A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is
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 ofdoi:10.1101/2020.10.27.357715 fatcat:qsvj6vjhq5e5xahzkscjh3uvky