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We have used the within- gene coefficient of variation as a measure of reproducibility of a specific platform.
In this paper, we have used the within- gene coefficient of variation as a measure of reproducibility of a specific platform.
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A model coefficient ρ is calculated as the average of the gene coefficients ρt.
The complete regression equation (with intercept and gene coefficients) is provided in the online supplement (Additional file 3).
A cut-off threshold of δ = 0.2 was set, so that gene coefficients with a value less than this were filtered out.
This procedure, known as the Lasso [ 25], resulted in a linear combination of the expression values of 14 genes (the remaining gene coefficients were effectively shrunk to zero) that provided a good approximation of the supervised principal component.
Specifically, we estimated the genomic heritability of the strain-by-gene coefficients.
Further, in order to minimize over-weighting of individual terms, meta-gene coefficients were penalized using Ridge regression.
Genomic heritability estimates for each of the cryptic phenotypes represented by the strain-by-gene coefficients was determined from the genetic and residual error variance components estimated by restricted maximum likelihood, using the function emma.REMLE.
On the basis of the gene-to-gene correlation coefficient calculated from 67 microarray hybridization data points, we performed a network-based analysis.
Likewise resolution into three distinct populations by PCoA (Figs. 7 and 8), and the observed gene differentiation coefficient (0.49) and gene flow statistic (0.51), highlighted some chances of genetic exchange.
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