gate_singlet {flowStats} | R Documentation |
We construct a singlet gate by applying a robust linear model with
rlm
. By default, we model the forward-scatter height
(FSC-H)as a function of forward-scatter area (FSC-A). If sidescatter
is given, forward-scatter height is as a function of area
+
sidescatter
+ sidescatter / area
.
gate_singlet( x, area = "FSC-A", height = "FSC-H", sidescatter = NULL, prediction_level = 0.99, subsample_pct = NULL, wider_gate = FALSE, filterId = "singlet", maxit = 5, ... ) singletGate( x, area = "FSC-A", height = "FSC-H", sidescatter = NULL, prediction_level = 0.99, subsample_pct = NULL, wider_gate = FALSE, filterId = "singlet", maxit = 5, ... )
x |
a |
area |
character giving the channel name that records the signal intensity as peak area |
height |
character giving the channel name that records the signal intensity as peak heightchannel name of height |
sidescatter |
character giving an optional channel name for the sidescatter signal. By default, ignored. |
prediction_level |
a numeric value between 0 and 1 specifying the level to use for the prediction bands |
subsample_pct |
a numeric value between 0 and 1 indicating the percentage
of observations that should be randomly selected from |
wider_gate |
logical value. If |
filterId |
the name for the filter that is returned |
maxit |
the limit on the number of IWLS iterations passed to |
... |
additional arguments passed to |
Because rlm
relies on iteratively reweighted least
squares (IRLS), the runtime to construct a singlet gate is dependent in part
on the number of observations in x
. To improve the runtime, we provide
an option to subsample randomly a subset of x
. A percentage of
observations to subsample can be given in subsample_pct
. By default, no
subsampling is applied.
a polygonGate
object with the singlet gate
## Not run: # fr is a flowFrame sg <- gate_singlet(fr, area = "FSC-A", height = "FSC-H") sg # plot the gate xyplot(`FSC-H` ~ `FSC-A`, fr, filter = sg) ## End(Not run)