Estimate the local densities for all cells in a high-dimensional cytometry dataset.

This function is a wrapper around tidytof's tof_*_density() function family. It performs local density estimation on high-dimensional cytometry data using a user-specified method (of 3 choices) and each method's corresponding input parameters.

Usage

tof_estimate_density(
  tof_tibble,
  distance_cols = where(tof_is_numeric),
  distance_function = c("euclidean", "cosine", "l2", "ip"),
  normalize = TRUE,
  ...,
  augment = TRUE,
  method = c("mean_distance", "sum_distance", "spade")
)

Arguments

tof_tibble

A `tof_tbl` or a `tibble`.

distance_cols

Unquoted names of the columns in `tof_tibble` to use in calculating cell-to-cell distances during the local density estimation for each cell. Defaults to all numeric columns in `tof_tibble`.

distance_function

A string indicating which distance function to use for calculating cell-to-cell distances during local density estimation. Options include "euclidean" (the default) and "cosine".

normalize

A boolean value indicating if the vector of local density estimates should be normalized to values between 0 and 1. Defaults to TRUE.

...

Additional arguments to pass to the `tof_*_density()` function family member corresponding to the chosen `method`.

augment

A boolean value indicating if the output should column-bind the local density estimates of each cell as a new column in `tof_tibble` (TRUE; the default) or if a single-column tibble including only the local density estimates should be returned (FALSE).

method

A string indicating which local density estimation method should be used. Valid values include "mean_distance", "sum_distance", and "spade".

Value

A `tof_tbl` or `tibble` If augment = FALSE, it will have a single column encoding the local density estimates for each cell in `tof_tibble`. If augment = TRUE, it will have ncol(tof_tibble) + 1 columns: each of the (unaltered) columns in `tof_tibble` plus an additional column encoding the local density estimates.

See also

Other local density estimation functions: tof_knn_density(), tof_spade_density()

Examples

sim_data <-
    dplyr::tibble(
        cd45 = rnorm(n = 1000),
        cd38 = rnorm(n = 1000),
        cd34 = rnorm(n = 1000),
        cd19 = rnorm(n = 1000)
    )

# perform the density estimation
tof_estimate_density(tof_tibble = sim_data, method = "spade")
#> # A tibble: 1,000 × 5
#>       cd45   cd38    cd34      cd19 .spade_density
#>      <dbl>  <dbl>   <dbl>     <dbl>          <dbl>
#>  1  0.760   0.167 -0.222  -0.928                 1
#>  2  0.0804 -0.569 -0.324   1.58                  1
#>  3  0.767   0.837  0.473   0.417                 1
#>  4  1.05   -1.14   0.0856 -0.0271                1
#>  5 -1.53   -0.352  0.710   0.000200              1
#>  6  0.371   0.875 -0.0882  0.514                 1
#>  7  0.655  -2.26   0.0972 -0.120                 1
#>  8  0.501   1.31   0.876  -0.0215                1
#>  9 -0.286  -0.307  1.53   -0.0930                1
#> 10 -0.0880 -1.71  -0.516   0.705                 1
#> # ℹ 990 more rows

# perform the density estimation with a smaller search radius around
# each cell
tof_estimate_density(
    tof_tibble = sim_data,
    alpha_multiplier = 2,
    method = "spade"
)
#> # A tibble: 1,000 × 5
#>       cd45   cd38    cd34      cd19 .spade_density
#>      <dbl>  <dbl>   <dbl>     <dbl>          <dbl>
#>  1  0.760   0.167 -0.222  -0.928               1  
#>  2  0.0804 -0.569 -0.324   1.58                1  
#>  3  0.767   0.837  0.473   0.417               1  
#>  4  1.05   -1.14   0.0856 -0.0271              1  
#>  5 -1.53   -0.352  0.710   0.000200            0.8
#>  6  0.371   0.875 -0.0882  0.514               1  
#>  7  0.655  -2.26   0.0972 -0.120               0.4
#>  8  0.501   1.31   0.876  -0.0215              0.9
#>  9 -0.286  -0.307  1.53   -0.0930              0.8
#> 10 -0.0880 -1.71  -0.516   0.705               1  
#> # ℹ 990 more rows