Description Usage Arguments Details Value Author(s) References See Also Examples
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.
1 2 | difconet.noise.inspection(ndata, tdata, sigma=c(0.5, 0.75, 1.25), maxgenes=5000,
corfunc=function(a,b) cor(a,b,method="spearman"))
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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. |
Plots the estimated density of correlation distributions of normal, tumor, and normal after adding sigma levels of noise.
Nothing.
Elpidio Gonzalez and Victor Trevino vtrevino@itesm.mx
Gonzalez-Valbuena and Trevino 2017 Metrics to Estimate Differential Co-Expression Networks Journal Pending volume 00–10
difconet.build.controlled.dataset
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difconet.run
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1 | ## Not run: difconet.noise.inspection(normaldata, tumordata, sigma=0:15/10)
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