mini seqFISH

library(Giotto)

Install Python Modules

To run this vignette you need to install all of the necessary Python modules.

Important

Python module installation can be done either automatically via our installation tool (from within R) (see step 2.2A) or manually (see step 2.2B).

See Part 2.2 Giotto-Specific Python Packages of our Giotto Installation section for step-by-step instructions.

Optional: Set Giotto Instructions

# to automatically save figures in save_dir set save_plot to TRUE
temp_dir = getwd()
temp_dir = '~/Temp/'
myinstructions = createGiottoInstructions(save_dir = temp_dir,
                      save_plot = TRUE,
                      show_plot = FALSE)

1. Giotto Object

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", "seqfish_field_expr.txt.gz", package = 'Giotto')
loc_path = system.file("extdata", "seqfish_field_locs.txt", package = 'Giotto')
seqfish_mini <- 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

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 (seqfish_mini)
showGiottoInstructions(seqfish_mini)

2. Processing Steps

  • Filter genes and cells based on detection frequencies

  • Normalize expression matrix (log transformation, scaling factor and/or z-scores)

  • Add cell and gene statistics (optional)

  • Adjust expression matrix for technical covariates or batches (optional). These results will be stored in the custom slot.

seqfish_mini <- filterGiotto(gobject = seqfish_mini,
             expression_threshold = 0.5,
             gene_det_in_min_cells = 20,
             min_det_genes_per_cell = 0)
seqfish_mini <- normalizeGiotto(gobject = seqfish_mini, scalefactor = 6000, verbose = T)
seqfish_mini <- addStatistics(gobject = seqfish_mini)
seqfish_mini <- adjustGiottoMatrix(gobject = seqfish_mini,
               expression_values = c('normalized'),
               covariate_columns = c('nr_genes', 'total_expr'))

3. Dimension Reduction

  • Identify highly variable genes (HVG)

  • Perform PCA

  • Identify number of significant prinicipal components (PCs)

  • Run UMAP and/or TSNE on PCs (or directly on matrix)

seqfish_mini <- calculateHVG(gobject = seqfish_mini)
3.1_HVGplot 
seqfish_mini <- runPCA(gobject = seqfish_mini)
screePlot(seqfish_mini, ncp = 20)
3.2_screePlot.png 
jackstrawPlot(seqfish_mini, ncp = 20)
3.3_jackstrawPlot.png 
plotPCA(seqfish_mini)
3.4_PCA.png 
seqfish_mini <- runUMAP(seqfish_mini, dimensions_to_use = 1:5, n_threads = 2)
plotUMAP(gobject = seqfish_mini)

seqfish_mini <- runtSNE(seqfish_mini, dimensions_to_use = 1:5)
plotTSNE(gobject = seqfish_mini)
3.5_tSNE.png

4. Clustering

  • Create a shared (default) nearest network in PCA space (or directly on matrix)

  • Cluster on nearest network with Leiden or Louvan (kmeans and hclust are alternatives)

seqfish_mini <- createNearestNetwork(gobject = seqfish_mini, dimensions_to_use = 1:5, k = 5)
seqfish_mini <- doLeidenCluster(gobject = seqfish_mini, resolution = 0.4, n_iterations = 1000)

# visualize UMAP cluster results
plotUMAP(gobject = seqfish_mini, cell_color = 'leiden_clus',
    show_NN_network = T, point_size = 2.5)
4.1_UMAP.png 
# visualize UMAP and spatial results
spatDimPlot(gobject = seqfish_mini, cell_color = 'leiden_clus', spat_point_shape = 'voronoi')

# heatmap and dendrogram
showClusterHeatmap(gobject = seqfish_mini, cluster_column = 'leiden_clus')
4.2_showClusterHeatmap.png 
showClusterDendrogram(seqfish_mini, h = 0.5, rotate = T, cluster_column = 'leiden_clus')
4.3_showClusterDendrogram.png

5. Differential Expression

gini_markers = findMarkers_one_vs_all(gobject = seqfish_mini,
                      method = 'gini',
                      expression_values = 'normalized',
                      cluster_column = 'leiden_clus',
                      min_genes = 20,
                      min_expr_gini_score = 0.5,
                      min_det_gini_score = 0.5)

