
mypl_image

1. add giotto image(s) to giotto object to call them within spat functions

## add images to Giotto object ##
image_list = list(hist_png)
pdac_test = addGiottoImage(gobject = pdac_test,
                               images = image_list)
showGiottoImageNames(pdac_test)

part 3: dimension reduction

# PCA
pdac_test <- runPCA(gobject = pdac_test, expression_values = 'normalized',
                    scale_unit = T, method = 'factominer')
signPCA(pdac_test, scale_unit = T, scree_ylim = c(0, 3))

plotPCA(gobject = pdac_test, point_shape = 'no_border', point_size = 0.2, save_param = list(save_name = '2_PCAplot'))

# UMAP
pdac_test <- runUMAP(pdac_test, dimensions_to_use = 1:14, n_components = 2, n_threads = 12)
plotUMAP(gobject = pdac_test, point_shape = 'no_border', point_size = 0.2)

part 4: cluster

## sNN network (default)
pdac_test <- createNearestNetwork(gobject = pdac_test, dimensions_to_use = 1:14, k = 20)

## 0.2 resolution
pdac_test <- doLeidenCluster(gobject = pdac_test, resolution = 0.2, n_iterations = 100, name = 'leiden')

# create customized color palette for leiden clustering results
pdac_metadata = pDataDT(pdac_test)
leiden_colors = Giotto:::getDistinctColors(length(unique(pdac_metadata$leiden)))
names(leiden_colors) = unique(pdac_metadata$leiden)
color_3 = leiden_colors['3'];color_10 = leiden_colors['10']
leiden_colors['3'] = color_10; leiden_colors['10'] = color_3

plotUMAP(gobject = pdac_test, cell_color = 'leiden', point_shape = 'no_border', point_size = 0.2, cell_color_code = leiden_colors)

plotUMAP(gobject = pdac_test, cell_color = 'hist_info',point_shape = 'no_border',   point_size = 0.2)

spatPlot(gobject = pdac_test, cell_color = 'leiden', point_shape = 'no_border', point_size = 0.2, 
         cell_color_code = leiden_colors, coord_fix_ratio = 1)

Add background image:

showGiottoImageNames(pdac_test)
spatPlot(gobject = pdac_test, show_image = T, image_name = 'image_hist',
         cell_color = 'leiden',
         point_shape = 'no_border', point_size = 0.2, point_alpha = 0.7, 
         cell_color_code = leiden_colors, coord_fix_ratio = 1)

part 5: visualize spatial and expression space

spatDimPlot2D(gobject = pdac_test, cell_color = 'leiden',
              spat_point_shape = 'no_border', spat_point_size = 0.2,
              dim_point_shape = 'no_border', dim_point_size = 0.2,
              cell_color_code = leiden_colors)

spatDimPlot2D(gobject = pdac_test, cell_color = 'leiden',
              spat_point_shape = 'border',
              spat_point_size = 0.2, spat_point_border_stroke = 0.01,
              dim_point_shape = 'border', dim_point_size = 0.2,
              dim_point_border_stroke = 0.01, cell_color_code = leiden_colors)

spatDimPlot2D(gobject = pdac_test, cell_color = 'hist_info2',
              spat_point_shape = 'border', spat_point_size = 0.2,
              spat_point_border_stroke = 0.01, dim_point_shape = 'border',
              dim_point_size = 0.2, dim_point_border_stroke = 0.01)

part 6: cell type marker gene detection

# resolution 0.2
cluster_column = 'leiden'

# gini
markers_gini = findMarkers_one_vs_all(gobject = pdac_test,
                                      method = "gini",
                                      expression_values = "scaled",
                                      cluster_column = cluster_column,
                                      min_genes = 5)
markergenes_gini = unique(markers_gini[, head(.SD, 5), by = "cluster"][["genes"]])

plotMetaDataHeatmap(pdac_test, expression_values = "norm",
                    metadata_cols = c(cluster_column),
                    selected_genes = markergenes_gini, 
                    custom_cluster_order = c(1, 10, 3, 12, 8, 2, 9, 6, 11, 13, 4, 5, 7),
                    y_text_size = 8, show_values = 'zscores_rescaled')

topgenes_gini = markers_gini[, head(.SD, 1), by = 'cluster']$genes
violinPlot(pdac_test, genes = unique(topgenes_gini), cluster_column = cluster_column,
           strip_text = 8, strip_position = 'right',
           save_param = c(save_name = '5_violinplot_gini', base_width = 5, base_height = 10))

part 7: cell type annotation

Metadata heatmap

Inspect the potential cell type markers

## all genes heatmap
plotMetaDataHeatmap(pdac_test, expression_values = "norm", metadata_cols = 'leiden', 
                    custom_cluster_order = c(1, 10, 3, 12, 8, 2, 9, 6, 11, 13, 4, 5, 7),
                    y_text_size = 8, show_values = 'zscores_rescaled')

