rfLabel | R Documentation |
Returns the final label assignments for a parameter using a random forest
rfLabel(
x,
type = c("bead", "doublet", "debris", "dead"),
loss = c("auc", "class"),
n = 4000,
standardize = TRUE
)
x |
A |
type |
Identifies the type of label that is being modeled. Must be 'bead', 'doublet', 'debris', or 'dead'. |
loss |
Specifies the type of loss used to tune the GBM. Can be either "auc" for the area under the curve or "class" for classification error. |
n |
number of observations in training dataset. |
standardize |
Indicates if the data should be standardized. Because the data are on different scales, it should be standardized for this analysis because the variables are on different scales. |
rfLabel
uses a random forest to compute the final labels
for the specified parameter type (bead, doublet, debris, or dead). This step
cannot be completed until the corresponding initialization function
(initialBead
, initialDebris
, initialDoublet
, or
initialDead
) is done on the SingleCellExperiment
created by
readCytof
.
The random forest uses the defaults from randomForest
and then
predicted values are computed for all of the events in x
. If the
predicted probability for the label type is greater than 0.5, the label is
changed to the specified type. However, if an observation already has a
label other than 'cell' in the label
variable, it will not be changed.
The predicted probabilities for all of the observations are stored in the
variable associated with that type in the probs
object of x
for further analysis. Thus, it is possible to have a probability greater
than 0.5 for 'debris' but still have a label of 'bead' if an observation
was classified as a bead prior to classifying the debris.
An updated SingleCellExperiment
is returned with the labels
for the parameter of interest (bead, doublet, debris, or dead) added to
the label
object of the SingleCellExperiment
and the
probabilities for the event type added to the probs
object of the
SingleCellExperiment
.
data("raw_data", package = "CATALYST")
sce <- readCytof(raw_data, beads = "Beads", viability = c("cisPt1", "cisPt2"))
sce <- initialBead(sce)
sce <- rfLabel(sce, type = "bead")
head(probs(sce))
table(label(sce))
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