Scientists merge statistics, biology to produce important new gene computational tool

The cells in our bodies express themselves in different ways. One cell might put a chunk of genetic code to work, while another cell ignores the same information entirely. Understanding why could spur new stem cell therapies, or lead to a more fundamental understanding of how organisms develop. But zeroing in on these cell-to-cell differences can be challenging. Now, two UCLA researchers have come up with a computational tool that increases the reliability of measuring how strongly genes are expressed in an individual cell, even when the cell is barely reading certain genes. The research was published last month Communications. "The DNA sequence is the same in a brain cell, a liver cell and a heart cell," said Jingyi "Jessica" Li , the study's corresponding author and a UCLA assistant professor of statistics.