Abstract
It has been hypothesized that the net expression of a gene is determined by the combined effects of various transcriptional system regulators (TSRs). However, characterizing the complexity of regulation of the transcriptome is a major challenge. Principal component analysis on 17,550 heterogeneous human microarray experiments revealed that 50 orthogonal factors (hereafter called TSRs) are able to capture 64% of the variability in expression in a wide range of experimental conditions and tissues. We identified gene clusters controlled in the same direction and show that gene expression can be conceptualized as a process influenced by a fairly limited set of TSRs. Furthermore, TSRs can be linked to biological functions, as we demonstrate a strong relation between TSR-related gene clusters and biological functionality as well as cellular localization, i.e. gene products of similarly regulated genes by a specific TSR are located in identical parts of a cell. Using 3,934 diverse mouse microarray experiments we found striking similarities in transcriptional system regulation between human and mouse. Our results give biological insights into regulation of the cellular transcriptome and provide a tool to characterize expression profiles with highly reliable TSRs instead of thousands of individual genes, leading to a >500-fold reduction of complexity with just 50 TSRs. This might open new avenues for those performing gene expression profiling studies.
Highlights
Biological systems have a layered complexity and it is known that a cell’s activity is modulated by a network of co-regulated gene clusters.[1]
principal component analysis (PCA) demonstrated that 64% of the variance in expression of 13,032 genes was explained by only 50 orthogonal factors, called transcriptional system regulators (TSRs), which means a .500-fold reduction in complexity (Fig. 1A)
Principal component analysis (PCA) on a large number of heterogeneous microarray experiments showed that a maximum of 50 statistically independent transcriptional system regulators (TSRs) can explain the vast majority of biological variance in gene expression in human as well as in mouse
Summary
Biological systems have a layered complexity and it is known that a cell’s activity is modulated by a network of co-regulated gene clusters.[1]. Clustering algorithms are less effective when applied to large datasets of heterogeneous material. Basic clustering algorithms assign each gene to a single cluster of co-regulated genes, whereas it is hypothesized that the net expression of a gene is determined by the combined effects of various transcriptional system regulators (TSRs).[4,5,6] In addition, each level of transcriptional regulation may only be active in certain phenotypes and the remaining phenotypes will contribute to noise.[6] In contrast, principal component analysis (PCA) on a large heterogeneous set could enable us to use correlation structures of strong and weakly expressed genes and could provide a global picture of the dynamics of gene expression on various transcriptional regulation levels.
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