King Lydia, Flaus Andrew, Coughlan Simone, Holian Emma, Golden Aaron
SFI Centre for Genomics Data Science, National University of Ireland, Galway, H91 TK33, Ireland.
School of Mathematical & Statistical Sciences, National University of Ireland, Galway, H91 TK33, Ireland.
HRB Open Res. 2022 Sep 12;5:8. doi: 10.12688/hrbopenres.13476.2. eCollection 2022.
Exploratory analysis of cancer consortia data curated by the cBioPortal repository typically requires advanced programming skills and expertise to identify novel genomic prognostic markers that have the potential for both diagnostic and therapeutic exploitation. We developed GNOSIS (GeNomics explOrer using StatistIcal and Survival analysis in R), an R Shiny App incorporating a range of R packages enabling users to efficiently explore and visualise such clinical and genomic data. GNOSIS provides an intuitive graphical user interface and multiple tab panels supporting a range of functionalities, including data upload and initial exploration, data recoding and subsetting, data visualisations, statistical analysis, mutation analysis and, in particular, survival analysis to identify prognostic markers. GNOSIS also facilitates reproducible research by providing downloadable input logs and R scripts from each session, and so offers an excellent means of supporting clinician-researchers in developing their statistical computing skills.
对由cBioPortal存储库整理的癌症联盟数据进行探索性分析通常需要先进的编程技能和专业知识，以识别具有诊断和治疗开发潜力的新型基因组预后标志物。我们开发了GNOSIS（使用R中的统计和生存分析的基因组探索器），这是一个R Shiny应用程序，它整合了一系列R包，使用户能够有效地探索和可视化此类临床和基因组数据。GNOSIS提供了一个直观的图形用户界面和多个标签面板，支持一系列功能，包括数据上传和初始探索、数据重新编码和子集化、数据可视化、统计分析、突变分析，特别是生存分析以识别预后标志物。GNOSIS还通过提供每个会话的可下载输入日志和R脚本促进可重复研究，因此为支持临床研究人员发展其统计计算技能提供了一种极好的方法。
