An improved version of mindensity used to determines a cutpoint as the minimum point of a kernel density estimate between two peaks.

Analogous to the original openCyto::mindensity(), mindensity2 operates on a standard flowFrame. Its behavior is closely modeled on the original mindensity() whenever possible. However, the underlying peak-finding algorithm (improvedMindensity) behaves significantly differently.

gate_mindensity2(fr, channel, filterId = "", pivot = FALSE,
  gate_range = NULL, min = NULL, max = NULL, peaks = NULL, ...)

Arguments

fr	a flowFrame object
channel	the channel to operate on
filterId	a name to refer to this filter
pivot	logical value. If TRUE, we choose as the two peaks the largest peak and its neighboring peak. See details.
gate_range	numeric vector of length 2. If given, this sets the bounds on the gate applied.
min	a numeric value that sets the lower boundary for data filtering
max	a numeric value that sets the upper boundary for data filtering
peaks	numeric vector. If not given , then perform peak detection first by .find_peaks
...	Additional arguments for peak detection.

Value

a rectangleGate object based on the minimum density cutpoint

Examples

if (FALSE) {
 gate <- gate_mindensity2(fr, channel = "APC-A") # fr is a flowFrame
}