The contigs generated in Trinity are by default clustered in "trinity components" or gene models, where each gene model represents a putative gene.
In our model, each gene constitutes a set of sextets and within each sextet, only the reference codon is allowed to evolve.
In Kauffman's [1] original NK model, each gene has exactly K inputs and in-degree distribution is therefore a Dirac delta function.
However, by modeling each gene individually, we have shown that TF combinations detected in one cell type could in some cases predict gene expression in other cell types.
In this model, each gene is a nearly insignificant and faceless pawn, but in aggregate these gray pawns form an effective army.
In a Bayesian network model, each gene is considered as a random variable and the edges between a pair of genes represent the conditional dependencies entailed in the network structure.
It is important to highlight that, in this multivariate model, each gene may depend not only on its own past values, but, also, on the past values of the other genes.
When the F/RPKM values are log transformed, the magnitude differences will be decreased and the importance of the preparation factor for modeling each gene expression estimate will appear smaller relative to other factors.
By fitting different evolutionary models to each gene, we show that at each time point more than 80% of genes fit best to models incorporating stabilizing selection, and that for genes whose evolutionarily optimal expression level is the same across all species, selective constraint is maximized during the phylotypic period.
We fit separate models for each gene.
