Enhancement and Cloud Deployment of CaDrA, a software tool for Candidate Driver Analysis of Multiomics Data

我们的目标是进一步开发、优化、记录、容器化并作为R包CaDrA（候选驱动程序）交付 分析），一个开源的，用户友好的计算工具，用于分析癌症多组学数据集。一 该软件包的原型已经存在，并已应用于在高影响力期刊上发表的研究。下- 用模拟数据和真实的数据对说谎方法进行了评价，结果表明，说谎方法具有较高的灵敏度， 的特异性该方法是在NIDCR赞助的F31（获奖者，Vinay）的赞助下部分开发的 Kartha），相关出版物受到相当大的关注，我们收到了几份请求， 分享代码并阐明其用途。我们计划在母公司补助金的背景下利用这种分析方法 （NIDCR MPI 1 R 01 DE 030350 - 01 A1），旨在通过实验和计算机模拟方法研究β- 连环蛋白/CBP轴在头颈癌中的作用一个扩展和优化的工具将支持分析散装和 我们正在生成单细胞RNAseq数据，作为赠款的一部分，以及公共数据集的查询，例如 TCGA、CPTAC、CCLE等，基于我们自己生成的数据衍生的转录签名。 虽然存在一些可以整合和解释多种实验和分子数据类型的分析方法， 它们倾向于提供高度交互的界面，但相对基本的分析功能，或者 复杂的分析方法没有得到有据可查和用户友好的工具的充分支持。我们 拟议的工具占据了两者之间的“最佳点”，并将提供先进的统计技术，用户- 友好的界面，以及一个文档齐全的开源R包，将可用于研究 社区作为一个独立的工具，或“集成”到其他工具和分析工作流程，并将促进 进行的分析的可重复性和透明度。 为了促进该工具更广泛的采用、适用性和可伸缩性，其健壮性、可移植性和用户友好性 需要加强。这一建议将使我们能够追求它的扩展与新的功能，其增强 和优化，文档和容器化到一个开源软件包，通过 GitHub，可通过CRAN或Bioconductor安装。 我们的工作计划包括扩展支持的统计功能，代码清理，分析，测试和 优化以提高运行时效率，采用符合的数据结构和构造 Bioconductor指南，打包以确保代码易于安装和测试，容器化以支持云 部署、开发图形界面以提高该工具的用户友好性，并设计一个网站， 基于贷款页面托管在我们的大学服务器上。 相关性和影响。该工具的可用性将有助于实现我们的父母补助金目标。在parti- cular，它将支持我们自己从小鼠模型中生成的批量和单细胞RNAseq数据的分析， 人体样本R包装和集装箱化也将促进研究界的采用。

We aim to further develop, optimize, document, containerize and deliver as an R package CaDrA (Candidate Driver Analysis), an open-source, user-friendly computational tool for the analyses of cancer multi-omics datasets. A prototype of the package already exists, and was applied in studies published in high-impact journals. The under- lying methodology was evaluated with simulated and real data, and it was shown to have high sensitivity and specificity. The methodology was developed in part under the auspices of a NIDCR-sponsored F31 (awardee, Vinay Kartha), with the associated publications receiving considerable attention, and we received several requests to share the code and clarify its use. We plan to utilize this analytical approach in the context of our parent grant (NIDCR MPI 1R01DE030350-01A1), aimed at investigating, through experimental and in-silico approaches, the β- catenin/CBP axis in head and neck cancer. An expanded and optimized tool will support the analysis of bulk and single cell RNAseq data we are generating as part of that grant, as well as the querying of public datasets, such as TCGA, CPTAC, CCLE and others, based on transcriptional signatures derived from our own generated data. While some analysis methods that can integrate and interpret multiple experimental and molecular data types exist, they tend to either provide for highly interactive interfaces but relatively basic analytic functionalities, or for sophisticated analysis methods not adequately supported by well-documented and user-friendly tools. Our proposed tool occupies the “sweet spot” in between and will provide for advanced statistical techniques, a user- friendly interface, and a well-documented, open-source R package, which will be available to the research community as a stand-alone tool, or “integratable” into other tools and analytical workflows, and will facilitate reproducibility and transparency of the analyses performed. To facilitate the tool’s wider adoption, applicability, and scalability, its robustness, portability, and user-friendliness need to be enhanced. This proposal would allow us to pursue it extension with new functionalities, its enhancement and optimization, documentation, and containerization into an open-source package made available through GitHub, and installable through CRAN or Bioconductor. Our work plan includes extension of the statistical functionalities supported, code cleaning, profiling, testing and optimization to improve run-time efficiency, adoption of data structures and constructs compliant with Bioconductor guidelines, packaging to ensure easy code installation and testing, containerization to support cloud deployment, development of a graphical interface to increase the tool’s user-friendliness, and design of a web- based lending page to be hosted on our university server. Relevance and Impact. Availability of the tool will be instrumental to the pursuit of our parent grant aims. In parti- cular, it will support the analysis of our own generated bulk and single cell RNAseq data from mouse models and human samples. R packaging and containerization will also facilitate adoption by the research community.