Pseudo-bulking with optimal number of Pseudotime based bins

`sc.squeeze()` discretizes a continuous time series column into bins of equal size using entropy-based binning method. It automatically calculates the optimal number of bins using one of the supported methods.

sc.squeeze(
  scmpObj,
  ptime_col = scmpObj@Parameters@ptime_col,
  path_col = scmpObj@Parameters@path_col,
  bin_method = "Sturges",
  drop_fac = 1,
  verbose = FALSE,
  bin_mem_col = "scmp_bin_members",
  bin_col = "scmp_bin",
  bin_size_col = "scmp_bin_size",
  bin_ptime_col = "scmp_binned_pseudotime",
  split_bins = FALSE,
  prune_bins = FALSE,
  assay_name = "counts",
  drop_trails = FALSE,
  aggregate = "sum",
  fill_gaps = FALSE,
  additional_params = list(use_unique_time_points = FALSE)
)

Arguments

scmpObj

An object of class ScMaSigPro.

ptime_col

A character string representing the column name for inferred Pseudotime values in 'Sparse' data. (Default is "Pseudotime").

path_col

A character string representing the column name for branching path assignment in 'Sparse' or 'Dense' data. (Default is `path_prefix`).

bin_method

A character string representing the algorithm used for binning. Available options: "Freedman.Diaconis", "Sqrt", "Sturges", "Rice", "Doane", and "Scott.Normal". (Default = "Sturges")

drop_fac

A numeric value specifying the factor by which to adjust the number of bins if the initial binning results in too many/few bins. (Default = 1).

verbose

Print detailed output in the console. (Default is TRUE)

bin_mem_col

A character string representing the name of the column in which cells per bin are stored. (Default is "scmp_bin_members").

bin_col

A character string representing the name of the column in which bin labels are stored. (Default is "scmp_bin").

bin_size_col

A character string representing the name of the column in which bin sizes per bin are stored. (Default is "scmp_bin_size").

bin_ptime_col

A character string representing the column name for binned Pseudotime values in 'Dense' data. (Default is "scmp_binned_pseudotime").

split_bins

If bin sizes are greater than mean + sd, split the bin into smaller bins by re-running the sc.squeeze() function. (Default = FALSE).

prune_bins

If bin sizes are smaller than mean - sd, remove the bin. (Default = FALSE).

assay_name

Name of the Assay in sparse data from which the counts are used. (Default = "counts").

drop_trails

If the paths have different lengths of the binned pseudotime, drop the bins from the path with more bins. (Default = FALSE).

aggregate

A character string specifying the method to aggregate counts within each cluster. Available options are 'mean' or 'sum'. (Default = "sum").

fill_gaps

If corresponding bin is missing for a time-point, pull the successive bins and fill the gaps.

additional_params

Pass additional parameters as a named list. See examples

Value

An object of class ScMaSigPro, with updated `Dense` slot.

See also

estBinSize, discretize, create_range

Author

Priyansh Srivastava spriyansh29@gmail.com