Inferring Transcription Factor Variation Of Murine Cardiogenesis From Single Cell Rna-Seq Data

Background: The RNA-seq analysis at the single cell level emerges as a powerful tool to study the complex transcriptional dynamics of heart development. However, the understanding of the epigenetic dynamics at the single cell level is required to further discover the cis-regulatory elements and trans-acting factors that drive the expression changes and the lineage differentiation. Though single cell ATAC-seq and ChIP-seq have been invented to analyze individual cells, signals from these experiments are intrinsically discrete and cannot be used to accurately describe the continuum of chromatin accessibility and histone modifications. Results: Using the publicly available sequencing data, we successfully built a statistical model to predict various histone marks such as H3K4me3, H3K27me3, H3K27ac and chromatin accessibility signals from corresponding RNA-seq data. We found a high correlation coefficient (0.86 on average) between observed and predicted epigenomic data. Applying this method on a combined data...