3. Algorithm for binning data

Here, we’ll discuss the algorithm for binning data.

library(quollr)
library(ggplot2)
library(dplyr)

By passing the preprocessed 2-D embedding data and hexagonal grid configurations, you can obtain the hexagonal binning information like centroid coordinates, hexagonal polygon coordinates, the standardise counts within each hexagon etc.

hb_obj <- hex_binning(nldr_obj = scurve_model_obj$nldr_obj, b1 = 5, q = 0.1)
## Data set with all possible centroids in the hexagonal grid
all_centroids_df <- hb_obj$centroids
glimpse(all_centroids_df)
#> Rows: 35
#> Columns: 3
#> $ h   <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,…
#> $ c_x <dbl> -0.1000000, 0.1641342, 0.4282684, 0.6924026, 0.9565368, 0.0320671,…
#> $ c_y <dbl> -0.1156801, -0.1156801, -0.1156801, -0.1156801, -0.1156801, 0.1130…

## Generate all coordinates of hexagons
hex_grid <- hb_obj$hex_poly
glimpse(hex_grid)
#> Rows: 210
#> Columns: 3
#> $ h <int> 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4,…
#> $ x <dbl> -0.1000000, -0.2320671, -0.2320671, -0.1000000, 0.0320671, 0.0320671…
#> $ y <dbl> 0.03681781, -0.03943117, -0.19192912, -0.26817809, -0.19192912, -0.0…

## To obtain the standardise counts within hexbins
counts_df <- hb_obj$std_cts
df_bin_centroids <- extract_hexbin_centroids(centroids_data = all_centroids_df, 
                                             counts_data = counts_df)
ggplot(data = hex_grid, aes(x = x, y = y)) + 
  geom_polygon(fill = "white", color = "black", aes(group = h)) +
  geom_point(data = all_centroids_df, aes(x = c_x, y = c_y), color = "red") +
  coord_fixed()

Grid with hexagon centroids.


ggplot(data = hex_grid, aes(x = x, y = y)) + 
  geom_polygon(fill = "white", color = "black", aes(group = h)) +
  geom_point(data = all_centroids_df, aes(x = c_x, y = c_y), color = "red") +
  geom_point(data = df_bin_centroids, aes(x = c_x, y = c_y), color = "purple") + 
  coord_fixed()

Grid with hexagons colored empty bin centroid in red.

umap_scaled <- scurve_model_obj$nldr_obj$scaled_nldr

ggplot(data = hex_grid, aes(x = x, y = y)) + 
  geom_polygon(fill = "white", color = "black", aes(group = h)) +
  geom_point(data = umap_scaled, aes(x = emb1, y = emb2), color = "blue", alpha = 0.3) +
  coord_fixed()

Grid with hexagons overlayed data.

hex_grid_with_counts <- left_join(hex_grid, counts_df, by = "h")

ggplot(data = hex_grid_with_counts, aes(x = x, y = y)) +
  geom_polygon(color = "black", aes(group = h, fill = w_h)) +
  geom_text(data = all_centroids_df, aes(x = c_x, y = c_y, label = h)) +
  scale_fill_viridis_c(direction = -1, na.value = "#ffffff") +
  coord_fixed()

Grid with hexagons colored with density of points.

You can also use geom_hexgrid to visualise the hexagonal grid rather than geom_polygon.

ggplot(data = all_centroids_df, aes(x = c_x, y = c_y)) +
  geom_hexgrid() +
  coord_equal() +
  xlab("x") + ylab("y") +
  theme(axis.text = element_text(size = 5),
        axis.title = element_text(size = 7))

Grid with hexagons.