difconet.noise.inspection {difconet}

PLOT ESTIMATED CORRELATION DISTRIBUTION AFTER ADDING NOISE

Description

Plots the estimated correlation distribution of a normal dataset after adding different levels of gaussian noise. It is used to estimate the level of noise needed to be added to a normal dataset to match the correlation distribution of a tumor dataset. This assumes that the correlation distribution of the tumor dataset is sharper around zero.

Usage

difconet.noise.inspection(ndata, tdata, sigma=c(0.5, 0.75, 1.25), maxgenes=5000, 
  corfunc=function(a,b) cor(a,b,method="spearman"))

Arguments

ndata

The normal dataset. Rows are genes and columns are samples.

tdata

The tumor dataset. Rows are genes and columns are samples. Rows of tumor and normal datasets should be the same.

sigma

Levels of gaussian noise to be added (at zero mean).

maxgenes

Number of genes used to estimate the correlation distribution. If the number of rows in normal/tumor datasets are larger than maxgenes, maxgenes random genes are used for the estimation.

corfunc

Correlation method used.

Details

Plots the estimated density of correlation distributions of normal, tumor, and normal after adding sigma levels of noise.

Value

Nothing.

Author(s)

Elpidio Gonzalez and Victor Trevino [e-mail]

References

Gonzalez-Valbuena and Trevino 2017 Metrics to Estimate Differential Co-Expression Networks Journal Pending volume 00–10

See Also

difconet.build.controlled.dataset. difconet.run.

Examples

## Not run: difconet.noise.inspection(normaldata, tumordata, sigma=0:15/10)