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Inference of Causal Networks from Time-Varying Transcriptome Data via Sparse Coding
Temporal 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 dimensi...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3423420/ https://www.ncbi.nlm.nih.gov/pubmed/22916126 http://dx.doi.org/10.1371/journal.pone.0042306 |
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author | Zhang, Kai Han, Ju Groesser, Torsten Fontenay, Gerald Parvin, Bahram |
author_facet | Zhang, Kai Han, Ju Groesser, Torsten Fontenay, Gerald Parvin, Bahram |
author_sort | Zhang, Kai |
collection | PubMed |
description | Temporal 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. |
format | Online Article Text |
id | pubmed-3423420 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-34234202012-08-22 Inference of Causal Networks from Time-Varying Transcriptome Data via Sparse Coding Zhang, Kai Han, Ju Groesser, Torsten Fontenay, Gerald Parvin, Bahram PLoS One Research Article Temporal 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. Public Library of Science 2012-08-20 /pmc/articles/PMC3423420/ /pubmed/22916126 http://dx.doi.org/10.1371/journal.pone.0042306 Text en © 2012 Zhang et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Zhang, Kai Han, Ju Groesser, Torsten Fontenay, Gerald Parvin, Bahram Inference of Causal Networks from Time-Varying Transcriptome Data via Sparse Coding |
title | Inference of Causal Networks from Time-Varying Transcriptome Data via Sparse Coding |
title_full | Inference of Causal Networks from Time-Varying Transcriptome Data via Sparse Coding |
title_fullStr | Inference of Causal Networks from Time-Varying Transcriptome Data via Sparse Coding |
title_full_unstemmed | Inference of Causal Networks from Time-Varying Transcriptome Data via Sparse Coding |
title_short | Inference of Causal Networks from Time-Varying Transcriptome Data via Sparse Coding |
title_sort | inference of causal networks from time-varying transcriptome data via sparse coding |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3423420/ https://www.ncbi.nlm.nih.gov/pubmed/22916126 http://dx.doi.org/10.1371/journal.pone.0042306 |
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