ExprNet (version 1.0.0)

compute_edge_t_stat: Computing edge length, t statistics and t percentiles

Description

Given data for two phenotypes and a network, compute the length of each edge and conduct two-sample t tests on all edges.

Usage

compute_edge_t_stat(
  data_type1,
  data_type2,
  network,
  type1_name = "Type1",
  type2_name = "Type2",
  alpha_t_test = 0.05,
  save_edge_res = FALSE,
  save_dir = here::here()
)

Value

A list consisting of edge_t_stat and edge_dist_mat. edge_t_stat is a data.frame storing results of the t test. Each row gives the following properties of an edge: Vertex1(V1), Vertex2(V2), (mean, sd) of each type, difference in means, t statistic, p-value, percentile of the t statistic. edge_dist_mat is a matrix containing all the sample distances of each edge. Each column is the computed edge length of an edge "vertex1-vertex2" for all samples (Type1 and Type2). The first column is the sample label.

Arguments

data_type1

data.frame for phenotype 1. Each row corresponds to a vertex feature. Each column corresponds to a sample. The first column are the numeric vertex indices.

data_type2

data.frame for phenotype 2, in the same format as data_type1.

network

An igraph-package graph object. It will be converted to an undirected graph by default.

type1_name

The name for phenotype 1, used for naming files of the results.

type2_name

The name for phenotype 2.

alpha_t_test

Significance level of the t test for each edge. It does not affect the computation of AT1 and AT2.

save_edge_res

Logical. Whether the results should be saved. Set to TRUE if you plan to conduct analysis on the t statistics of the edges.

save_dir

Directory to save the results. Results are saved in a subfolder result_ExprNet will be created under the directory.

Examples

library(ExprNet)
network <- network_demo
data_type1 <- data_LGG_demo
data_type2 <- data_GBM_demo

# compute the t-statistics and percentiles
res <- compute_edge_t_stat(data_type1, data_type2, network, type1_name = "LGG", type2_name = "GBM")
res