prepare_data: Prepare a directory of .fcs files

In shdam/cyCombine: Robust integration of single-cell cytometry datasets within and across technologies

Prepare a directory of .fcs files

Description

This is a wrapper function that takes you from a directory of .fcs files or a flowset to a transformed tibble.

Usage

prepare_data(
  data_dir = NULL,
  flowset = NULL,
  markers = NULL,
  pattern = "\\.fcs",
  metadata = NULL,
  filename_col = "filename",
  sample_ids = NULL,
  batch_ids = NULL,
  condition = NULL,
  anchor = NULL,
  down_sample = TRUE,
  sample_size = 5e+05,
  sampling_type = "random",
  seed = 473,
  panel = NULL,
  panel_channel = "fcs_colname",
  panel_antigen = "antigen",
  transform = TRUE,
  cofactor = 5,
  derand = TRUE,
  .keep = FALSE,
  clean_colnames = TRUE
)

Arguments

data_dir	Directory containing the .fcs files
flowset	Optional: Prepare a flowset instead of a directory of fcs files
markers	The markers to transform on
pattern	The pattern to use to find the files in the folder
metadata	Optional: Can be either a filename or data.frame of the metadata file. Please give the full path from working directory to metadata file
filename_col	Optional: The column in the metadata containing the fcs filenames. Needed if metadata is given, but sample_ids is not
sample_ids	Optional: If a character, it should be the sample column in the metadata. If its a vector, it should have the same length as the total flowset. If NULL, sample ids will be the file names. If a single value, all rows will be assigned this value.
batch_ids	Optional: If a character, it should be the column in the metadata containing the batch ids. If its a vector, it should have the same length as the total flowset. If a single value, all rows will be assigned this value.
condition	Optional: The column in the metadata containing the condition. Will be used as the covariate in ComBat, but can be specified later. You may use this to add a different column of choice, in case you want to use a custom column in the ComBat model matrix.
anchor	Experimental: The column in the metadata referencing the anchor samples (control references). Will be used as a covariate in ComBat, if specified. Please be aware that this column may be confounded with the condition column. You may use this to add a different column of choice, in case you want to use a custom column in the ComBat model matrix. You may use a custom column name, but it is good practice to add the name to the 'non_markers' object exported by cyCombine, to reduce the risk of unexpected errors.
down_sample	If TRUE, the output will be down-sampled to size sample_size
sample_size	The size to down-sample to
sampling_type	The type of down-sampling to use. "random" to randomly select cells across the entire dataset, "batch_ids" to sample evenly (sample_size) from each batch, or "sample_ids" sample evenly (sample_size) from each sample.
seed	The seed to use for down-sampling
panel	Optional: Panel as a filename or data.frame. Is used to define colnames from the panel_antigen column
panel_channel	Optional: Only used if panel is given. It is the column name in the panel data.frame that contains the channel names
panel_antigen	Optional: Only used if panel is given. It is the column name in the panel data.frame that contains the antigen names
transform	If TRUE, the data will be transformed; if FALSE, it will not.
cofactor	The cofactor to use when transforming
derand	Derandomize. Should be TRUE for CyTOF data, otherwise FALSE.
.keep	Keep all channels. If FALSE, channels that are not transformed are removed
clean_colnames	(Default: TRUE). A logical defining whether column names should be cleaned or not. Cleaning involves removing isotope tags, spaces, dashes, and underscores.

See Also

Other dataprep: compile_fcs(), convert_flowset(), transform_asinh()

Examples

## Not run: 
uncorrected <- data_dir %>%
  prepare_data(metadata = "metadata.csv",
  markers = markers,
  filename_col = "FCS_name",
  batch_ids = "Batch",
  condition = "condition",
  down_sample = FALSE)
  
## End(Not run)

shdam/cyCombine documentation built on June 12, 2022, 11:29 p.m.