groupFisher: Fisher Exact Test for group enrichment analysis of...

In YunhuiQi/TestPMD: Post Selection Inference on Results from Penalized Matrix Decomposition (SCCA only)

Fisher Exact Test for group enrichment analysis of significant loadings

Description

This function generates the heatmap of qvalues from Fisher Exact Test of group enrichment analysis of sinigicant loadings.

Usage

groupFisher(data, loadings, qvalues, feature_groups, alpha, draw = T)

Arguments

data	Data matrix corresponding to the canonical loadings, samples are in rows, variables are in columns.
loadings	A named vector containing the estimated loadings.
qvalues	A vector containing qvalues for the canonical loadings.
feature_groups	A vector indicating the group information of variables.
alpha	Significance level, 0.05 by default.
draw	Whether draw the heatmap, default is TRUE.

Value

A vector containing the qvalues of fisher exact tests

Examples

library(TestPMD)
data("covid")
out <- PMA::CCA(standsdmu(covid$metabolite), standsdmu(covid$protein),
typex = "standard", typez="standard",
penaltyx = 0.9, penaltyz = 0.9, K = 3, standardize = FALSE, trace = FALSE)
pvalue <- CPTloading(X = covid$metabolite, Y = covid$protein, side = "X", K = 1, r = 10,
penalty = "Fixed", rho_x = 0.9, rho_y = 0.9, permutation_no = 100)
feature_groups <- c(rep("A", 61), rep("B", 50))
groupFisher(data = covid$metabolite, loadings = out$u[,1], qvalues = pvalue,
feature_groups = feature_groups, alpha = 0.1, draw = FALSE)

YunhuiQi/TestPMD documentation built on May 5, 2022, 8:23 p.m.