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Automated discovery of functional generality of human gene expression programs.

Gerber GK, Dowell RD, Jaakkola TS, Gifford DK - PLoS Comput. Biol. (2007)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Electrical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.

ABSTRACT
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.

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Related in: MedlinePlus

Conceptual Overview of the Data Generation Process for Gene Expression in Human TissuesThe GeneProgram probability model can be thought of as a series of “recipes” for constructing the gene expression of tissues, as depicted in this schematic example for a digestive tract. In the upper right, four expression programs (labeled A–D) are shown, consisting of sets of genes (e.g., GA1 represents gene 1 in program A). Cells (circles) throughout the digestive tract choose genes to be expressed probabilistically from the programs. The biological experimenter then collects mRNA by dissecting out the appropriate tissue sample, homogenizing it, lysing cells, and extracting nucleic acids.
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pcbi-0030148-g002: Conceptual Overview of the Data Generation Process for Gene Expression in Human TissuesThe GeneProgram probability model can be thought of as a series of “recipes” for constructing the gene expression of tissues, as depicted in this schematic example for a digestive tract. In the upper right, four expression programs (labeled A–D) are shown, consisting of sets of genes (e.g., GA1 represents gene 1 in program A). Cells (circles) throughout the digestive tract choose genes to be expressed probabilistically from the programs. The biological experimenter then collects mRNA by dissecting out the appropriate tissue sample, homogenizing it, lysing cells, and extracting nucleic acids.

Mentions: The GeneProgram probability model can be understood intuitively as a series of “recipes” for constructing the gene expression of human tissues. Figure 2 presents a schematic diagram of this process, in which we imagine that we are generating the expression data for the digestive tract of a person. The digestive tract is composed of a variety of cell types, with cells of a given type living in different microenvironments, and thus expressing somewhat different sets of genes. We can envision each cell in an organ choosing to express a subset of genes from relevant expression programs; some programs will be shared among many cell types and others will be more specific. As we move along the digestive tract, the cell types present will change, and different expression programs will become active. However, based on the similar physiological functions of the tissues of the digestive tract, we expect more extensive sharing of expression programs than we would between dissimilar organs such as the brain and kidneys. As can be seen in Figure 2, the final steps of our imaginary data generation experiment involve organ dissection, homogenization, cell lysis, and nucleic acid extraction, to yield the total mRNA expressed in the tissue, which is then measured on a DNA microarray.


Automated discovery of functional generality of human gene expression programs.

Gerber GK, Dowell RD, Jaakkola TS, Gifford DK - PLoS Comput. Biol. (2007)

Conceptual Overview of the Data Generation Process for Gene Expression in Human TissuesThe GeneProgram probability model can be thought of as a series of “recipes” for constructing the gene expression of tissues, as depicted in this schematic example for a digestive tract. In the upper right, four expression programs (labeled A–D) are shown, consisting of sets of genes (e.g., GA1 represents gene 1 in program A). Cells (circles) throughout the digestive tract choose genes to be expressed probabilistically from the programs. The biological experimenter then collects mRNA by dissecting out the appropriate tissue sample, homogenizing it, lysing cells, and extracting nucleic acids.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1941755&req=5

pcbi-0030148-g002: Conceptual Overview of the Data Generation Process for Gene Expression in Human TissuesThe GeneProgram probability model can be thought of as a series of “recipes” for constructing the gene expression of tissues, as depicted in this schematic example for a digestive tract. In the upper right, four expression programs (labeled A–D) are shown, consisting of sets of genes (e.g., GA1 represents gene 1 in program A). Cells (circles) throughout the digestive tract choose genes to be expressed probabilistically from the programs. The biological experimenter then collects mRNA by dissecting out the appropriate tissue sample, homogenizing it, lysing cells, and extracting nucleic acids.
Mentions: The GeneProgram probability model can be understood intuitively as a series of “recipes” for constructing the gene expression of human tissues. Figure 2 presents a schematic diagram of this process, in which we imagine that we are generating the expression data for the digestive tract of a person. The digestive tract is composed of a variety of cell types, with cells of a given type living in different microenvironments, and thus expressing somewhat different sets of genes. We can envision each cell in an organ choosing to express a subset of genes from relevant expression programs; some programs will be shared among many cell types and others will be more specific. As we move along the digestive tract, the cell types present will change, and different expression programs will become active. However, based on the similar physiological functions of the tissues of the digestive tract, we expect more extensive sharing of expression programs than we would between dissimilar organs such as the brain and kidneys. As can be seen in Figure 2, the final steps of our imaginary data generation experiment involve organ dissection, homogenization, cell lysis, and nucleic acid extraction, to yield the total mRNA expressed in the tissue, which is then measured on a DNA microarray.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Electrical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus