corTest (version 1.0.7)

construct_network: Construct Differential Correlation Network

Description

Construct differential correlation network with expressionSet,st5 is recommand for testing equal correlation.

Usage

construct_network(es,
                  cor_method = "st5",
                  var.grp,
                  pseudo_adjust_cutoff = FALSE,
                  pAdjMethod = 'fdr',
                  cutoff = 0.05,
                  nPseudo = 25)

Arguments

es

an ExpressionSet object of microRNA dataset

cor_method

a string represents the method for equal correlation, 'st5' is recommand.

var.grp

character. phenotype variable name indicating case-control status,0 as control, 1 as case.

pseudo_adjust_cutoff

if the value is TRUE, pseudo probes will be used for setting the cutoff of p-value for differential corrlation test. Otherwise, adjusted p-value will be compared with cutoff.

pAdjMethod

if pAdjMethod='none', the function will not do mutiple testing adjustment. If pAdjMethod="fdr"/"BH"/"BY"/"holm"/"hochberg"/"hommel"/"bonferroni"/"BH"/"BY", the specific method will be used for adjusting p-value. pAdjMethod will only be used when pseudo_adjust_cutoff=FALSE

cutoff

if p value is smaller than the cutoff, there will be an edge between the two nodes. cutoff will only be used when pseudo_adjust_cutoff=FALSE.

nPseudo

if pseudo_adjust_cutoff=TRUE, then nPseudo genes will be randomly chosen to produce pseudo genes (i.e., non-differentially correlated with other genes between cases and controls). We will use the minimum of the p-values of these pseudo genes as the cutoff of p-values for differential correlation analysis.

Value

A list with 6 elements:

my_graph

obtained network as igraph object

my_dat

obtained netork as data frame with 3 columns: edge id, node_id1,node_id2

pvalMat

raw p-values for testing differential correlation for each pair of genes

pAdjMat

adjusted p-values for testing differential correlation for each pair of genes

pvalPseudo

p-values for testing differential correlation between pseudo genes and other genes

alpha1

cutoff for p-values for testing differential correlation

References

Danyang Yu, Zeyu Zhang, Kimberly Glass, Jessica Su, Dawn L. DeMeo, Kelan Tantisira, Scott T. Weiss, Weiliang Qiu(corresponding author). New Statistical Methods for Constructing Robust Differential Correlation Networks to characterize the interactions among microRNAs. Scientific Reports 9, Article number: 3499 (2019)

Examples

# NOT RUN {
set.seed(1234567)
res = generate_data(n1 = 50, n2 = 60, p1 = 5, p2 = 50)
es = res$es
print(es)

covCtrl = res$covCtrl
covCase = res$covCase

# we expect cov for 1st 5 genes are different between cases and controls
print(round(covCtrl[1:5, 1:5], 2))
print(round(covCase[1:5, 1:5], 2))

# we expect cov for other genes are same between cases and controls
print(round(covCtrl[6:10, 6:10], 2))
print(round(covCase[6:10, 6:10], 2))

res2 = construct_network(es = es,
                  cor_method = "st5",
                  pseudo_adjust_cutoff = FALSE,
                  var.grp = "grp",
                  pAdjMethod = 'fdr',
                  cutoff = 0.05,
                  nPseudo = 25)

print(res2$graph)
print(res2$network_dat)

# }