tpr: Calculate true positive rate (TPR) on a tree structure

In fionarhuang/treeclimbR: An algorithm to find optimal signal levels in a tree

Calculate true positive rate (TPR) on a tree structure

Description

Calculate the true positive rate on a tree structure, at either leaf or node level.

Usage

tpr(tree, truth, found, only.leaf = TRUE)

Arguments

tree	A phylo object.
truth	True signal nodes (e.g., nodes that are truly differentially abundant between experimental conditions). Note: when the TPR is requested at the leaf level (only.leaf = TRUE), the descendant leaves of the given nodes will be found and the TPR will be estimated on the leaf level.
found	Detected signal nodes (e.g., nodes that have been found to be differentially abundant via a statistical testing procedure). Note: when the TPR is requested at the leaf level (only.leaf = TRUE), the descendant leaves of the given nodes will be found out and the TPR will be estimated on the leaf level.
only.leaf	A logical scalar. If TRUE, the false discovery rate is calculated at the leaf (tip) level; otherwise it is calculated at the node level.

Value

The estimated true positive rate.

Author(s)

Ruizhu Huang

Examples

suppressPackageStartupMessages({
    library(ggtree)
    library(TreeSummarizedExperiment)
})

data("tinyTree")

## Two branches are truly differential
ggtree(tinyTree, branch.length = "none") +
   geom_text2(aes(label = node)) +
   geom_hilight(node = 16, fill = "orange", alpha = 0.3) +
   geom_hilight(node = 13, fill = "blue", alpha = 0.3)

## TPR at the leaf level if nodes 14 and 15 are called differential  (7/8)
tpr(tree = tinyTree, truth = c(16, 13),
    found = c(15, 14), only.leaf = TRUE)

## TPR at the node level if nodes 14 and 15 are called differential (12/14)
tpr(tree = tinyTree, truth = c(16, 13),
    found = c(15, 14), only.leaf = FALSE)

fionarhuang/treeclimbR documentation built on May 19, 2024, 1:18 p.m.