Zipf's law and human transcriptomes: an explanation with an evolutionary model

Detailed analysis of human gene expression data reveals several patterns of relationship between transcript frequency and abundance rank. In muscle and liver, organs composed primarily of a homogeneous population of differentiated cells, they obey Zipf's law. In cell lines, epithelial tissue and compiled transcriptome data, only high-rankers deviate from it. We propose an evolutionary process model during which expression level changes stochastically proportionally to its intensity, providing a novel interpretation of transcriptome data and of evolutionary constraints on gene expression.