Department of Molecular, Cell, and Developmental Biology, University of California, Los Angeles, CA, 90095, USA.
Division of Dermatology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA.
BMC Genomics. 2017 Oct 25;18(1):824. doi: 10.1186/s12864-017-4167-7.
Molecular signatures are collections of genes characteristic of a particular cell type, tissue, disease, or perturbation. Signatures can also be used to interpret expression profiles generated from heterogeneous samples. Large collections of gene signatures have been previously developed and catalogued in the MSigDB database. In addition, several consortia and large-scale projects have systematically profiled broad collections of purified primary cells, molecular perturbations of cell types, and tissues from specific diseases, and the specificity and breadth of these datasets can be leveraged to create additional molecular signatures. However, to date there are few tools that allow the visualization of individual signatures across large numbers of expression profiles. Signature visualization of individual samples allows, for example, the identification of patient subcategories a priori on the basis of well-defined molecular signatures.
Here, we generate and compile 10,985 signatures (636 newly-generated and 10,349 previously available from MSigDB) and provide a web-based Signature Visualization Tool (SaVanT; http://newpathways.mcdb.ucla.edu/savant ), to visualize these signatures in user-generated expression data. We show that using SaVanT, immune activation signatures can distinguish patients with different types of acute infections (influenza A and bacterial pneumonia). Furthermore, SaVanT is able to identify the prominent signatures within each patient group, and identify the primary cell types underlying different leukemias (acute myeloid and acute lymphoblastic) and skin disorders.
The development of SaVanT facilitates large-scale analysis of gene expression profiles on a patient-level basis to identify patient subphenotypes, or potential therapeutic target pathways.
分子特征是特定细胞类型、组织、疾病或干扰的特征基因集合。特征也可用于解释从异质样本生成的表达谱。先前已在 MSigDB 数据库中开发和编目了大量基因特征集。此外，几个联盟和大型项目已经系统地对广泛的纯化原代细胞、细胞类型的分子扰动以及特定疾病的组织进行了分析，这些数据集的特异性和广度可用于创建其他分子特征。然而，迄今为止，很少有工具可以跨大量表达谱可视化单个特征。单个样本的特征可视化允许例如根据明确定义的分子特征预先识别患者亚类。
在这里，我们生成并编译了 10985 个特征（636 个新生成的和 10349 个来自 MSigDB 的特征），并提供了一个基于网络的特征可视化工具（SaVanT；http://newpathways.mcdb.ucla.edu/savant），用于可视化用户生成的表达数据中的这些特征。我们表明，使用 SaVanT，可以区分具有不同类型急性感染（甲型流感和细菌性肺炎）的患者的免疫激活特征。此外，SaVanT 能够识别每个患者组中的主要特征，并识别不同白血病（急性髓性和急性淋巴细胞性）和皮肤疾病的主要细胞类型。
SaVanT 的开发促进了基于患者水平的大规模基因表达谱分析，以识别患者亚表型或潜在的治疗靶点途径。
