The major goals of this project are to develop scalable R / Bioconductor software infrastructure and data resources to integrate complex, heterogeneous, and large cancer genomic experiments. The falling cost of genomic assays facilitates collection of multiple data types (e.g., gene and transcript expression, structural variation, copy number, methylation, and microRNA data) from a set of clinical specimens. Furthermore, substantial resources are now available from large consortium activities like The Cancer Genome Atlas (TCGA). Existing analysis pipelines focus on the treatment of a specific data type, leaving a critical need for tools for integrative analysis of multiple genomic assays for locally generated or publicly available data. This proposal adapts R / Bioconductor to meet the increasing conceptual and computational complexity of multi-assay cancer genomic experiments. PMID: 26463000, 25633503
Project Investigators
Levi Waldron PI
Professor of Biostatistics
Ludwig Geistlinger
Senior Computational Biologist, Center for Computational Biomedicine, Harvard Medical School
