In RubD/Giotto_site: Spatial Single-Cell Transcriptomics Toolbox

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

library(Giotto)

## visual cortex expression DATA ##
VC_exprs = read.table(system.file("extdata", "cortex_svz_expression.txt", package = "Giotto"))

## prepare cell locations
VC_locs = fread(system.file("extdata", "cortex_svz_centroids_rotated.csv", package = "Giotto"))
my_offset_file = data.table(field = c(0, 1, 2, 3, 4, 5, 6),
                            x_offset = c(0, 2048, 2048, 2048, 675, 2048, 675),
                            y_offset = c(2048, 2048, 2048, 2048,0, 0, 2048))
stitch_file = stitchFieldCoordinates(location_file = VC_locs, offset_file = my_offset_file,
                                     cumulate_offset_x = T, cumulate_offset_y = F,
                                     field_col = 'Field of View',
                                     reverse_final_x = F,
                                     reverse_final_y = T)
stitch_file    = stitch_file[,.(X_final, Y_final)]
my_offset_file = my_offset_file[,.(field, x_offset_final, y_offset_final)]

## create
VC_test <- createGiottoObject(raw_exprs = VC_exprs, spatial_locs = stitch_file, offset_file = my_offset_file)
## filter
VC_test <- filterGiotto(gobject = VC_test,
                        expression_threshold = 1,
                        min_det_genes_per_cell = 10,
                        gene_det_in_min_cells = 10,
                        expression_values = c('raw'))
## normalize
VC_test <- normalizeGiotto(gobject = VC_test)
# gene and cell statistics
VC_test <- addStatistics(gobject = VC_test)
# adjust for covariates
VC_test = adjustGiottoMatrix(gobject = VC_test, expression_values = c('normalized'),
                             batch_columns = NULL, covariate_columns = c('nr_genes', 'total_expr'),
                             return_gobject = TRUE,
                             update_slot = c('custom'))

Start from Giotto object from the Mouse Visual Cortex and SVZ:

spatPlot(gobject = VC_test, point_size = 2, return_plot = F)

1. add annotation

# cell metadata before 
print(pDataDT(VC_test))

# add metadata by column name
cortex_annotation = fread(system.file("extdata", "cortex_annotation.txt", package = "Giotto"))
VC_test = addCellMetadata(gobject = VC_test, new_metadata = cortex_annotation, by_column = T, column_cell_ID = 'uniq_ID')

# cell metadata after
cell_metadata = pDataDT(VC_test)
print(cell_metadata)

2. subset Giotto

subset first 500 cells

first_500_cells = cell_metadata[1:500][['cell_ID']]
VC_subset = subsetGiotto(VC_test, cell_ids = first_500_cells)
spatPlot2D(gobject = VC_subset, point_size = 2, cell_color = 'cell_types', return_plot = F)

{ width=50% }

subset for SVZ cells (located in field 5)

SVZ_cells = cell_metadata[Field_of_View == 5][['cell_ID']]
VC_SVZ = subsetGiotto(VC_test, cell_ids = SVZ_cells)
spatPlot2D(gobject = VC_SVZ, point_size = 4, cell_color = 'cell_types', return_plot = F)

subset for all interneurons

interneuron_cells = cell_metadata[cell_types == 'Interneuron'][['cell_ID']]
VC_interneuron = subsetGiotto(VC_test, cell_ids = interneuron_cells)
spatPlot2D(gobject = VC_interneuron, point_size = 2, cell_color = 'cell_types', return_plot = F)