Human Biology Division, Fred Hutchinson Cancer Center, 1100 Fairview Avenue North, Mailstop C3-168, Seattle, WA, 98109, USA.
Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA.
Sci Rep. 2023 Mar 14;13(1):4228. doi: 10.1038/s41598-023-31180-z.
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和肿瘤脑组织之间的关系，我们结合了五个公开的大型数据集，对批次效应进行了校正，并对 RNA-Seq 数据应用了统一流形逼近和投影（UMAP）。我们组装了一个参考 Brain-UMAP，其中包含 702 例成人脑胶质瘤、802 例儿科肿瘤和 1409 例健康正常脑组织样本，可用于研究从多个公开数据集合并获得的丰富信息，以研究单个器官部位。正常脑组织区域和肿瘤类型形成独特的簇，由于该图谱是通过 RNA-Seq 生成的，因此比较基因表达谱和基因本体论模式很容易显现出来。据我们所知，这是第一次允许对成人脑胶质瘤、儿科脑肿瘤和正常脑组织区域的感兴趣的基因表达和途径进行比较的荟萃分析。我们通过开源的交互式在线工具 Oncoscape 提供对该资源的访问，科学界可以在这个参考图谱上轻松地可视化临床元数据、基因表达模式、基因融合、突变和单个基因及途径的拷贝数模式。
