View source: R/compute_edge_t_stat.R
compute_edge_t_stat | R Documentation |
Given data for two phenotypes and a network, compute the length of each edge and conduct two-sample t tests on all edges.
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()
)
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. |
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.
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
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