# get top 2 genes per cluster and visualize with violinplot
topgenes_gini = gini_markers[, head(.SD, 2), by = 'cluster']
violinPlot(seqfish_mini, genes = topgenes_gini$genes, cluster_column = 'leiden_clus')
5.1_violinPlot.png 
# get top 6 genes per cluster and visualize with heatmap
topgenes_gini2 = gini_markers[, head(.SD, 6), by = 'cluster']
plotMetaDataHeatmap(seqfish_mini, selected_genes = topgenes_gini2$genes,
        metadata_cols = c('leiden_clus'))
5.2_plotMetaDataHeatmap.png

6. Cell Type Annotation

clusters_cell_types = c('cell A', 'cell B', 'cell C', 'cell D',
        'cell E', 'cell F', 'cell G')
names(clusters_cell_types) = 1:7
seqfish_mini = annotateGiotto(gobject = seqfish_mini,
              annotation_vector = clusters_cell_types,
              cluster_column = 'leiden_clus',
              name = 'cell_types')
# check new cell metadata
pDataDT(seqfish_mini)

# visualize annotations
spatDimPlot(gobject = seqfish_mini, cell_color = 'cell_types',
    spat_point_size = 3, dim_point_size = 3)
6.1_spatDimPlot2D.png

7. Spatial Grid

Create a grid based on defined stepsizes in the x,y(,z) axes.

seqfish_mini <- createSpatialGrid(gobject = seqfish_mini,
              sdimx_stepsize = 300,
              sdimy_stepsize = 300,
              minimum_padding = 50)
showGrids(seqfish_mini)

# visualize grid
spatPlot(gobject = seqfish_mini, show_grid = T, point_size = 1.5)

8. Spatial Network

  • Visualize information about the default Delaunay network

  • Create a spatial Delaunay network (default)

  • Create a spatial kNN network

plotStatDelaunayNetwork(gobject = seqfish_mini, maximum_distance = 400)
seqfish_mini = createSpatialNetwork(gobject = seqfish_mini, minimum_k = 2,
                  maximum_distance_delaunay = 400)
seqfish_mini = createSpatialNetwork(gobject = seqfish_mini, minimum_k = 2,
                method = 'kNN', k = 10)
showNetworks(seqfish_mini)

# visualize the two different spatial networks
spatPlot(gobject = seqfish_mini, show_network = T,
    network_color = 'blue', spatial_network_name = 'Delaunay_network',
    point_size = 2.5, cell_color = 'leiden_clus')

spatPlot(gobject = seqfish_mini, show_network = T,
    network_color = 'blue', spatial_network_name = 'kNN_network',
    point_size = 2.5, cell_color = 'leiden_clus')

9. Spatial Genes

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(seqfish_mini)
spatGenePlot(seqfish_mini, 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(seqfish_mini, bin_method = 'rank')
spatGenePlot(seqfish_mini, 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 = seqfish_mini) # TODO: suppress print output
spatGenePlot(seqfish_mini, 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)
9.1_spatGenePlot2D.png

10. Spatial Co-Expression Patterns

Identify robust spatial co-expression patterns using the spatial network or grid and a subset of individual spatial genes.

10.1 Calculate spatial correlation scores

# 1. calculate spatial correlation scores
ext_spatial_genes = km_spatialgenes[1:500]$genes
spat_cor_netw_DT = detectSpatialCorGenes(seqfish_mini,
                    method = 'network',
                    spatial_network_name = 'Delaunay_network',
                    subset_genes = ext_spatial_genes)

10.2. Cluster correlation scores

# 2. cluster correlation scores
spat_cor_netw_DT = clusterSpatialCorGenes(spat_cor_netw_DT,
                    name = 'spat_netw_clus', k = 8)
heatmSpatialCorGenes(seqfish_mini, spatCorObject = spat_cor_netw_DT,
        use_clus_name = 'spat_netw_clus')
10.1_heatmSpatialCorGenes.png 
netw_ranks = rankSpatialCorGroups(seqfish_mini,
                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

seqfish_mini = createMetagenes(seqfish_mini, gene_clusters = cluster_genes, name = 'cluster_metagene')
spatCellPlot(seqfish_mini,
    spat_enr_names = 'cluster_metagene',
    cell_annotation_values = netw_ranks$clusters,
    point_size = 1.5, cow_n_col = 3)
10.2_spatCellPlot2D.png

11. Spatial HMRF Domains

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 = seqfish_mini,
            expression_values = 'scaled',
            spatial_genes = my_spatial_genes,
            spatial_network_name = 'Delaunay_network',
            k = 9,
            betas = c(28,2,2),
            output_folder = paste0(hmrf_folder, '/', 'Spatial_genes/SG_top100_k9_scaled'))

