fr into K clusters. We set the
cutpoint to be the point at which the density between the first and second
smallest cluster centroids is minimum.flowClust.2d(fr, xChannel, yChannel, filterId = "", K = 2,
usePrior = "no", prior = list(NA), trans = 0, plot = FALSE,
target = NULL, transitional = FALSE, quantile = 0.9,
translation = 0.25, transitional_angle = NULL, min = NULL, max = NULL,
...)flowFrame objectcharacter specifying channels to be gated oncharacter string that identifies the filter created.flowClust. If usePrior is set to no, then the
list is unused.flowClust, this value cannot be 2.2 (number of dimensions) containing the location of
the cluster of interest. See details.flowClust cluster. By default, no.flowClust fit to construct the gatetransitional = TRUE. This argument is ignored if
transitional = FALSE. See detailstransitional = FALSE.xChannel, and the second value
truncates the yChannel. By default, this vector is NULL and is
ignored.xChannel, and the second
value truncates the yChannel. By default, this vector is NULL
and is ignored.flowClustpolygonGate object containing the contour (ellipse) for 2D
gating.target in Euclidean distance. By default,
the largest cluster (i.e., the cluster with the largest proportion of
observations) is selected as the population of interest.We also provide the option of constructing a transitional gate from
the selected population of interest. The location of the gate can be
controlled with the translation argument, which translates the gate
along the major axis of the targest cluster as a function of the appropriate
chi-squared coefficient. The larger translation is, the more gate is
shifted in a positive direction. Furthermore, the width of the
transitional gate can be controlled with the quantile argument.
The direction of the transitional gate can be controlled with the
transitional_angle argument. By default, it is NULL, and we use
the eigenvector of the target cluster that points towards the first
quadrant (has positive slope). If transitional_angle is specified, we
rotate the eigenvectors so that the angle between the x-axis (with the cluster
centroid as the origin) and the major eigenvector (i.e., the eigenvector with
the larger eigenvalue) is transitional_angle.
gate <- flowClust.2d(fr, xChannel = "FSC-A", xChannel = "SSC-A", K = 3) # fr is a flowFrameRun the code above in your browser using DataLab