CHSI’s statistical/computational immunology division carries out statistical/computational design and analysis for our collaborative projects with investigators. These projects range from correlative immune studies to building predictive models for disease risk. Our team also develops novel statistical methods for research questions that cannot be addressed by existing quantitative analyses.

• Exploration, analysis and visualization of correlative immune studies. The statistical inference group can provide support for experimental design and analysis of correlative studies and biomarker discovery from soluble factor, antibody, cellular, transcriptomic, or imaging studies.

• Development of custom statistical models for immune-related assays. Existing analytic methods for specific assays may be inadequate or inappropriate for the needs of the investigator. The CHSI statistical inference group can help develop new statistical methods to rigorously analyze assay data. The development of new methods will be led by a faculty member working with a graduate student or postdoc. CHSI will provide partial funding (50%) for a graduate student/postdoc for the development of new methods likely to be of high impact.

• Integrative analysis of data from multiple assay platforms. We are interested in initiating collaborative projects that involve integrating data from more than one assay platform to provide a more complete picture of the immune system. Please contact us if you have a project that could benefit from such “data fusion” and comes with or will generate such multi-platform data.
• Genomics of highly polymorphic loci. We are interested in the imputation of highly polymorphic genomic loci relevant to immunology such as the HLA, KIR, and FcR regions, and in performing association analysis of the genomic variation with immune-related diseases.
• Polygenic risk score for immune diseases. Constructing a predictive model for the PRS requires jointly analyzing genome-wide SNPs simultaneously on large GWAS datasets. CHSI has developed Bayesian model averaging and Markov Chain Monte Carlo (MCMC) to derive such prediction models.

The immune informatics group manages assay data and computing resources for collaborative CHSI projects. Our team also develops reproducible pipelines for standard and novel assays used by investigators.

• Management of assay data. The immune informatics group is responsible for managing the storage, annotation, and curation of immunological data sets for CHSI collaborative projects. In addition to data, metadata that meet minimal standards requirements for uploading to public repositories is maintained.

• Development of reproducible pipelines. The immune informatics group develops containerized pipelines for scalable and reproducible analysis. Pipelines are currently being developed for flow cytometry, bulk RNA-seq, scRNA-seq, TCR and BCR repertoires, and immuno-histochemistry/immunofluorescent (IHC/IF) image analysis. Custom pipelines for novel assays can also be developed collaboratively with investigators.
• Computing resources. Our computing resources consist of 20 servers (each with 4 GPUs, 44 CPU cores, and 384/768 GB RAM) on the Duke Compute Cluster. Immunology researchers with large computing needs are encouraged to consult the group or participate in the quarterly RFP for computationally intensive immunology projects. There is no charge to use the computing resources or bioinformatics expertise for projects selected in the quarterly RFP.

The math modeling team works primarily on developing mathematical models for individual immune cells, host-pathogen dynamics, and immune networks within or involving cells.

• Population dynamics. The mathematical immunology group develops models for host-pathogen dynamics, focused on nonlinear deterministic and stochastic ordinary equation models of the immune response to viral pathogens.

• Single cell fates. We develop models for the mechanistic and/or stochastic determinants that drive immune cell activation, differentiation, and death. These models are complementary to the populating dynamics approaches but at a different scale. In the long-term, we plan to work on integrating the approaches to derive multi-scale models.

• Immune networks. We are interested in developing research into the static and dynamic properties of immune networks. Such networks may include intra-cellular signaling and transcriptional networks, cell-cell signaling networks, and idiotypic antibody networks.