Interaction changed genes

1. processing steps

library(Giotto)

path_to_matrix = system.file("extdata", "seqfish_field_expr.txt", package = 'Giotto')
path_to_locations = system.file("extdata", "seqfish_field_locs.txt", package = 'Giotto')

my_giotto_object = createGiottoObject(raw_exprs = path_to_matrix,
                                      spatial_locs = path_to_locations)

# processing
my_giotto_object <- filterGiotto(gobject = seqfish_mini, 
                             expression_threshold = 0.5, 
                             gene_det_in_min_cells = 20, 
                             min_det_genes_per_cell = 0)
my_giotto_object <- normalizeGiotto(gobject = my_giotto_object)

# dimension reduction
my_giotto_object <- calculateHVG(gobject = my_giotto_object)
my_giotto_object <- runPCA(gobject = my_giotto_object)
my_giotto_object <- runUMAP(my_giotto_object, dimensions_to_use = 1:5)

# leiden clustering
my_giotto_object = doLeidenCluster(my_giotto_object, name = 'leiden_clus')

# annotate
metadata = pDataDT(my_giotto_object)
uniq_clusters = length(unique(metadata$leiden_clus))

clusters_cell_types = paste0('cell ', LETTERS[1:uniq_clusters])
names(clusters_cell_types) = 1:uniq_clusters

my_giotto_object = annotateGiotto(gobject = my_giotto_object, 
                              annotation_vector = clusters_cell_types, 
                              cluster_column = 'leiden_clus', 
                              name = 'cell_types')

# create network (required for binSpect methods)
my_giotto_object = createSpatialNetwork(gobject = my_giotto_object, minimum_k = 2)

# identify genes with a spatial coherent expression profile
km_spatialgenes = binSpect(my_giotto_object, bin_method = 'kmeans')

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2. Run Interaction Changed Genes (ICG)

## select top 25th highest expressing genes
gene_metadata = fDataDT(my_giotto_object)
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 = my_giotto_object,
                              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')

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3. Filter ICGs

## 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)

4. visualize selected ICG

## visualize subset of interaction changed genes (ICGs)
# random subset
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 = my_giotto_object,
        cpgObject = ICGscoresHighGenes,
        source_type = 'cell A',
        source_markers = c('Csf1r', 'Laptm5'),
        ICG_genes = ICG_genes)