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Deriving transcriptional programs and functional processes from gene expression databases.

ABSTRACT: A system-wide approach to revealing the underlying molecular state of a cell is a long-standing biological challenge. Developed over the last decade, gene expression profiles possess the characteristics of such an assay. They have the capacity to reveal both underlying molecular events as well as broader phenotypes such as clinical outcomes. To interpret these profiles, many gene sets have been developed that characterize biological processes. However, the full potential of these gene sets has not yet been achieved. Since the advent of gene expression databases, many have posited that they can reveal properties of activities that are not evident from individual datasets, analogous to how the expression of a single gene generally cannot reveal the activation of a biological process.To address this issue, we have developed a high-throughput method to mine gene expression databases for the regulation of gene sets. Given a set of genes, we scored it against each gene expression dataset by looking for enrichment of co-regulated genes relative to an empirical null distribution. After validating the method, we applied it to address two biological problems. First, we deciphered the E2F transcriptional network. We confirmed that true transcriptional targets exhibit a distinct regulatory profile across a database. Second, we leveraged the patterns of regulation across a database of gene sets to produce an automatically generated catalog of biological processes. These demonstrations revealed the power of a global analysis of the data contained within gene expression databases, and the potential for using them to address biological questions.

SUBMITTER: Chang JT

PROVIDER: S-EPMC3324522 | biostudies-other | 2012 Apr

REPOSITORIES: biostudies-other

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Deriving transcriptional programs and functional processes from gene expression databases.

Chang Jeffrey T JT

Bioinformatics (Oxford, England) 20120308 8

<h4>Motivation</h4>A system-wide approach to revealing the underlying molecular state of a cell is a long-standing biological challenge. Developed over the last decade, gene expression profiles possess the characteristics of such an assay. They have the capacity to reveal both underlying molecular events as well as broader phenotypes such as clinical outcomes. To interpret these profiles, many gene sets have been developed that characterize biological processes. However, the full potential of th ...[more]

PMID: 22408194

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Project description:An important research problem in computational biology is the identification of expression programs, sets of co-expressed genes orchestrating normal or pathological processes, and the characterization of the functional breadth of these programs. The use of human expression data compendia for discovery of such programs presents several challenges including cellular inhomogeneity within samples, genetic and environmental variation across samples, uncertainty in the numbers of programs and sample populations, and temporal behavior. We developed GeneProgram, a new unsupervised computational framework based on Hierarchical Dirichlet Processes that addresses each of the above challenges. GeneProgram uses expression data to simultaneously organize tissues into groups and genes into overlapping programs with consistent temporal behavior, to produce maps of expression programs, which are sorted by generality scores that exploit the automatically learned groupings. Using synthetic and real gene expression data, we showed that GeneProgram outperformed several popular expression analysis methods. We applied GeneProgram to a compendium of 62 short time-series gene expression datasets exploring the responses of human cells to infectious agents and immune-modulating molecules. GeneProgram produced a map of 104 expression programs, a substantial number of which were significantly enriched for genes involved in key signaling pathways and/or bound by NF-kappaB transcription factors in genome-wide experiments. Further, GeneProgram discovered expression programs that appear to implicate surprising signaling pathways or receptor types in the response to infection, including Wnt signaling and neurotransmitter receptors. We believe the discovered map of expression programs involved in the response to infection will be useful for guiding future biological experiments; genes from programs with low generality scores might serve as new drug targets that exhibit minimal "cross-talk," and genes from high generality programs may maintain common physiological responses that go awry in disease states. Further, our method is multipurpose, and can be applied readily to novel compendia of biological data.

Dataset Information

Deriving transcriptional programs and functional processes from gene expression databases.

Publications

Deriving transcriptional programs and functional processes from gene expression databases.

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