covMat: A covariance matrix generating function.
In SPreFuGED: Selecting a Predictive Function for a Given Gene Expression Data

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

For a given number of genes and a proportion of differentially expressed (informative) genes, the this function creates a covariance matrix by sampling variances from an exponential distribution with lambda and the correlation values corrDE and corrOther. Where corrOther is generated from a normal distribution with mean=0, and sigma as SD.

1	covMat(pAll = 1000, pie = 0.05, lambda, corrDE, sigma)

`pAll`	the total number of genes (Default is 1000). For desktop users, we encourage pAll <=2500 for compuational reasons.
`pie`	a value in the interval (0, 1) and corresponds to the proportion of differentially expressed (informative) genes (Defualt is 0.05)
`lambda`	a positive rate parameter for sampling variances from an exponential distribution. The smaller the value the larger the variances.
`corrDE`	a value in the interval [0, 1] specifying the correlation values of DE genes to each other. Half of which are up-regulated (positively associated to survival time) and the others are down-regulated (negatively associated to survival time). The inter-cluster (between up- and down-regulated) genes take negatively signed value of corrDE. The value 0 corresponds to complete independence of these DE genes.
`sigma`	a value in the interval [0, 1] specifying the distribution of correlations within noisy genes and between noisy genes and informative genes. Where 0 means complete indipendence of noisy genes to each other and to informative genes.

This functions assumes three clusters of genes (up-regulated, down-regulated and noisy genes). While the pairwise correlations of the DE genes is a descrete value corrDE, the correlations of the non-DE genes are sampled from a normal distribution with mean zero and SD=sigma. Values beyond the interval [-1, 1] are unformly converted to that interval.

A list containing:

`cov`	the covariance matrix generated
`pie`	the proportion of differentially expressed genes

Victor Lih Jong

Jong VL, Novianti PW, Roes KCB & Eijkemans MJC. Selecting a classification function for class prediction with gene expression data. Bioinformatics (2016) 32(12): 1814-1822

generateGED

myCov<-covMat(pAll=100, lambda=2, corrDE=0.75, sigma=0.25);
#Observe the covariance matrix of 6 genes, 2 each from up-regulated, down-regulated and non-DE
myCov$cov[c(1,2, 4,5, 30,31), c(1,2, 4,5, 30,31)];
myCov$pie;

            [,1]       [,2]        [,3]        [,4]        [,5]        [,6]
[1,]  1.01281920  0.6889321 -0.43178364 -0.33422269  0.01582561  0.14861065
[2,]  0.68893214  0.8331025 -0.39160609 -0.30312320  0.10820713 -0.37078972
[3,] -0.43178364 -0.3916061  0.32724868  0.18998045  0.03084596 -0.38505592
[4,] -0.33422269 -0.3031232  0.18998045  0.19607282 -0.02165560  0.01091141
[5,]  0.01582561  0.1082071  0.03084596 -0.02165560  0.20738303 -0.05366820
[6,]  0.14861065 -0.3707897 -0.38505592  0.01091141 -0.05366820  1.16902805
[1] 0.05