# check and select hmrf
for(i in seq(28, 30, by = 2)) {
 viewHMRFresults2D(gobject = seqfish_mini,
        HMRFoutput = HMRF_spatial_genes,
        k = 9, betas_to_view = i,
        point_size = 2)
}

seqfish_mini = addHMRF(gobject = seqfish_mini,
        HMRFoutput = HMRF_spatial_genes,
        k = 9, betas_to_add = c(28),
        hmrf_name = 'HMRF')

# visualize selected hmrf result
giotto_colors = Giotto:::getDistinctColors(9)
names(giotto_colors) = 1:9
spatPlot(gobject = seqfish_mini, cell_color = 'HMRF_k9_b.28',
    point_size = 3, coord_fix_ratio = 1, cell_color_code = giotto_colors)

12. Cell Neighborhood: Cell-Type / Cell-Type Interactions

set.seed(seed = 2841)
cell_proximities = cellProximityEnrichment(gobject = seqfish_mini,
                   cluster_column = 'cell_types',
                   spatial_network_name = 'Delaunay_network',
                   adjust_method = 'fdr',
                   number_of_simulations = 1000)
# barplot
cellProximityBarplot(gobject = seqfish_mini,
        CPscore = cell_proximities,
        min_orig_ints = 5, min_sim_ints = 5)
12.1_cellProximityBarplot.png 
## heatmap
cellProximityHeatmap(gobject = seqfish_mini, CPscore = cell_proximities,
        order_cell_types = T, scale = T,
        color_breaks = c(-1.5, 0, 1.5),
        color_names = c('blue', 'white', 'red'))
12.2.png 
# network
cellProximityNetwork(gobject = seqfish_mini, CPscore = cell_proximities,
        remove_self_edges = T, only_show_enrichment_edges = T)
12.3_cellProximityHeatmap.png 
# network with self-edges
cellProximityNetwork(gobject = seqfish_mini, CPscore = cell_proximities,
        remove_self_edges = F, self_loop_strength = 0.3,
        only_show_enrichment_edges = F,
        rescale_edge_weights = T,
        node_size = 8,
        edge_weight_range_depletion = c(1, 2),
        edge_weight_range_enrichment = c(2,5))
12.4_cellProximityNetwork.png

12.1 Visualization of Specific Cell Types

Option 1

# Option 1
spec_interaction = "cell D--cell F"
cellProximitySpatPlot2D(gobject = seqfish_mini,
            interaction_name = spec_interaction,
            show_network = T,
            cluster_column = 'cell_types',
            cell_color = 'cell_types',
            cell_color_code = c('cell D' = 'lightblue', 'cell F' = 'red'),
         point_size_select = 4, point_size_other = 2)
12.5_cellProximitySpatPlot2D.png

Option 2

# Option 2: create additional metadata
seqfish_mini = addCellIntMetadata(seqfish_mini,
            spatial_network = 'Delaunay_network',
            cluster_column = 'cell_types',
            cell_interaction = spec_interaction,
             name = 'D_F_interactions')
spatPlot(seqfish_mini, cell_color = 'D_F_interactions', legend_symbol_size = 3,
    select_cell_groups =  c('other_cell D', 'other_cell F', 'select_cell D', 'select_cell F'))
12.6_spatPlot2D.png

13. Cell Neighborhood: Interaction Changed Genes

## select top 25th highest expressing genes
gene_metadata = fDataDT(seqfish_mini)
plot(gene_metadata$nr_cells, gene_metadata$mean_expr)
plot(gene_metadata$nr_cells, gene_metadata$mean_expr_det)

quantile(gene_metadata$mean_expr_det)
high_expressed_genes = gene_metadata[mean_expr_det > 4]$gene_ID

## identify genes that are associated with proximity to other cell types
ICGscoresHighGenes =  findICG(gobject = seqfish_mini,
              selected_genes = high_expressed_genes,
              spatial_network_name = 'Delaunay_network',
              cluster_column = 'cell_types',
              diff_test = 'permutation',
              adjust_method = 'fdr',
              nr_permutations = 500,
              do_parallel = T, cores = 2)

## visualize all genes
plotCellProximityGenes(seqfish_mini, cpgObject = ICGscoresHighGenes, method = 'dotplot')
13.1_plotCellProximityGenes.png 
## filter genes
ICGscoresFilt = filterICG(ICGscoresHighGenes,
          min_cells = 2, min_int_cells = 2, min_fdr = 0.1,
          min_spat_diff = 0.1, min_log2_fc = 0.1, min_zscore = 1)

