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add mini starmap test
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@RubD
RubD committed Jun 14, 2020
1 parent 92e5067 commit 2dcd26b
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12 changes: 6 additions & 6 deletions R/spatial_enrichment.R
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Original file line number Diff line number Diff line change
Expand Up @@ -136,7 +136,7 @@ do_rank_permutation <- function(sc_gene, n){
do_page_permutation<-function(gobject,
sig_gene,
ntimes){
# check available gene
# check available gene
available_ct<-c()
for (i in colnames(sig_gene)){
gene_i=rownames(sig_gene)[which(sig_gene[,i]==1)]
Expand Down Expand Up @@ -221,8 +221,8 @@ PAGEEnrich <- function(gobject,
# expression values to be used
values = match.arg(expression_values, c('normalized', 'scaled', 'custom'))
expr_values = select_expression_values(gobject = gobject, values = values)
# check available gene

# check available gene
available_ct<-c()
for (i in colnames(sign_matrix)){
gene_i=rownames(sign_matrix)[which(sign_matrix[,i]==1)]
Expand Down Expand Up @@ -359,7 +359,7 @@ rankEnrich <- function(gobject,

multiplyRank = (filterRankFold*filterSig[,i])^(1/2)
rpb = 0.01*(0.99^(multiplyRank-1))

vectorX = rep(NA, dim(filterRankFold)[2])

for (j in (1:dim(filterRankFold)[2])){
Expand Down Expand Up @@ -503,7 +503,7 @@ createSpatialEnrich = function(gobject,
expression_values = c('normalized', 'scaled', 'custom'),
reverse_log_scale = TRUE,
logbase = 2,
p_value = TRUE,
p_value = FALSE,
n_genes = 100,
n_times = 1000,
top_percentage = 5,
Expand All @@ -527,7 +527,7 @@ createSpatialEnrich = function(gobject,
# default name for page enrichment
if(is.null(name)) name = 'PAGE'
if (p_value==TRUE){
# check available gene
# check available gene
available_ct<-c()
for (i in colnames(sign_matrix)){
gene_i=rownames(sign_matrix)[which(sign_matrix[,i]==1)]
Expand Down
160 changes: 160 additions & 0 deletions inst/examples/mini_starmap_test/mini_starmap_v0.3.5_200614.R
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@@ -0,0 +1,160 @@


library(Giotto)

# createGiottoInstructions(python_path = '/your/path')

## Giotto 0.3.5 ##
## mini-test Visium Brain Giotto 0.3.5 ##

## !! change this directory path !!:
temp_dir = '/path/to/your/temporary/directory/results'
temp_dir = '/Volumes/Ruben_Seagate/Dropbox/Projects/GC_lab/Ruben_Dries/190225_spatial_package/Results/Temp/starmap/'

## 1. giotto object ####
expr_path = system.file("extdata", "starmap_expr.txt", package = 'Giotto')
loc_path = system.file("extdata", "starmap_cell_loc.txt", package = 'Giotto')

# default
star_small <- createGiottoObject(raw_exprs = expr_path, spatial_locs = loc_path)

## 2. processing steps ####
filterDistributions(star_small, detection = 'genes')
filterDistributions(star_small, detection = 'cells')
filterCombinations(star_small,
expression_thresholds = c(1),
gene_det_in_min_cells = c(50, 100, 200),
min_det_genes_per_cell = c(20, 28, 28))

star_small <- filterGiotto(gobject = star_small,
expression_threshold = 1,
gene_det_in_min_cells = 50,
min_det_genes_per_cell = 20,
expression_values = c('raw'),
verbose = T)
star_small <- normalizeGiotto(gobject = star_small, scalefactor = 6000, verbose = T)
star_small <- addStatistics(gobject = star_small)


## 3. dimension reduction ####
star_small <- runPCA(gobject = star_small, method = 'factominer')
screePlot(star_small, ncp = 30)
plotPCA(gobject = star_small)

# 2D umap
star_small <- runUMAP(star_small, dimensions_to_use = 1:8)
plotUMAP(gobject = star_small)

# 3D umap
star_small <- runUMAP(star_small, dimensions_to_use = 1:8, name = '3D_umap', n_components = 3)
plotUMAP_3D(gobject = star_small, dim_reduction_name = '3D_umap')

# 2D tsne
star_small <- runtSNE(star_small, dimensions_to_use = 1:8)
plotTSNE(gobject = star_small)

## 4. clustering ####
star_small <- createNearestNetwork(gobject = star_small, dimensions_to_use = 1:8, k = 25)
star_small <- doLeidenCluster(gobject = star_small, resolution = 0.5, n_iterations = 1000)

