Dataset Information

Functional clustering of periodic transcriptional profiles through ARMA(p,q).

ABSTRACT: BACKGROUND:Gene clustering of periodic transcriptional profiles provides an opportunity to shed light on a variety of biological processes, but this technique relies critically upon the robust modeling of longitudinal covariance structure over time. METHODOLOGY:We propose a statistical method for functional clustering of periodic gene expression by modeling the covariance matrix of serial measurements through a general autoregressive moving-average process of order (p,q), the so-called ARMA(p,q). We derive a sophisticated EM algorithm to estimate the proportions of each gene cluster, the Fourier series parameters that define gene-specific differences in periodic expression trajectories, and the ARMA parameters that model the covariance structure within a mixture model framework. The orders and of the ARMA process that provide the best fit are identified by model selection criteria. CONCLUSIONS:Through simulated data we show that whenever it is necessary, employment of sophisticated covariance structures such as ARMA is crucial in order to obtain unbiased estimates of the mean structure parameters and increased precision of estimation. The methods were implemented on recently published time-course gene expression data in yeast and the procedure was shown to effectively identify interesting periodic clusters in the dataset. The new approach will provide a powerful tool for understanding biological functions on a genomic scale.

SUBMITTER: Li N

PROVIDER: S-EPMC2855703 | biostudies-literature | 2010 Apr

REPOSITORIES: biostudies-literature

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Functional clustering of periodic transcriptional profiles through ARMA(p,q).

Li Ning N McMurry Timothy T Berg Arthur A Wang Zhong Z Berceli Scott A SA Wu Rongling R

PloS one 20100416 4

<h4>Background</h4>Gene clustering of periodic transcriptional profiles provides an opportunity to shed light on a variety of biological processes, but this technique relies critically upon the robust modeling of longitudinal covariance structure over time.<h4>Methodology</h4>We propose a statistical method for functional clustering of periodic gene expression by modeling the covariance matrix of serial measurements through a general autoregressive moving-average process of order (p,q), the so-c ...[more]

PMID: 20419127

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Project description:An understanding of the regulatory mechanisms responsible for pluripotency in embryonic stem cells (ESCs) is critical for realizing their potential in medicine and science. Significant similarities exist among ESCs harvested from different species, yet major differences have also been observed. Here, by cross-species analysis of a large set of functional categories and all transcription factors and growth factors, we reveal conserved and divergent functional landscapes underlining fundamental and species-specific mechanisms that regulate ESC development. Global transcriptional trends derived from all expressed genes, instead of differentially expressed genes alone, were examined, allowing for a higher discriminating power in the functional portrait. We demonstrate that cross-species correlation of transcriptional changes that occur upon ESC differentiation is a powerful predictor of ESC-important biological pathways and functional cores within a pathway. Hundreds of functional modules, as defined by Gene Ontology, were associated with conserved expression patterns but bear no overt relationship to ESC development, suggestive of new mechanisms critical to ESC pluripotency. Yet other functional modules were not conserved; instead, they were significantly up-regulated in ESCs of either species, suggestive of species-specific regulation. The comparisons of ESCs across species and between human ESCs and embryonal carcinoma stem cells suggest that while pluripotency as an essential function in multicellular organisms is conserved throughout evolution, mechanisms primed for differentiation are less conserved and contribute substantially to the differences among stem cells derived from different tissues or species. Our findings establish a basis for defining the "stemness" properties of ESCs from the perspective of functional conservation and variation. The data and analyses resulting from this study provide a framework for new hypotheses and research directions and a public resource for functional genomics of ESCs.

Dataset Information

Functional clustering of periodic transcriptional profiles through ARMA(p,q).

Publications

Functional clustering of periodic transcriptional profiles through ARMA(p,q).

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