ac: Artificial Components for Gene Expression Data
In acde: Artificial Components Detection of Differentially Expressed Genes

Description Usage Arguments Details Value Author(s) References Examples

Computes the artificial components for gene expression data between two conditions for a single time point.

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ac(Z, design)

ac2(Z, design)

`Z`	a numeric matrix or data.frame with n rows and p columns representing genes' expression levels. The rows of Z correspond to the genes in the experiment, and the columns correspond to the replicates. Treatment replicates are to the left, control replicates to the right.
`design`	a vector of length p with 1's for the treatment replicates and 2's for the control replicates (1, …, 1, 2, …, 2).

This function computes the artificial components of Z, based on the specified design vector. First, the function scales Z so that its columns have zero mean and unit variance. Then computation of the artificial components ψ[1] and ψ[2] is performed as ψ[1] = Zv[1], where v[1] = (1, … , 1) / sqrt(p), and ψ[2] = Zv[2], where v[2] = (1, … , 1, -1, … , -1 ) / sqrt(p*p[1]*(p-p[1])). Here, p[1] is the number of treatment replicates, and v[2] has p[1] positive and p-p[1] negative entries.

ac returns a matrix with the artificial components ψ[1] and ψ[2] in the columns.

ac2 returns a matrix with the second artificial component ψ[2] in the only column.

Juan Pablo Acosta (jpacostar@unal.edu.co).

Acosta, J. P. (2015) Strategy for Multivariate Identification of Differentially Expressed Genes in Microarray Data. Unpublished MS thesis. Universidad Nacional de Colombia, Bogot\'a.

## Computes the artificial components for the 
## phitophthora infestans data at 60 hai.
psi <- ac(phytophthora[[4]], c(rep(1,8), rep(2,8)))
plot(x=psi[,1], y=psi[,2])