A challenge with studying cancer transcriptomes is in distilling the wealth of information down into manageable portions of information. In this resource, we develop an approach that creates and assembles cancer type-specific gene expression modules into flexible barcodes, allowing for adaptation to a wide variety of uses. Specifically, we propose that modules derived organically from high-quality gold standards such as The Cancer Genome Atlas (TCGA) can accurately capture and describe functionally related genes that are relevant to specific cancer types. We show that such modules can: (1) uncover novel gene relationships and nominate new functional memberships, (2) improve and speed up analysis of smaller or lower-resolution datasets, (3) re-create and expand known cancer subtyping schemes, (4) act as a “decoder” to bridge seemingly disparate established gene signatures, and (5) efficiently apply single-cell RNA sequencing information to other datasets. Moreover, such modules can be used in conjunction with native spreadsheet program commands to create a powerful and rapid approach to hypothesis generation and testing that is readily accessible to non-bioinformaticians. Finally, we provide tools for users to create and interpret their own modules. Overall, the flexible modular nature of the proposed barcoding provides a user-friendly approach to rapidly decoding transcriptome-wide data for research or, potentially, clinical uses.
研究癌症转录组学面临的一个挑战在于如何将海量信息提炼成可管理的信息片段。在本研究中，我们开发了一种方法，能够创建并整合癌症类型特异性基因表达模块，构建成灵活的条形码系统，从而适应多种应用场景。具体而言，我们提出通过高质量金标准（如癌症基因组图谱TCGA）有机衍生的模块，能够准确捕捉并描述与特定癌症类型相关的功能关联基因。研究表明此类模块具备以下功能：(1) 揭示新型基因关联并提名新的功能成员；(2) 优化并加速小型或低分辨率数据集的分析；(3) 重构并扩展已知的癌症分型方案；(4) 作为"解码器"连接看似无关的既定基因特征；(5) 将单细胞RNA测序信息高效应用于其他数据集。此外，这些模块可与原生电子表格程序命令结合使用，为非生物信息学专业人员提供强大而快速的假设生成与验证方法。最后，我们为用户提供了创建和解读自定义模块的工具。总体而言，这种灵活的模块化条形码系统为用户提供了一种便捷途径，能够快速解码全转录组数据，适用于研究乃至潜在的临床应用。
A Gold Standard-Derived Modular Barcoding Approach to Cancer Transcriptomics
肿瘤(癌症)患者之家