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-κB 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.
Highlights
The great complexity of the human body, in both normal physiology and pathological states, arises from the coordinated expression of genes
We demonstrated that GeneProgram produces superior results when compared with traditional biclustering algorithms on two types of tasks
We used synthetic data experiments to show that GeneProgram was able to correctly recover gene sets that other popular analysis methods could not
Summary
The great complexity of the human body, in both normal physiology and pathological states, arises from the coordinated expression of genes. Sets of genes that are active across diverse cell types or pathological states can give us insight into unexpected functional similarities and involvement of core common pathways. We use a large compendium of short timeseries gene expression datasets measuring the responses of human cells to infectious agents or immune-modulating molecules, to discover a set of biologically interpretable expression programs and to characterize quantitatively the specificity of each program. Such large genome-wide human expression data compendia present several new challenges that do not necessarily arise when analyzing data from simpler organisms.
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