Extract Coordinates of Shifted Hexagonal Grid — extract_coord_of_shifted_hex

This function takes input data, which includes a hexbin ID, and extracts the coordinates of a hexagonal grid with a specified shift. The resulting dataset includes hexagon centroids with updated coordinates and additional information such as counts within each hexagon.

Usage

extract_coord_of_shifted_hex_grid(
  nldr_data_with_hb_id,
  num_bins_x,
  hex_full_count_df,
  shift_x = NA,
  shift_y = NA,
  cell_area = 1
)

Arguments

nldr_data_with_hb_id: A containing 2D embeddings with a hexbin ID.
num_bins_x: The number of bins along the x-axis for the hexagonal grid.
hex_full_count_df: A data frame with information about all hexagonal grid cells.
shift_x: The value that centroids need to be shifted in x-axis. To shift in the negative direction along the x-axis, set shift_x to a positive value (shift_x > 0); otherwise, for a shift in the positive direction, use a negative value (shift_x < 0). If not provided, it is calculated as half of the cell diameter of a hexagon.
shift_y: The value that centroids need to be shifted in y-axis. To shift in the negative direction along the y-axis, set shift_y to a positive value (shift_y > 0); otherwise, for a shift in the positive direction, use a negative value (shift_y < 0). If not provided, it is calculated as half of the cell diameter of a hexagon.
cell_area: A numeric value that initialise the area of the hexagon. The default is 1.

Value

A data frame with updated hexagon coordinates, hexagon IDs, and counts within each hexagon.

A list containing:

hex_full_count_df_new: Data frame with updated hexagonal grid information, including counts and standardized counts.
nldr_df_with_new_hexID: Data frame with new hexagonal bin IDs assigned to 2D embeddings.

Examples

num_bins_x <- 4
shape_value <- 1.833091
hexbin_data_object <- extract_hexbin_centroids(nldr_df = s_curve_noise_umap,
num_bins = num_bins_x, shape_val = shape_value)
df_bin_centroids <- hexbin_data_object$hexdf_data
hex_full_count_df <- generate_full_grid_info(df_bin_centroids)
UMAP_data_with_hb_id <- s_curve_noise_umap |> dplyr::mutate(hb_id = hexbin_data_object$hb_data@cID)
extract_coord_of_shifted_hex_grid(nldr_data_with_hb_id = UMAP_data_with_hb_id,
num_bins_x = num_bins_x, hex_full_count_df)
#> $hex_full_count_df_new
#> # A tibble: 300 × 9
#>        x     y    id   c_x   c_y hexID polygon_id counts std_counts
#>    <dbl> <dbl> <int> <dbl> <dbl> <int>      <int>  <int>      <dbl>
#>  1 -3.09 -5.83     1 -3.81 -6.28     1          1     NA       NA  
#>  2 -3.09 -6.73     1 -3.81 -6.28     1          1     NA       NA  
#>  3 -3.81 -7.19     1 -3.81 -6.28     1          1     NA       NA  
#>  4 -4.52 -6.73     1 -3.81 -6.28     1          1     NA       NA  
#>  5 -4.52 -5.83     1 -3.81 -6.28     1          1     NA       NA  
#>  6 -3.81 -5.37     1 -3.81 -6.28     1          1     NA       NA  
#>  7 -1.66 -5.83     2 -2.38 -6.28     2          2      4        0.4
#>  8 -1.66 -6.73     2 -2.38 -6.28     2          2      4        0.4
#>  9 -2.38 -7.19     2 -2.38 -6.28     2          2      4        0.4
#> 10 -3.09 -6.73     2 -2.38 -6.28     2          2      4        0.4
#> # ℹ 290 more rows
#> 
#> $nldr_df_with_new_hexID
#>         UMAP1        UMAP2 hb_id
#> 1  -2.8097466 -3.910005483     6
#> 2   0.9587085 -0.002708352    29
#> 3   1.5389855  0.462163394    29
#> 4  -2.3129820 -5.495084256     2
#> 5  -1.7648455 -3.461140117    12
#> 6   1.5291731  5.746500231    49
#> 7   0.9304771 -0.175236058    29
#> 8   0.3194170 -1.614071523    18
#> 9   1.3675692  0.054122172    29
#> 10  1.9025902  4.943403570    45
#> 11  0.4080949 -1.525707795    18
#> 12 -2.9735853 -3.600461658    11
#> 13  2.2396519  4.583359135    45
#> 14 -2.8567553 -4.284969555     6
#> 15  0.4467211 -1.780856063    18
#> 16  1.3552697  3.435359060    39
#> 17 -2.4962950 -3.502364361    12
#> 18  1.3486792  3.477064406    39
#> 19  0.1830594 -1.754456198    18
#> 20 -2.3455605 -5.676414556     2
#> 21  0.8137371 -0.419929238    24
#> 22 -2.7317882 -5.350256398     6
#> 23 -2.7607269 -4.058474305     6
#> 24  1.1678508  5.807591478    49
#> 25  1.2701112  1.606845833    34
#> 26 -1.6341561 -4.391463483     7
#> 27 -0.5902292 -2.519974956    18
#> 28  0.0244208 -1.012724342    23
#> 29 -2.0648469 -4.728244274     6
#> 30  0.3033282 -0.986658735    23
#> 31  1.1236335  1.058988539    29
#> 32  1.1058204  4.622732304    44
#> 33  0.4424786 -1.323557255    18
#> 34 -3.0888293 -3.715523544    11
#> 35  2.2523472  5.375278885    50
#> 36 -2.6910127 -5.402672793     6
#> 37  0.8102587  4.536541146    44
#> 38  1.4767116  5.758544173    49
#> 39  1.3834120  5.820530794    49
#> 40 -3.2703577 -3.928162328    11
#> 41 -1.0720147 -3.137653586    13
#> 42  1.4614730  1.011057982    29
#> 43 -1.5951242 -4.821159579     7
#> 44  1.5670651  0.820999232    29
#> 45  1.3101234  0.696316310    29
#> 46  0.6999723  3.925631251    38
#> 47 -2.4996853 -5.742540480     2
#> 48 -3.0293226 -4.020706179     6
#> 49  2.1260682  5.479212510    50
#> 50 -2.9640458 -4.487579628     6
#> 51  0.6620562 -0.544895479    24
#> 52  1.1860855 -0.135167840    29
#> 53  1.0530198  5.412247159    49
#> 54 -1.9462124 -5.335024462     7
#> 55  1.1289559  4.252394278    44
#> 56  0.7288703  5.083452963    44
#> 57  0.8913631  0.477821407    29
#> 58  2.4165932  4.412252828    45
#> 59  2.2395888  4.579307233    45
#> 60 -1.0191663 -3.551255942    12
#> 61  0.9769054  5.485903919    49
#> 62  2.1492828  4.432353405    39
#> 63 -0.3539761 -1.620782255    18
#> 64  2.4555949  5.109462560    45
#> 65 -1.3494107 -3.882030702     7
#> 66 -1.8130766 -5.321458199     7
#> 67  1.8021561  3.660608734    39
#> 68  0.8633422  0.619339211    28
#> 69  0.6249804  4.329653948    38
#> 70  2.0282706  3.973496871    39
#> 71  1.3656407  4.467208531    44
#> 72 -0.4312690 -1.703695332    17
#> 73 -2.3369666 -5.540483168     2
#> 74 -1.5812238 -4.959468236     7
#> 75  1.9432959  3.907273242    39
#>