Improved RNA data visualization method gets to the bigger picture faster
Thursday, February 14, 2019 - 16:30
in Mathematics & Economics
Like going from a pinhole camera to a Polaroid, a significant mathematical update to the formula for a popular bioinformatics data visualization method will allow researchers to develop snapshots of single-cell gene expression not only several times faster but also at much higher-resolution. Published in Nature Methods, this innovation by Yale mathematicians will reduce the rendering time of a million-point single-cell RNA-sequencing (scRNA-seq) data set from over three hours down to just fifteen minutes.