# 2D umap
plotUMAP(gobject = star_small, cell_color = 'leiden_clus', show_NN_network = T, point_size = 2.5)

# 3D umap
plotUMAP_3D(gobject = star_small, dim_reduction_name = '3D_umap', cell_color = 'leiden_clus')


## 5. co-visualize ####

# 2D
spatDimPlot(gobject = star_small, cell_color = 'leiden_clus',
dim_point_size = 2, spat_point_size = 2.5)

# 3D
spatDimPlot3D(gobject = star_small, cell_color = 'leiden_clus', dim_reduction_name = '3D_umap')



## 6. differential expression ####
markers = findMarkers_one_vs_all(gobject = star_small,
method = 'gini',
expression_values = 'normalized',
cluster_column = 'leiden_clus')

# violinplot
topgenes = markers[, head(.SD, 2), by = 'cluster']$genes
violinPlot(star_small, genes = topgenes, cluster_column = 'leiden_clus')

# genes heatmap
plotHeatmap(star_small, genes = star_small@gene_ID, cluster_column = 'leiden_clus',
legend_nrows = 2, expression_values = 'scaled',
cluster_order = 'correlation', gene_order = 'correlation')

# cluster heatmap
plotMetaDataHeatmap(star_small, expression_values = 'scaled', metadata_cols = c('leiden_clus'))



## 7. gene expression
dimGenePlot3D(star_small,
dim_reduction_name = '3D_umap',
expression_values = 'scaled',
genes = "Pcp4",
genes_high_color = 'red', genes_mid_color = 'white', genes_low_color = 'darkblue')

spatGenePlot3D(star_small,
expression_values = 'scaled',
genes = "Pcp4",
show_other_cells = F,
genes_high_color = 'red', genes_mid_color = 'white', genes_low_color = 'darkblue')




## 8. cross section
# install.packages('geometry') # necessary for 3D delaunay network
star_small <- createSpatialNetwork(gobject = star_small, delaunay_method = 'delaunayn_geometry')

star_small = createCrossSection(star_small,
method="equation",
equation=c(0,1,0,600),
extend_ratio = 0.6)

# show cross section
insertCrossSectionSpatPlot3D(star_small, cell_color = 'leiden_clus',
axis_scale = 'cube',
point_size = 2)

insertCrossSectionGenePlot3D(star_small, expression_values = 'scaled',
axis_scale = "cube",
genes = "Slc17a7")


# for cell annotation
crossSectionPlot(star_small,
point_size = 2, point_shape = "border",
cell_color = "leiden_clus")

crossSectionPlot3D(star_small,
point_size = 2, cell_color = "leiden_clus",
axis_scale = "cube")


# for gene expression
crossSectionGenePlot(star_small,
genes = "Slc17a7",
point_size = 2,
point_shape = "border",
cow_n_col = 1.5,
expression_values = 'scaled')

crossSectionGenePlot3D(star_small,
point_size = 2,
genes = c("Slc17a7"),
expression_values = 'scaled')






18 changes: 15 additions & 3 deletions inst/examples/mini_visium_test/mini_visium_deg_v0.3.5_200612.R
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Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,7 @@ library(Giotto)

## !! change this directory path !!:
temp_dir = '/path/to/your/temporary/directory/results'
temp_dir = '/Volumes/Ruben_Seagate/Dropbox/Projects/GC_lab/Ruben_Dries/190225_spatial_package/Results/Temp/visium/'

## 1. giotto object ####
expr_path = system.file("extdata", "visium_DG_expr.txt", package = 'Giotto')
Expand Down Expand Up @@ -126,14 +127,25 @@ brain_sc_markers = data.table::fread(sign_matrix_path) # file don't exist in dat
sig_matrix = as.matrix(brain_sc_markers[,-1]); rownames(sig_matrix) = brain_sc_markers$Event

## enrichment tests
visium_brain = createSpatialEnrich(brain_small, sign_matrix = sig_matrix, enrich_method = 'PAGE') #default = 'PAGE'
brain_small = createSpatialEnrich(brain_small, 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 = visium_brain,
plotMetaDataCellsHeatmap(gobject = brain_small,
metadata_cols = 'leiden_clus',
value_cols = cell_types,
spat_enr_names = 'PAGE',x_text_size = 8, y_text_size = 8)
spat_enr_names = 'PAGE',
x_text_size = 8, y_text_size = 8)


enrichment_results = brain_small@spatial_enrichment$PAGE
enrich_cell_types = colnames(enrichment_results)
enrich_cell_types = enrich_cell_types[enrich_cell_types != 'cell_ID']

## spatplot
spatCellPlot(gobject = brain_small, spat_enr_names = 'PAGE',
cell_annotation_values = enrich_cell_types,
cow_n_col = 3,coord_fix_ratio = NULL, point_size = 1)



Expand Down

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