Robust and High-Resolution Study of Chromatin Spatial Organization via Joint Modeling of Single Cell Imaging and Hi-C Sequencing Data

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Ying Sun
- Affiliation: Gillings School of Global Public Health, Department of Biostatistics
- Other Affiliation: Department of Genetics

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My research will focus on the development of statistical and computational framework that jointly models Hi-C sequencing data and single cell imaging data to study chromatin spatial organization. The ultimate goal of my project is to leverage the genome-wide Hi-C data and high-resolution single cell level imaging data to generate more robust and high-resolution inference of chromatin conformation and to gain more complete understanding of cell-type specific chromatin spatial architecture. We hypothesize that combining imaging and sequencing data will help to more accurately detect interactions between genes and their distal regulatory sequences that either regulate (including enhancing, repressing or even silence) the expression of the genes. We anticipate that the single cell imaging data with cell-type specific information will provide meaningful refinement to existing analysis done with only Hi-C sequencing data. Such refinement or resolution inference will, among others, allow us to narrow down the regulator regions and even to pinpoint to individual causal genetic variants. We leverage innovative statistical methods to effectively integrate information from both sources (detailed in Methods section below). In addition, further investigation may be done to compare different weight functions and fine tuning of parameters to optimize our proposed model

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