Using shRNA experiments to validate gene regulatory networks
Quantitative validation of gene regulatory networks (GRNs) inferred from observational expression data is a difficult task usually involving time intensive and costly laboratory experiments. We were able to show that gene knock-down experiments can be used to quantitatively assess the quality of lar...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2015-06-01
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| Series: | Genomics Data |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213596015000288 |
| _version_ | 1818821638981943296 |
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| author | Catharina Olsen Kathleen Fleming Niall Prendergast Renee Rubio Frank Emmert-Streib Gianluca Bontempi John Quackenbush Benjamin Haibe-Kains |
| author_facet | Catharina Olsen Kathleen Fleming Niall Prendergast Renee Rubio Frank Emmert-Streib Gianluca Bontempi John Quackenbush Benjamin Haibe-Kains |
| author_sort | Catharina Olsen |
| collection | DOAJ |
| description | Quantitative validation of gene regulatory networks (GRNs) inferred from observational expression data is a difficult task usually involving time intensive and costly laboratory experiments. We were able to show that gene knock-down experiments can be used to quantitatively assess the quality of large-scale GRNs via a purely data-driven approach (Olsen et al. 2014). Our new validation framework also enables the statistical comparison of multiple network inference techniques, which was a long-standing challenge in the field.
In this Data in Brief we detail the contents and quality controls for the gene expression data (available from NCBI Gene Expression Omnibus repository with accession number GSE53091) associated with our study published in Genomics (Olsen et al. 2014). We also provide R code to access the data and reproduce the analysis presented in this article. |
| first_indexed | 2024-12-18T23:11:23Z |
| format | Article |
| id | doaj.art-8967cfc2aaf746ea84dcd33facda8890 |
| institution | Directory Open Access Journal |
| issn | 2213-5960 |
| language | English |
| last_indexed | 2024-12-18T23:11:23Z |
| publishDate | 2015-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Genomics Data |
| spelling | doaj.art-8967cfc2aaf746ea84dcd33facda88902022-12-21T20:48:20ZengElsevierGenomics Data2213-59602015-06-014C12312610.1016/j.gdata.2015.03.011Using shRNA experiments to validate gene regulatory networksCatharina Olsen0Kathleen Fleming1Niall Prendergast2Renee Rubio3Frank Emmert-Streib4Gianluca Bontempi5John Quackenbush6Benjamin Haibe-Kains7Machine Learning Group, Université Libre de Bruxelles, Brussels, BelgiumComputational Biology and Functional Genomics Laboratory, Dana-Farber Cancer Institute, Harvard School of Public Health, Boston, MA, USAComputational Biology and Functional Genomics Laboratory, Dana-Farber Cancer Institute, Harvard School of Public Health, Boston, MA, USAComputational Biology and Functional Genomics Laboratory, Dana-Farber Cancer Institute, Harvard School of Public Health, Boston, MA, USAComputational Medicine and Statistical Learning Laboratory, Department of Signal Processing, Tampere University of Technology, Korkeakoulunkatu 1, 33720 Tampere, FinlandMachine Learning Group, Université Libre de Bruxelles, Brussels, BelgiumComputational Biology and Functional Genomics Laboratory, Dana-Farber Cancer Institute, Harvard School of Public Health, Boston, MA, USABioinformatics and Computational Genomics Laboratory, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, CanadaQuantitative validation of gene regulatory networks (GRNs) inferred from observational expression data is a difficult task usually involving time intensive and costly laboratory experiments. We were able to show that gene knock-down experiments can be used to quantitatively assess the quality of large-scale GRNs via a purely data-driven approach (Olsen et al. 2014). Our new validation framework also enables the statistical comparison of multiple network inference techniques, which was a long-standing challenge in the field. In this Data in Brief we detail the contents and quality controls for the gene expression data (available from NCBI Gene Expression Omnibus repository with accession number GSE53091) associated with our study published in Genomics (Olsen et al. 2014). We also provide R code to access the data and reproduce the analysis presented in this article.http://www.sciencedirect.com/science/article/pii/S2213596015000288Knock-downGene expressionMicroarrayColon cancershRNA |
| spellingShingle | Catharina Olsen Kathleen Fleming Niall Prendergast Renee Rubio Frank Emmert-Streib Gianluca Bontempi John Quackenbush Benjamin Haibe-Kains Using shRNA experiments to validate gene regulatory networks Genomics Data Knock-down Gene expression Microarray Colon cancer shRNA |
| title | Using shRNA experiments to validate gene regulatory networks |
| title_full | Using shRNA experiments to validate gene regulatory networks |
| title_fullStr | Using shRNA experiments to validate gene regulatory networks |
| title_full_unstemmed | Using shRNA experiments to validate gene regulatory networks |
| title_short | Using shRNA experiments to validate gene regulatory networks |
| title_sort | using shrna experiments to validate gene regulatory networks |
| topic | Knock-down Gene expression Microarray Colon cancer shRNA |
| url | http://www.sciencedirect.com/science/article/pii/S2213596015000288 |
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