Bioinformatics and Biostatistics Core

核心D摘要 生物信息学和生物统计核心D将提供生物信息和生物统计服务和支持数据 对所有项目的分析。CORE D将设在西奈山的伊坎医学院，由Dr。 生物信息学和机器学习应用方面的专家李申说。核心的任务之一是 支持多元组学和综合分析。项目1、2和4中的临床前和临床研究 将利用多组学技术在细胞内描述系统性应激相关的免疫失调 和分子水平。核心有专门的生物信息学家来帮助PPG研究人员分析， 可视化并从各种不同的转录组学、表观基因组学和蛋白质组学实验中挖掘数据 动物模型(项目1和2)和患者(项目2和4)。核心将利用最先进的技术 生物信息学工具和机器学习方法，用于稳健、高效和综合地分析 多组学及相关表型数据。核心还将提供全面的生物统计支持。 统计问题将在所有调查层面上解决，从设计实验到口译 结果和形成结论。将在基于形式的结论之间作出明确的区分 假设检验和通过数据探索发现的重要线索。最后，核心目标是减少 与科学界共享数据和代码，从而产生偏见，增强再现性。核心将使 尽一切可能减少统计分析中的潜在偏差。将进行多次测试修正 对所有组学数据进行了分析。核心还将共享来自RAW的测序和次要数据 向公众展示图片。核心开发的软件工具也将作为开放源码项目共享。

SUMMARY FOR CORE D The Bioinformatics and Biostatistics Core D will provide bioinformatic and biostatistical services and support data analyses for all Projects. Core D will be situated at the Icahn School of Medicine at Mount Sinai and led by Dr. Li Shen, who is an expert in bioinformatics and machine learning applications. One of the Core’s tasks is to support multi-omics and integrated analysis. The pre-clinical and clinical studies in Projects 1, 2, and 4 will leverage multi-omics technologies to profile systemic stress-related immune dysregulations at the cellular and molecular levels. The Core has dedicated bioinformaticians to aid PPG researchers in analyzing, visualizing, and mining data from transcriptomics, epigenomics, and proteomics experiments in various animal models (Projects 1 and 2) and patients (Projects 2 and 4). The Core will leverage state-of-the-arts bioinformatic tools and machine learning methods for robust, efficient and integrated analyses of the multi-omics and related phenotypic data. The Core will also provide comprehensive biostatistical support. Statistical issues will be addressed at all investigatory levels, from designing experiments to interpreting results and forming conclusions. Clear distinctions will be made between conclusions based on formal hypothesis testing and important leads discovered by data exploration. Lastly, the Core aims to reduce biases, enhance reproducibility, and share data and code with the scientific community. The Core will make every effort possible to reduce potential biases in the statistical analysis. Multiple testing correction will be performed on all omics data. The Core will also share the sequencing and secondary data derived from raw images to the public. Software tools developed by the Core will also be shared as open-source projects.