Computational Core

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.