pop.cutoff: Determine optimal cutoffs for immune populations

In jgarces02/FlowCT: A semi-automated workflow for deconvolution of immunophenotypic data and objective reporting on large datasets

Determine optimal cutoffs for immune populations

Description

Once immune cell populations are identified, this function calculates a cutoff from the percentage (or raw counts).

Usage

pop.cutoff(
  fcs.SCE,
  assay.i = "normalized",
  cell.clusters,
  value = "percentage",
  time.var,
  event.var,
  cutoff.type = "maxstat",
  variables
)

Arguments

fcs.SCE	A fcs.SCE object generated through FlowCT::fcs.SCE().
cell.clusters	Name of column containing clusters identified through FlowCT::clustering.flow().
value	String specifying if final resuls should be proportions ("percentage", default) or raw counts ("counts").
time.var	Survival time variable.
event.var	Variable with event censoring.
cutoff.type	Method for calculating survival cutoffs. Available methods are "maxstat" (default)maxstat), "ROC", "quantiles" (i.e., terciles) and "median".
variables	Vector with variables for calculating the cutoff. If nothing is detailed (NULL, default), all immune populations from cell.clusters will be considered.

Details

There four available methods for cytoff calculation:

• median

• quantiles is leveraged on quantile categorization.

• maxstat (default), based on maximally selected ranks statistics. It takes into account the survival time and censoring event for cutoff calculation.

• roc, the classical ROC-based calculation according Youden's index. It considerates the censoring event for categorizing.

Note: for accessing to cutoff values used for categorization... `extract.cutoffs(your_cutoff_object)`

Examples

## Not run: 
ct <- pop.cutoff(fcs.SCE = fcs, cell.clusters = "SOM_named", time.var = "PFS",
    event.var = "PFS_c", cutoff.type = "quantiles")

extract.cutoffs(ct)

## End(Not run)

jgarces02/FlowCT documentation built on March 28, 2023, 12:42 p.m.