Using shRNA experiments to validate gene regulatory networks
Catharina Olsen,
Kathleen Fleming,
Niall Prendergast,
Renee Rubio,
Frank Emmert-Streib,
Gianluca Bontempi,
John Quackenbush,
Benjamin Haibe-Kains
Affiliations
Catharina Olsen
Machine Learning Group, Université Libre de Bruxelles, Brussels, Belgium
Kathleen Fleming
Computational Biology and Functional Genomics Laboratory, Dana-Farber Cancer Institute, Harvard School of Public Health, Boston, MA, USA
Niall Prendergast
Computational Biology and Functional Genomics Laboratory, Dana-Farber Cancer Institute, Harvard School of Public Health, Boston, MA, USA
Renee Rubio
Computational Biology and Functional Genomics Laboratory, Dana-Farber Cancer Institute, Harvard School of Public Health, Boston, MA, USA
Frank Emmert-Streib
Computational Medicine and Statistical Learning Laboratory, Department of Signal Processing, Tampere University of Technology, Korkeakoulunkatu 1, 33720 Tampere, Finland
Gianluca Bontempi
Machine Learning Group, Université Libre de Bruxelles, Brussels, Belgium
John Quackenbush
Computational Biology and Functional Genomics Laboratory, Dana-Farber Cancer Institute, Harvard School of Public Health, Boston, MA, USA
Benjamin Haibe-Kains
Bioinformatics and Computational Genomics Laboratory, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
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.
