Perform Random Forest Analysis Using GatingTree Node Data

This function trains a Random Forest model using node-level percentage data from a FlowObject that includes a GatingTree. The fitted Random Forest model and associated metadata are then stored in the `@Model` slot of the same FlowObject.

Usage

GatingTreeRandomForest(
  train_x,
  node_paths = NULL,
  ntree = 100,
  mtry = NULL,
  expr_group = NULL,
  ctrl_group = NULL,
  class_weight = FALSE,
  filter = "all",
  q = 0.75
)

Arguments

train_x

A FlowObject containing a GatingTree and node-level percentage data in `train_x@Gating$PrunedGatingTreeNodePercentages`.

node_paths

A character vector of node paths to include as predictors. If `NULL`, all columns containing "logdata" are used.

ntree

Number of trees to grow in the Random Forest. Defaults to 100.

mtry

Number of variables randomly sampled at each split in the Random Forest. The defaul is NULL and uses the automatic selection implemented in randomForest.

expr_group

Optional. A character vector to specify the experimental group when class weight is considered.

ctrl_group

Optional. A character vector to specify the control group when class weight is considered.

class_weight

Logical. Whether to consider class weight. The default is FALSE.

filter

A character to spercify how to filter nodes. The option 'all' will consider all enrichment score, entropy, and average proportion of node with equal weight. The option 'enrichment' will use enrichment score only.

q

Number between 0 and 1. Quantile value for the filter parameter to select top nodes. For example, 0.95 will select nodes above the 95th percentile.

Value

The updated FlowObject with a new Random Forest model stored in the `@Model` slot under the name `"RandomForestGatingTree"`.

See also

Other GatingTree Random Forest Analysis: predictGatingTreeRandomForest()

Examples

if (FALSE) { # \dontrun{
  train_x <- GatingTreeRandomForest(train_x)
} # }