Arora Sonali, Szulzewsky Frank, Jensen Matt, Nuechterlein Nicholas, Pattwell Siobhan S, Holland Eric C
Fred Hutchinson Cancer Center.
University of Washington.
Res Sq. 2023 Jan 10:rs.3.rs-2448083. doi: 10.21203/rs.3.rs-2448083/v1.
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.
为了更好地理解正常大脑与肿瘤性大脑之间的关系，我们整合了五个公开可用的大规模数据集，校正了批次效应，并将均匀流形逼近与投影（UMAP）应用于RNA测序数据。我们构建了一个参考脑UMAP，其中包括702个成人胶质瘤、802个儿童肿瘤和1409个健康正常脑样本，可用于研究从整合多个公开可用数据集以研究单个器官部位所获得的丰富信息。正常脑区和肿瘤类型形成了不同的簇，并且由于该图谱是由RNA测序生成的，比较基因表达谱和基因本体模式很容易显现出来。据我们所知，这是第一项能够比较成人胶质瘤、儿童脑肿瘤和正常脑区中感兴趣的基因表达和通路的荟萃分析。我们通过开源的交互式在线工具Oncoscape提供对该资源的访问，科学界可以在此参考图谱上轻松可视化个体基因和通路的临床元数据、基因表达模式、基因融合、突变和拷贝数模式。
