In drieslab/Giotto_site_suite: Spatial Single-Cell Transcriptomics Toolbox

Technologies like Visium spatial transcriptomics does not provide single-cell resolution, making cell type annotation a harder problem.
One barcode of these spatial datasets can be made up of different cell types.

To solve this problem, Giotto provides several ways to calculate enrichment of specific cell-type signature gene list:
Spatial Enrichment:
- PAGE
- hypergeometric test
- Rank
Spatial Deconvolution:
- DWLS Deconvolution

The deconvolution analysis is also a good way to integrate single cell RNAseq data with spatial transcriptomics datasets.
In this example, Corresponded Single cell dataset can be generated from here.
Giotto_SC is processed from the downsampled Loom file and can also be downloaded from getSpatialDataset.

# download data to results directory ####
# if wget is installed, set method = 'wget'
# if you run into authentication issues with wget, then add " extra = '--no-check-certificate' "
getSpatialDataset(dataset = 'Mouse_brain_scRNAseq', directory = results_folder)

sc_expression = paste0(results_folder, "/brain_sc_expression_matrix.txt.gz")
sc_metadata = paste0(results_folder,"/brain_sc_metadata.csv")

giotto_SC <- createGiottoObject(
  expression = sc_expression,
  instructions = instrs
)

giotto_SC <- addCellMetadata(giotto_SC, 
                             new_metadata = data.table::fread(sc_metadata))

giotto_SC<- normalizeGiotto(giotto_SC)

PAGE enrichment

# Create PAGE matrix
# PAGE matrix should be a binary matrix with each row represent a gene marker and each column represent a cell type
# There are several ways to create PAGE matrix
# 1.1 create binary matrix of cell signature genes
# small example #
gran_markers = c("Nr3c2", "Gabra5", "Tubgcp2", "Ahcyl2",
                 "Islr2", "Rasl10a", "Tmem114", "Bhlhe22", 
                 "Ntf3", "C1ql2")

oligo_markers = c("Efhd1", "H2-Ab1", "Enpp6", "Ninj2",
                  "Bmp4", "Tnr", "Hapln2", "Neu4",
                  "Wfdc18", "Ccp110")        

di_mesench_markers = c("Cartpt", "Scn1a", "Lypd6b",  "Drd5",
                       "Gpr88", "Plcxd2", "Cpne7", "Pou4f1",
                       "Ctxn2", "Wnt4")

PAGE_matrix_1 = makeSignMatrixPAGE(sign_names = c('Granule_neurons',
                                                  'Oligo_dendrocytes',
                                                  'di_mesenchephalon'),
                                   sign_list = list(gran_markers,
                                                    oligo_markers,
                                                    di_mesench_markers))



# ----

# 1.2 [shortcut] fully pre-prepared matrix for all cell types
sign_matrix_path = system.file("extdata", "sig_matrix.txt", package = 'Giotto')
brain_sc_markers = data.table::fread(sign_matrix_path) 
PAGE_matrix_2 = as.matrix(brain_sc_markers[,-1])
rownames(PAGE_matrix_2) = brain_sc_markers$Event


# ---

# 1.3 make PAGE matrix from single cell dataset
markers_scran = findMarkers_one_vs_all(gobject=giotto_SC, method="scran",
                                       expression_values="normalized", cluster_column = "Class", min_feats=3)
top_markers <- markers_scran[, head(.SD, 10), by="cluster"]
celltypes<-levels(factor(markers_scran$cluster)) 
sign_list<-list()
for (i in 1:length(celltypes)){
  sign_list[[i]]<-top_markers[which(top_markers$cluster == celltypes[i]),]$feats
}

PAGE_matrix_3 = makeSignMatrixPAGE(sign_names = celltypes,
                                   sign_list = sign_list)

# 1.4 enrichment test with PAGE

# runSpatialEnrich() can also be used as a wrapper for all currently provided enrichment options
visium_brain = runPAGEEnrich(gobject = visium_brain, sign_matrix = PAGE_matrix_2)

# 1.5 heatmap of enrichment versus annotation (e.g. clustering result)
cell_types_PAGE = colnames(PAGE_matrix_2)
plotMetaDataCellsHeatmap(gobject = visium_brain,
                         metadata_cols = 'leiden_clus',
                         value_cols = cell_types_PAGE,
                         spat_enr_names = 'PAGE',
                         x_text_size = 8,
                         y_text_size = 8)

{ width=50% }

PAGE enrichment stats will be stored in the giotto_object@spatial_enrichment slot and can be retrieved by get_spatial_enrichment. The enrichment result can be visualized by spatplot by adding the spat_enr_names argument.

