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Abstract:
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With the recent advance in technologies to profile multi-omics data at the single-cell level, integrative multi-omics data analysis has been increasingly popular. For example, information such as methylation changes, chromatin accessibility, and gene expression are jointly collected in a single-cell experiment. In biological studies related to embryonic cell development, it is often useful to analyze how the association between two data types changes given other data types. However, since each data type usually has a distinct marginal distribution, joint analysis of this dynamic association for multi-omics data presents a statistical challenge.
In this presentation, we propose a flexible copula-based framework to study the dynamic association across different data types. This approach can incorporate a wide variety of univariate marginal distributions, either discrete or continuous, including the class of zero-inflated distributions. We will present results from simulation studies and real data analysis in examing the dynamic relationship between single-cell RNA-sequencing, chromatin accessibility, and DNA methylation at different germ layers during mouse gastrulation.
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