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dc.creatorZhang, K
dc.creatorHan, J
dc.creatorGroesser, T
dc.creatorFontenay, G
dc.creatorParvin, B
dc.date.accessioned2021-01-31T21:12:52Z
dc.date.available2021-01-31T21:12:52Z
dc.date.issued2012-08-20
dc.identifier.issn1932-6203
dc.identifier.issn1932-6203
dc.identifier.doihttp://dx.doi.org/10.34944/dspace/5433
dc.identifier.other22916126 (pubmed)
dc.identifier.urihttp://hdl.handle.net/20.500.12613/5451
dc.description.abstractTemporal analysis of genome-wide data can provide insights into the underlying mechanism of the biological processes in two ways. First, grouping the temporal data provides a richer, more robust representation of the underlying processes that are co-regulated. The net result is a significant dimensional reduction of the genome-wide array data into a smaller set of vocabularies for bioinformatics analysis. Second, the computed set of time-course vocabularies can be interrogated for a potential causal network that can shed light on the underlying interactions. The method is coupled with an experiment for investigating responses to high doses of ionizing radiation with and without a small priming dose. From a computational perspective, inference of a causal network can rapidly become computationally intractable with the increasing number of variables. Additionally, from a bioinformatics perspective, larger networks always hinder interpretation. Therefore, our method focuses on inferring the simplest network that is computationally tractable and interpretable. The method first reduces the number of temporal variables through consensus clustering to reveal a small set of temporal templates. It then enforces simplicity in the network configuration through the sparsity constraint, which is further regularized by requiring continuity between consecutive time points. We present intermediate results for each computational step, and apply our method to a time-course transcriptome dataset for a cell line receiving a challenge dose of ionizing radiation with and without a prior priming dose. Our analyses indicate that (i) the priming dose increases the diversity of the computed templates (e.g., diversity of transcriptome signatures); thus, increasing the network complexity; (ii) as a result of the priming dose, there are a number of unique templates with delayed and oscillatory profiles; and (iii) radiation-induced stress responses are enriched through pathway and subnetwork studies. © 2012 Zhang et al.
dc.format.extente42306-e42306
dc.language.isoen
dc.relation.haspartPLoS ONE
dc.relation.isreferencedbyPublic Library of Science (PLoS)
dc.rightsCC BY
dc.subjectGene Expression Profiling
dc.subjectGenome-Wide Association Study
dc.subjectHumans
dc.subjectRadiation, Ionizing
dc.subjectTranscriptome
dc.titleInference of causal networks from time-varying transcriptome data via sparse coding
dc.typeArticle
dc.type.genreJournal Article
dc.relation.doi10.1371/journal.pone.0042306
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.date.updated2021-01-31T21:12:48Z
refterms.dateFOA2021-01-31T21:12:52Z


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