Seyed Ali Madani Tonekaboni1,2, Venkata Satya Kumar Manem1,2,3, Nehme El-Hachem4,5, Benjamin Haibe-Kains1,2,6,7,8. 1. Princess Margaret Cancer Centre, University of Toronto, Toronto, ON M5G 1L7, Canada. 2. Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada. 3. Institut Universitaire de Cardiologie et de Pneumologie de Québec, Université Laval, QC G1V 4G5, Canada. 4. Integrative Systems Biology, Institut de Recherches Cliniques de Montréal, Montréal, QC, Canada. 5. Department of Medicine, University of Montreal, Montréal, QC, Canada. 6. Department of Computer Science, University of Toronto, Toronto, ON M5T 3A1, Canada. 7. Ontario Institute of Cancer Research, Toronto, ON M5G 1L7, Canada. 8. Vector Institute, Toronto, ON M5G 1L7, Canada.
Abstract
MOTIVATION: High-throughput molecular profiles of human cells have been used in predictive computational approaches for stratification of healthy and malignant phenotypes and identification of their biological states. In this regard, pathway activities have been used as biological features in unsupervised and supervised learning schemes. RESULTS: We developed SIGN (Similarity Identification in Gene expressioN), a flexible open-source R package facilitating the use of pathway activities and their expression patterns to identify similarities between biological samples. We defined a new measure, the transcriptional similarity coefficient, which captures similarity of gene expression patterns, instead of quantifying overall activity, in biological pathways between the samples. To demonstrate the utility of SIGN in biomedical research, we establish that SIGN discriminates subtypes of breast tumors and patients with good or poor overall survival. SIGN outperforms the best models in DREAM challenge in predicting survival of breast cancer patients using the data from the Molecular Taxonomy of Breast Cancer International Consortium. In summary, SIGN can be used as a new tool for interrogating pathway activity and gene expression patterns in unsupervised and supervised learning schemes to improve prognostic risk estimation for cancer patients by the biomedical research community. AVAILABILITY AND IMPLEMENTATION: An open-source R package is available (https://cran.r-project.org/web/packages/SIGN/).
MOTIVATION: High-throughput molecular profiles of human cells have been used in predictive computational approaches for stratification of healthy and malignant phenotypes and identification of their biological states. In this regard, pathway activities have been used as biological features in unsupervised and supervised learning schemes. RESULTS: We developed SIGN (Similarity Identification in Gene expressioN), a flexible open-source R package facilitating the use of pathway activities and their expression patterns to identify similarities between biological samples. We defined a new measure, the transcriptional similarity coefficient, which captures similarity of gene expression patterns, instead of quantifying overall activity, in biological pathways between the samples. To demonstrate the utility of SIGN in biomedical research, we establish that SIGN discriminates subtypes of breast tumors and patients with good or poor overall survival. SIGN outperforms the best models in DREAM challenge in predicting survival of breast cancerpatients using the data from the Molecular Taxonomy of Breast Cancer International Consortium. In summary, SIGN can be used as a new tool for interrogating pathway activity and gene expression patterns in unsupervised and supervised learning schemes to improve prognostic risk estimation for cancerpatients by the biomedical research community. AVAILABILITY AND IMPLEMENTATION: An open-source R package is available (https://cran.r-project.org/web/packages/SIGN/).
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