# 1.6 visualizations
# Check PAGE enrichment result
# get_spatial_enrichment(visium_brain,enrichm_name = "PAGE")
spatCellPlot2D(gobject = visium_brain,
               spat_enr_names = 'PAGE',
               cell_annotation_values = cell_types_PAGE[1:4],
               cow_n_col = 2,coord_fix_ratio = 1, point_size = 1.25, show_legend = T,
               save_param = list(save_name="spat_enr_PAGE_spatcellplot"))

{ width=50% }

spatDimCellPlot2D(gobject = visium_brain,
                  spat_enr_names = 'PAGE',
                  cell_annotation_values = cell_types_PAGE[1:4],
                  cow_n_col = 1, spat_point_size = 1,
                  plot_alignment = 'horizontal',
                  save_param = list(base_width=7, base_height=10, save_name="spat_enr_PAGE_spatdimplot"))

{ width=50% }

HyperGeometric test

#Modify the sparse matrix in normalized slot
visium_brain = runHyperGeometricEnrich(gobject = visium_brain,
                                       expression_values = "normalized",
                                       sign_matrix = PAGE_matrix_2)

HyperGeometric enrichment stats will be stored in the giotto_object@spatial_enrichment slot and can be retrieved by get_spatial_enrichment. The enrichment result can be visualized by spatplot by adding the spat_enr_names argument.

# Check HyperGeometric enrichment result
# get_spatial_enrichment(visium_brain,enrichm_name = "hypergeometric")
cell_types_HyperGeometric = colnames(PAGE_matrix_2)
spatCellPlot(gobject = visium_brain,
             spat_enr_names = 'hypergeometric',
             cell_annotation_values = cell_types_HyperGeometric[1:4],
             cow_n_col = 2,coord_fix_ratio = 1, point_size = 1.75,
             save_param = list(save_name="spat_enr_HyperGeometric_spatplot"))

{ width=50% }

Rank

This algorithm calculates gene signature enrichment scores per spatial position using a rank based approach. Rank enrichment stats will be stored in the giotto_object@spatial_enrichment slot and can be retrieved by get_spatial_enrichment. The enrichment result can be visualized by spatplot by adding the spat_enr_names argument.

# Create rank matrix, not that rank matrix is different from PAGE
# A count matrix and a vector for all cell labels will be needed
rank_matrix = makeSignMatrixRank(sc_matrix = get_expression_values(giotto_SC,values = "normalized"),
                                 sc_cluster_ids = pDataDT(giotto_SC)$Class)
colnames(rank_matrix)<-levels(factor(pDataDT(giotto_SC)$Class))
visium_brain = runRankEnrich(gobject = visium_brain, sign_matrix = rank_matrix,expression_values = "normalized")

# Check Rank enrichment result
# get_spatial_enrichment(visium_brain,enrichm_name = "rank")

# Plot Rank enrichment result
spatCellPlot2D(gobject = visium_brain,
               spat_enr_names = 'rank',
               cell_annotation_values = colnames(rank_matrix)[1:4],
               cow_n_col = 2,coord_fix_ratio = 1, point_size = 1,
               save_param = list(save_name = "spat_enr_Rank_plot"))

{ width=50% }

Deconvolution

DWLS deconvolution stats will be also stored in the giotto_object@spatial_enrichment slot and can be retrieved by get_spatial_enrichment. The enrichment result can be visualized by spatplot by adding the spat_enr_names argument.

# Create DWLS matrix, not that DWLS matrix is different from PAGE and rank
# A count matrix a vector for a list of gene signatures and a vector for all cell labels will be needed
DWLS_matrix<-makeSignMatrixDWLSfromMatrix(matrix = as.matrix(get_expression_values(giotto_SC,values = "normalized")),
                                          cell_type = pDataDT(giotto_SC)$Class,
                                          sign_gene = top_markers$feats)
visium_brain = runDWLSDeconv(gobject = visium_brain, sign_matrix = DWLS_matrix)

# Check DWLS deconvolution result
# get_spatial_enrichment(visium_brain,enrichm_name = "DWLS")

# Plot DWLS deconvolution result
spatCellPlot2D(gobject = visium_brain,
               spat_enr_names = 'DWLS',
               cell_annotation_values = levels(factor(pDataDT(giotto_SC)$Class))[1:4],
               cow_n_col = 2,coord_fix_ratio = 1, point_size = 1,
               save_param = list(save_name = "DWLS_plot"))