ppa.in.silico {isa2}

Generate in-silico input data for testing the PPA algorithm

Description

This function generates an artificial test data set for the PPA algorithm: two matrices, with common column dimension, containing co-modules of prescribed number, size, signal level, noise level and background noise.

Usage

ppa.in.silico (num.rows1 = 300, num.rows2 = 200, num.cols = 50,
    num.fact = 3, mod.row1.size = round(0.5 * num.rows1/num.fact),
    mod.row2.size = round(0.5 * num.rows2/num.fact),
    mod.col.size = round(0.5 * num.cols/num.fact), 
    noise = 0.1, mod.signal = rep(1, num.fact),
    mod.noise = rep(0, num.fact), overlap.row1 = 0,
    overlap.row2 = overlap.row1, overlap.col = overlap.row1) 

Arguments

num.rows1

The number of rows in the first data matrix.

num.rows2

The number of rows in the second data matrix.

num.cols

The number of columns in both matrices.

num.fact

The number of co-modules to put in the data.

mod.row1.size

The size of the co-modules, the number of rows from the first data matrix. It can be a scalar or a vector, and it is recycled.

mod.row2.size

The size of the co-modules, the number of rows from the second data matrix. It can be a scalar or a vector, and it is recycled.

mod.col.size

The size of the co-modules, the number of columns (from both matrices). It can be a scalar or a vector, and it is recycled.

noise

The level of the background noise to be added to the data matrices. It gives the standard deviation of the normal distribution from which the noise is generated.

mod.signal

The signal level of the co-modules.

mod.noise

The noise levels of the different co-modules. Normally distributed noise with standard deviation mod.noise is added to the data. This is in addition to the background noise.

overlap.row1

The overlap of the modules, for the rows of the first matrix. Zero means no overlap, one means one overlapping row, etc.

overlap.row2

The same as overlap.row1, but for the rows of the second matrix.

overlap.col

The same as overlap.row1, but for the columns of both matrices.

Details

ppa.in.silico creates an artificial data set to test the PPA (or potentially other) algorithm. It creates two data matrices with an overlapping dimension and a checkerboard-like structure. The fields of the checkerboard are the co-modules, and they may have different signal and noise levels, and they may also overlap.

Value

A list with five matrices. The first two are the two data matrices, they have the same number of columns. The last three matrices contain the correct modules, for the rows of the first matrix, the rows of the second matrix, and finally for the common column dimension.

Author(s)

Gabor Csardi [e-mail]

References

Kutalik Z, Bergmann S, Beckmann, J: A modular approach for integrative analysis of large-scale gene-expression and drug-response data Nat Biotechnol 2008 May; 26(5) 531-9.

See Also

See ppa for an easy way of running the PPA

Examples

## Define a function for plotting if we are interactive
if (interactive()) { layout(cbind(1:2,3:4,5:6,7:8)) }
myimage <- function(mat) {
  if (interactive()) {
    par(mar=c(3,2,1,1)); image(mat[[1]])
    par(mar=c(3,2,1,1)); image(mat[[2]])
  }
}

## Co-modules without overlap and noise
silico1 <- ppa.in.silico(100, 100, 100, 10, mod.row1.size=10,
                         mod.row2.size=10, mod.col.size=10, noise=0)
myimage(silico1)

## The same, but with overlap and noise
silico2 <- ppa.in.silico(100, 100, 100, 10, mod.row1.size=10,
                         mod.row2.size=10, mod.col.size=10, noise=0.1,
                         overlap.row1=3)
myimage(silico2)

## Co-modules with different noise levels
silico3 <- ppa.in.silico(100, 100, 100, 5, mod.row1.size=10,
                         mod.row2.size=10, mod.col.size=10, noise=0.01,
                         mod.noise=seq(0.1,by=0.1,length=5))
myimage(silico3)

## Co-modules withe different signal levels
silico4 <- ppa.in.silico(100, 100, 100, 5, mod.row1.size=10,
                         mod.row2.size=10, mod.col.size=10, noise=0.01,
                         mod.signal=seq(1,5,length=5))
myimage(silico4)