Neo4j in The Cancer Genome Atlas
The Cancer Genome Atlas (TCGA) provides an unprecedented opportunity to take an integrated approach toward a systems level understanding of regulatory disruptions in cancer. Such disruptions and their consequences are intertwined within complex dynamical networks through a multitude of interactions among different types of biomolecules. Understanding such relationships requires multivariate analysis methods that can be effective in the context of highly heterogeneous data, measurement uncertainty, and missing data.
This web-based tool provides for the exploration of data from TCGA. It can be used to explore the following:
- The cancer comparison data sets which give information about common gene disruptions across different cancers.
- Associations among multiple types of features (e.g., gene expression, methylation, copy number variation, clinical), computed using random forest regression and displayed within an interactive Circvis tool.
- Individual genome aberrations identified through the analysis of structural variations using FastBreak.