gene.pvalue: Calculates p-value for gene-gene interaction
In BUS: Gene network reconstruction

Description Usage Arguments Details Value Author(s) See Also Examples

To calculate p-value for the null hypothesis that there is no gene-gene interaction. For gene expression data with M genes, a p-value matrix under MxM single null hypotheses (each two genes have no interaction) is computed; besides, matrices with correct p-values are output: corrected permutation method using a distribution of MxMxP (P number of permutations) null hypotheses tests (multi.perm.p.value). p-values are calculated based on the adjacency matrix for gene-gene interaction computed by function gene.similarity.

1	gene.pvalue(EXP, measure, net.trim, n.replica = 400)

`EXP`	Gene expression data in form of a matrix. Row stands for genes and column for experiments.
`measure`	Metric used to calculate similarity between genes: "corr" for correlation, "MI" for mutual information.
`net.trim`	Method used to trim the network: "mrnet", "clr", "aracne" and "none" . "mrnet" infers a network using the maximum relevance/minimum redundancy feature selection method; "clr" use the CLR algorithm; "aracne" applies the data processing inequality to all triplets of nodes in order to remove the least significant edge in each triplet. These options come from the package minet, and they are used only for mutual information. "none" indicates no trim operation. It should be chosen when correlation is considered.
`n.replica`	Number of permutations used for the correction of multiple hypothesis testing; default value is 400.

Normally, in a permutation method, we use the empirical distribution of some statistics to estimate the p-value. To get a simple p-value for no interaction between gene i and j, empirical distribution of a vector with length of P (number of replicates) is used; to correct for multiple hypothesis with permutations, an empirical distribution of a vector with length of PxM (M being the number of hypotheses tested) is used.

`single.perm.p.value`	A matrix of single p-values obtained with permutation method + beta distribution for extreme values (for MI) or obtained with the exact distribution computed directly by cor.test (for correlation)
`multi.perm.p.value`	A matrix of corrected p-values obtained with permutation method

Yin Jin, Hesen Peng, Lei Wang, Raffaele Fronza, Yuanhua Liu and Christine Nardini

gene.similarity

data(copasi)
mat=as.matrix(copasi)[1:10,]
rownames(mat)<-paste("G",1:nrow(mat), sep="")
gene.pvalue(mat,measure="MI",net.trim="mrnet")

Loading required package: minet
$single.perm.p.value
        G1     G2     G3     G4     G5     G6     G7     G8     G9    G10
G1  0.0000 0.3250 0.3400 0.2050 0.2300 0.5650 0.9750 0.2850 0.7425 0.4450
G2  0.3250 0.0000 1.0000 0.2775 0.3825 0.2100 0.5650 0.0000 0.7750 0.1950
G3  0.3400 1.0000 0.0000 0.7400 0.2050 0.2625 0.1300 1.0000 0.3250 0.7050
G4  0.2050 0.2775 0.7400 0.0000 0.8075 0.1375 0.2850 0.1325 0.0650 0.2775
G5  0.2300 0.3825 0.2050 0.8075 0.0000 0.5175 0.0750 0.3700 0.3125 0.1425
G6  0.5650 0.2100 0.2625 0.1375 0.5175 0.0000 0.1500 0.2650 0.0375 0.7850
G7  0.9750 0.5650 0.1300 0.2850 0.0750 0.1500 0.0000 1.0000 0.0775 0.6375
G8  0.2850 0.0000 1.0000 0.1325 0.3700 0.2650 1.0000 0.0000 0.4525 0.1950
G9  0.7425 0.7750 0.3250 0.0650 0.3125 0.0375 0.0775 0.4525 0.0000 0.8075
G10 0.4450 0.1950 0.7050 0.2775 0.1425 0.7850 0.6375 0.1950 0.8075 0.0000

$multi.perm.p.value
           G1        G2        G3         G4         G5         G6         G7
G1  0.0000000 0.3153999 0.3357498 0.17746519 0.19900427 0.56425532 0.99005823
G2  0.3153999 0.0000000 0.8978518 0.25085324 0.35592255 0.27413401 0.66894101
G3  0.3357498 0.8978518 0.0000000 0.70089476 0.18882411 0.30993903 0.08830210
G4  0.1774652 0.2508532 0.7008948 0.00000000 0.78894258 0.10714286 0.25486022
G5  0.1990043 0.3559226 0.1888241 0.78894258 0.00000000 0.53419410 0.09006961
G6  0.5642553 0.2741340 0.3099390 0.10714286 0.53419410 0.00000000 0.14123814
G7  0.9900582 0.6689410 0.0883021 0.25486022 0.09006961 0.14123814 0.00000000
G8  0.2618202 0.0000000 0.9858076 0.14938289 0.34043460 0.22957088 0.93505389
G9  0.7016324 0.7802245 0.3093076 0.05970784 0.27104927 0.01585728 0.05713071
G10 0.4539624 0.1813124 0.6670466 0.26331529 0.09324209 0.83451806 0.65338078
           G8         G9        G10
G1  0.2618202 0.70163236 0.45396237
G2  0.0000000 0.78022446 0.18131241
G3  0.9858076 0.30930760 0.66704659
G4  0.1493829 0.05970784 0.26331529
G5  0.3404346 0.27104927 0.09324209
G6  0.2295709 0.01585728 0.83451806
G7  0.9350539 0.05713071 0.65338078
G8  0.0000000 0.49603175 0.17888146
G9  0.4960317 0.00000000 0.86328069
G10 0.1788815 0.86328069 0.00000000