Computational Core

计算分析核心(核心C)--项目摘要 计算分析核心(核心C)的主要目标是提供集中的统计和 为本P01研究项目提供生物信息学服务，并与其合作。核心C将作为焦点 P01调查人员利用统计和生物信息学专业知识设计和分析其 研究项目以及执行计划研究的人员编制支助。核心C将实现深层次、多层次的 模型理解，以帮助识别可以预测或告知机制的细胞和/或空间特征 对HIV病毒库的干预效果。核心的具体目标是：1)使用既定的 计算方法以增强、质量控制和分析批量/单细胞基因组数据；2)使用已建立的 空间组学数据质量控制和分析的计算方法；以及3)跨平台和跨物种 整合单细胞和空间组学数据以确定艾滋病毒干预效果的预测特征。 核心C成员将在每个研究阶段参与所有项目。随着项目产生结果，核心 将进行数据分析，准备任何必要的报告，并协助调查人员准备 演讲和手稿。CORE C将由秦马博士(俄亥俄州立大学)、蒋思尊共同领导 (哈佛医学院)、亚历克斯·K·沙莱克(麻省理工学院/拉贡研究所/博德研究所)和钟东军(俄亥俄州 州立大学)。所有核心成员在应用生物统计学和生物信息学方面都有丰富的经验 血清学、批量、单细胞和空间多组学数据的方法。它们将与其他核心密切合作(例如， 多组学核心)，用于无缝集成这一创新的P01项目的所有方面。具体来说，马博士 沙莱克将负责大宗和单细胞相关数据分析，江博士和马博士将负责空间组学 数据分析(包括CODEX和CosMX)，钟博士将负责统计和权力分析。 此外，对单细胞和空间组学数据的综合分析将由所有人共同监督 会员。总而言之，核心C将通过应用被接受的和适当的 数据分析的统计和生物信息学方法，通过清楚地描述方法、原理和 报告和手稿中的解释，以及公开分享研究成果和数据。

Computational Analysis Core (Core C) - Project Summary The primary objective of the Computational Analysis Core (Core C) is to provide centralized statistical and bioinformatics services for, and collaboration on, the research projects of this P01. Core C will serve as the focal point for P01 investigators to draw statistical and bioinformatics expertise for the design and analysis of their research projects as well as for staffing support to execute the planned studies. Core C will enable a deep, multi- model understanding to help identify cellular and/or spatial signatures that can predict or inform mechanisms of interventional efficacy against HIV viral reservoirs. The Specific Aims of the Core are to: 1) use established computational methods to power, quality control, and analyze bulk/single-cell genomics data; 2) use established computational methods to quality control and analyze spatial-omics data; and 3) Cross-platform and -species integration of single-cell and spatial-omics data for identifying predictive features of HIV interventional efficacy. Core C members will be involved in all Projects at every research stage. As the Projects yield results, the Core will conduct data analyses, prepare any necessary reports, and assist investigators with the preparation of presentations and manuscripts. Core C will be co-led by Drs. Qin Ma (Ohio State University), Sizun Jiang (Harvard Medical School), Alex K. Shalek (MIT/Ragon Institute/Broad Institute), and Dongjun Chung (Ohio State University). All Core members have extensive experience in applied biostatistics and bioinformatics methods for serology, bulk, single-cell, and spatial multi-omics data. They will work closely with other cores (e.g., Multi-omics Core) for seamless integration of all aspects of this innovative P01 project. Specifically, Drs. Ma and Shalek will oversee bulk and single-cell related data analyses, Drs. Jiang and Ma will oversee spatial omics data analysis (including CODEX and CosMX), and Dr. Chung will oversee the statistical and power analyses. Moreover, the integrative analysis of single-cell and spatial-omics data will be collaboratively overseen by all members. In summary, Core C will ensure rigor and reproducibility by applying accepted and appropriate statistical and bioinformatics methods to data analysis, by clearly describing methodology, rationale, and interpretations in reports and manuscripts, and by openly sharing research results and data.