Inspect the potential cell type markers stratified by tissue location

plotMetaDataHeatmap(pdac_test, expression_values = "norm", metadata_cols = c('leiden','hist_info2'), 
                    first_meta_col = 'leiden', second_meta_col = 'hist_info2',
                    y_text_size = 8, show_values = 'zscores_rescaled')

Spatial subsets

Inspect subsets of the data based on tissue location

spatPlot(pdac_test, cell_color = 'leiden', cell_color_code = leiden_colors,
         point_shape = 'no_border', point_size = 0.75, group_by = 'hist_info2')

spatPlot(pdac_test, cell_color = 'leiden', cell_color_code = leiden_colors,
         point_shape = 'no_border', point_size = 0.3,
         group_by = 'hist_info2', group_by_subset = c('pancr'), cow_n_col = 1)

spatPlot(pdac_test, cell_color = 'leiden', cell_color_code = leiden_colors,
         point_shape = 'no_border', point_size = 0.3,
         group_by = 'hist_info2', group_by_subset = c('PDAC'), cow_n_col = 1)

spatPlot(pdac_test, cell_color = 'leiden', cell_color_code = leiden_colors,
         point_shape = 'no_border', point_size = 0.3,
         group_by = 'hist_info2', group_by_subset = c('small_intest'), cow_n_col = 1)

Spatial distribution of clusters

Visually inspect the spatial distribution of different clusters

# spatial enrichment of groups
for(group in unique(pDataDT(pdac_test)$leiden)) {
  spatPlot(pdac_test, cell_color = 'leiden', point_shape = 'no_border',
           point_size = 0.3, other_point_size = 0.1,
           select_cell_groups = group, cell_color_code = 'red')
}

Here we just show a small subset:

Annotate clusters based on spatial position and dominant expression patterns

cell_metadata =  pDataDT(pdac_test)
cluster_data = cell_metadata[, .N, by = c('leiden', 'hist_info2')]
cluster_data[, fraction:= round(N/sum(N), 2), by = c('leiden')]
setorder(cluster_data, leiden, hist_info2, fraction)

# final annotation
names = 1:13
location = c('pancr', 'intest', 'general', 'intest', 'pancr',
             'intest', 'pancr', 'canc', 'general', 'pancr',
             'general', 'pancr', 'intest')
feats = c('epithelial_I', 'fibroblast_VEGFR+', 'stroma_HER2+_pERK+', 'epithelial_lining_p21+', 'epithelial_keratin',
          'epithelial_prolif', 'epithelial_actin++', 'immune_PD-L1+', 'stromal_actin-', 'epithelial_tx_active',
          'epithelial_MET+_EGFR+', 'immune_CD45+', 'epithelial_pAKT')

annot_dt = data.table('names' = names, 'location' = location, 'feats' = feats)
annot_dt[, annotname := paste0(location,'_',feats)]
cell_annot = annot_dt$annotname;names(cell_annot) = annot_dt$names
pdac_test = annotateGiotto(pdac_test, annotation_vector = cell_annot, cluster_column = 'leiden')


# specify colors
leiden_colors
leiden_names = annot_dt$annotname; names(leiden_names) = annot_dt$names

cell_annot_colors = leiden_colors
names(cell_annot_colors) = leiden_names[names(leiden_colors)]

# covisual
spatDimPlot(gobject = pdac_test, cell_color = 'cell_types', cell_color_code = cell_annot_colors,
            spat_point_shape = 'border', spat_point_size = 0.2, spat_point_border_stroke = 0.01,
            dim_point_shape = 'border', dim_point_size = 0.2, dim_point_border_stroke = 0.01,
            dim_show_center_label = F, spat_show_legend = T, dim_show_legend = T, legend_symbol_size = 3)

# spatial only
spatPlot(gobject = pdac_test, cell_color = 'cell_types', point_shape = 'no_border', point_size = 0.2, 
         coord_fix_ratio = 1, show_legend = T, cell_color_code = cell_annot_colors, background_color = 'black')

# dimension only
plotUMAP(gobject = pdac_test, cell_color = 'cell_types', point_shape = 'no_border', point_size = 0.2, 
         show_legend = T, cell_color_code = cell_annot_colors, show_center_label = F, background_color = 'black')

