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Single-cell RNA-seq analysis of human coronary arteries using an enhanced workflow reveals SMC transitions and candidate drug targets
[article]
2020
bioRxiv
pre-print
Recent advances in single-cell RNA sequencing (scRNA-seq) methods have enabled high-resolution profiling and quantification of cellular expression and transcriptional states. Here we incorporate automated cell labeling, pseudotemporal ordering, ligand-receptor evaluation, and drug-gene interaction analysis into an enhanced and reproducible scRNA-seq analysis workflow. We applied this analysis method to a recently published human coronary artery scRNA dataset and revealed distinct derivations of
doi:10.1101/2020.10.27.357715
fatcat:qsvj6vjhq5e5xahzkscjh3uvky