## visualize subset of interaction changed genes (ICGs)
ICG_genes = c('Cpne2', 'Scg3', 'Cmtm3', 'Cplx1', 'Lingo1')
ICG_genes_types = c('cell E', 'cell D', 'cell D', 'cell G', 'cell E')
names(ICG_genes) = ICG_genes_types

plotICG(gobject = seqfish_mini,
    cpgObject = ICGscoresHighGenes,
        source_type = 'cell A',
        source_markers = c('Csf1r', 'Laptm5'),
        ICG_genes = ICG_genes)
13.2_plotICG.png

14. Cell Neighborhood: Ligand-Receptor Cell-Cell Communication

LR_data = data.table::fread(system.file("extdata", "mouse_ligand_receptors.txt", package = 'Giotto'))

LR_data[, ligand_det := ifelse(mouseLigand %in% seqfish_mini@gene_ID, T, F)]
LR_data[, receptor_det := ifelse(mouseReceptor %in% seqfish_mini@gene_ID, T, F)]
LR_data_det = LR_data[ligand_det == T & receptor_det == T]
select_ligands = LR_data_det$mouseLigand
select_receptors = LR_data_det$mouseReceptor


## get statistical significance of gene pair expression changes based on expression ##
expr_only_scores = exprCellCellcom(gobject = seqfish_mini,
               cluster_column = 'cell_types',
               random_iter = 500,
               gene_set_1 = select_ligands,
               gene_set_2 = select_receptors)

## get statistical significance of gene pair expression changes upon cell-cell interaction
spatial_all_scores = spatCellCellcom(seqfish_mini,
                 spatial_network_name = 'Delaunay_network',
                 cluster_column = 'cell_types',
                 random_iter = 500,
                 gene_set_1 = select_ligands,
                 gene_set_2 = select_receptors,
                 adjust_method = 'fdr',
                 do_parallel = T,
                 cores = 4,
                 verbose = 'none')


## * plot communication scores ####

## select top LR ##
selected_spat = spatial_all_scores[p.adj <= 0.5 & abs(log2fc) > 0.1 & lig_nr >= 2 & rec_nr >= 2]
data.table::setorder(selected_spat, -PI)

top_LR_ints = unique(selected_spat[order(-abs(PI))]$LR_comb)[1:33]
top_LR_cell_ints = unique(selected_spat[order(-abs(PI))]$LR_cell_comb)[1:33]

plotCCcomHeatmap(gobject = seqfish_mini,
        comScores = spatial_all_scores,
        selected_LR = top_LR_ints,
        selected_cell_LR = top_LR_cell_ints,
        show = 'LR_expr')
14.1_plotCCcomHeatmap.png 
plotCCcomDotplot(gobject = seqfish_mini,
        comScores = spatial_all_scores,
        selected_LR = top_LR_ints,
        selected_cell_LR = top_LR_cell_ints,
        cluster_on = 'PI')
14.2_plotCCcomDotplot.png 
## * spatial vs rank ####
comb_comm = combCCcom(spatialCC = spatial_all_scores,
          exprCC = expr_only_scores)

# top differential activity levels for ligand receptor pairs
plotRankSpatvsExpr(gobject = seqfish_mini,
        comb_comm,
        expr_rnk_column = 'exprPI_rnk',
        spat_rnk_column = 'spatPI_rnk',
        midpoint = 10)
14.3_plotRankSpatvsExpr.png 
## * recovery ####
## predict maximum differential activity
plotRecovery(gobject = seqfish_mini,
    comb_comm,
    expr_rnk_column = 'exprPI_rnk',
    spat_rnk_column = 'spatPI_rnk',
    ground_truth = 'spatial')
14.4_plotRecovery.png

15. Export Giotto Analyzer to Viewer

viewer_folder = paste0(temp_dir, '/', 'Mouse_cortex_viewer')

# select annotations, reductions and expression values to view in Giotto Viewer
exportGiottoViewer(gobject = seqfish_mini, output_directory = viewer_folder,
        factor_annotations = c('cell_types',
                  'leiden_clus',
                  'HMRF_k9_b.28'),
        numeric_annotations = 'total_expr',
        dim_reductions = c('umap'),
        dim_reduction_names = c('umap'),
        expression_values = 'scaled',
        expression_rounding = 3,
        overwrite_dir = T)