8. spatial grid

pdac_test <- createSpatialGrid(gobject = pdac_test,
                               sdimx_stepsize = 150,
                               sdimy_stepsize = 150,
                               minimum_padding = 0)
spatPlot(pdac_test, 
         cell_color = 'leiden', 
         show_grid = T, point_size = 0.75, point_shape = 'no_border',
         grid_color = 'red', spatial_grid_name = 'spatial_grid')

9. spatial network

pdac_test <- createSpatialNetwork(gobject = pdac_test, minimum_k = 2)

12. cell-cell preferential proximity

## calculate frequently seen proximities
cell_proximities = cellProximityEnrichment(gobject = pdac_test,
                                           cluster_column = 'cell_types',
                                           spatial_network_name = 'Delaunay_network',
                                           number_of_simulations = 200)
## barplot
cellProximityBarplot(gobject = pdac_test, CPscore = cell_proximities, min_orig_ints = 5, min_sim_ints = 5)

## network
cellProximityNetwork(gobject = pdac_test, CPscore = cell_proximities,
                     remove_self_edges = T, only_show_enrichment_edges = F)

## visualization
spec_interaction = "1--5"
cellProximitySpatPlot2D(gobject = pdac_test, point_select_border_stroke = 0,
                        interaction_name = spec_interaction,
                        cluster_column = 'leiden', show_network = T,
                        cell_color = 'leiden', coord_fix_ratio = NULL,
                        point_size_select = 0.3, point_size_other = 0.1)

part X: analyses for paper

Heatmap for cell type annotation:

cell_type_order_pdac = c("pancr_epithelial_actin++", "pancr_epithelial_I",
                         "intest_epithelial_lining_p21+", "pancr_epithelial_keratin", 
                         "intest_epithelial_prolif" ,"general_epithelial_MET+_EGFR+",
                         "intest_epithelial_pAKT", "pancr_epithelial_tx_active",
                         "canc_immune_PD-L1+","general_stromal_actin-",
                         "pancr_immune_CD45+", "intest_fibroblast_VEGFR+",
                         "general_stroma_HER2+_pERK+")

plotMetaDataHeatmap(pdac_test, expression_values = "scaled", metadata_cols = c('cell_types'), 
                    custom_cluster_order = cell_type_order_pdac,
                    y_text_size = 8, show_values = 'zscores_rescaled')

Pancreas

Highlight region in pancreas:

## pancreas region ##
my_pancreas_Ids = pdac_metadata[frame == 17][['cell_ID']]
my_pancreas_giotto = subsetGiotto(pdac_test, cell_ids = my_pancreas_Ids)

spatPlot(my_pancreas_giotto, cell_color = 'leiden', point_shape = 'no_border', point_size = 1, cell_color_code = leiden_colors)

spatGenePlot(my_pancreas_giotto, expression_values = 'scaled', point_border_stroke = 0.01,
             genes = c('E_Cadherin','Catenin', 'Vimentin',  'VEGFR'), point_size = 1)

plotUMAP(my_pancreas_giotto, cell_color = 'leiden', point_shape = 'no_border', point_size = 0.5, 
         cell_color_code = leiden_colors, show_center_label = F)

dimGenePlot(my_pancreas_giotto, expression_values = 'scaled', point_border_stroke = 0.01,
            genes = c('E_Cadherin','Catenin', 'Vimentin',  'VEGFR'), point_size = 1)

Small intestines

Highlight region in small intestines (same as in original paper):

## intestine region ##
my_intest_Ids = pdac_metadata[frame == 115][['cell_ID']]
my_intest_giotto = subsetGiotto(pdac_test, cell_ids = my_intest_Ids)

spatPlot(my_intest_giotto, cell_color = 'leiden', point_shape = 'no_border', point_size = 1, cell_color_code = leiden_colors)

spatGenePlot(my_intest_giotto, expression_values = 'scaled', point_border_stroke = 0.01,
             genes = c('PCNA','Catenin', 'Ki67',  'pERK'), point_size = 1)

plotUMAP(my_intest_giotto, cell_color = 'leiden', point_shape = 'no_border', point_size = 0.5, 
         cell_color_code = leiden_colors, show_center_label = F)

dimGenePlot(my_intest_giotto, expression_values = 'scaled', point_border_stroke = 0.01,
            genes = c('PCNA','Catenin', 'Ki67',  'pERK'), point_size = 1)

RubD/Giotto_site documentation built on April 12, 2025, 6:46 p.m.