Visualizing genomic characteristics across an RNA-Seq based reference landscape of normal and neoplastic brain

Author(s): Sonali Arora

Affiliation(s): Fred Hutch Cancer Center

Social media: https://twitter.com/Sonali_bioc

In order to better understand the relationship between normal and neoplastic brain, we combined five publicly available large-scale datasets, correcting for batch effects and applying Uniform Manifold Approximation and Projection (UMAP) to RNA-Seq data. We assembled a reference Brain-UMAP including 702 adult gliomas, 802 pediatric tumors and 1409 healthy normal brain samples, which can be utilized to investigate the wealth of information obtained from combining several publicly available datasets to study a single organ site. Normal brain regions and tumor types create distinct clusters and because the landscape is generated by RNA-Seq, comparative gene expression profiles and gene ontology patterns are readily evident. To our knowledge, this is the first meta-analysis that allows for comparison of gene expression and pathways of interest across adult gliomas, pediatric brain tumors, and normal brain regions. We provide access to this resource via the open source, interactive online tool Oncoscape, where the scientific community can readily visualize clinical metadata, gene expression patterns, gene fusions, mutations, and copy number patterns for individual genes and pathway over this reference landscape. In the era where a bunch of single cell atlas's are being used to understand development, this study highlights how we can quickly download uniformly processed data ( using bioconductor and recount 2), and explore publicly available datasets to explore and understand cancer. Link to paper = https://www.nature.com/articles/s41598-023-31180-z