gate_mindensity.Rd
We fit a kernel density estimator to the cells in the flowFrame
and
identify the two largest peaks. We then
select as the cutpoint the value at which the minimum density is attained
between the two peaks of interest.
gate_mindensity( fr, channel, filterId = "", positive = TRUE, pivot = FALSE, gate_range = NULL, min = NULL, max = NULL, peaks = NULL, ... )
fr  a 

channel  TODO 
filterId  TODO 
positive  If 
pivot  logical value. If 
gate_range  numeric vector of length 2. If given, this sets the bounds
on the gate applied. If no gate is found within this range, we set the gate to
the minimum value within this range if 
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 

...  Additional arguments for peak detection. 
a rectangleGate
object based on the minimum density cutpoint
In the default case, the two peaks of interest are the two largest peaks
obtained from the link{density}
function. However, if pivot
is
TRUE
, we choose the largest peak and its neighboring peak as the two
peaks of interest. In this case, the neighboring peak is the peak immediately
to the left of the largest peak if positive
is TRUE
. Otherwise,
the neighboring peak is selected as the peak to the right.
In the special case that there is only one peak, we are conservative and set
the cutpoint as the min(x)
if positive
is TRUE
, and the
max(x)
otherwise.
if (FALSE) { gate < gate_mindensity(fr, channel = "APCA") # fr is a flowFrame }