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Post-process transformed CyTOF data.

Source: R/preprocessing.R
tof_postprocess.Rd

This function transforms a `tof_tibble` of transformed ion counts from a mass cytometer back into something that looks more like an .fcs file that Fluidigm software generates.

Usage

tof_postprocess(
  tof_tibble = NULL,
  channel_cols = where(tof_is_numeric),
  redo_noise = FALSE,
  transform_fun = function(x) rev_asinh(x, shift_factor = 0, scale_factor = 0.2)
)

Arguments

tof_tibble

A `tof_tibble` or a `tibble`.

channel_cols

A vector of non-quoted column names indicating which columns in `tof_tibble` contain protein measurements. Supports tidyselect helpers. If nothing is specified, the default is to transform all numeric columns.

redo_noise

A boolean value indicating whether to add uniform noise that to each CyTOF measurement for aesthetic and visualization purposes. See this paper. Defaults to FALSE

transform_fun

A vectorized function to apply to each column specified by `channel_cols` for post-processing. Defaults to rev_asinh transformation (with a cofactor of 5).

Value

A `tof_tbl` with identical dimensions to the input `tof_tibble`, with all columns specified in channel_cols transformed using `transform_fun` (with noise added or not removed depending on `redo_noise`).

See also

[tof_preprocess()]

Examples


# read in an example .fcs file from tidytof's internal datasets
input_file <- dir(tidytof_example_data("aml"), full.names = TRUE)[[1]]
tof_tibble <- tof_read_data(input_file)

# preprocess all numeric columns with default behavior
# arcsinh transformation with a cofactor of 5
preprocessed_tof_tibble <- tof_preprocess(tof_tibble)

# postprocess all numeric columns to reverse the preprocessing
tof_postprocess(tof_tibble)
#> # A tibble: 100 × 59
#>     Time Event_length `CD45|Y89` `empty|Pd102` `empty|Pd104` `empty|Pd105`
#>    <dbl>        <dbl>      <dbl>         <dbl>         <dbl>         <dbl>
#>  1   Inf    22215276.   6.00e224          8.82      1411688.    Inf       
#>  2   Inf   446205752. Inf               135.         859679.    Inf       
#>  3   Inf    22215276. Inf              2634.            321.      8.51e277
#>  4   Inf    22215276.   2.24e254          3.47         1383.      8.29e254
#>  5   Inf   164149923. Inf               127.          38726.      3.70e280
#>  6   Inf   446205752.   1.14e211        163.            116.      3.29e272
#>  7   Inf    22215276.   2.62e219         79.1           175.      1.82e246
#>  8   Inf   164149923.   5.35e246       1876.         291228.      5.64e305
#>  9   Inf   446205752. Inf                55.3         24727.    Inf       
#> 10   Inf    22215276.   2.38e236          5.05      2010273.    Inf       
#> # ℹ 90 more rows
#> # ℹ 53 more variables: `empty|Pd106` <dbl>, `empty|Pd108` <dbl>,
#> #   `empty|Pd110` <dbl>, `CD61|In113` <dbl>, `CD99|In115` <dbl>,
#> #   `empty|I127` <dbl>, `CD45RA|La139` <dbl>, `CD93|Ce140` <dbl>,
#> #   `CD3_CD19|Pr141` <dbl>, `CCR2|Nd142` <dbl>, `CD117|Nd143` <dbl>,
#> #   `CD123|Nd144` <dbl>, `CD64|Nd145` <dbl>, `CD90|Nd146` <dbl>,
#> #   `CD38|Sm147` <dbl>, `CD34|Nd148` <dbl>, `CEBPa|Sm149` <dbl>, …