Machine Learning for Single Cell Analysis Workshop - June 17-19
Single cell methods, such as single cell RNA-sequencing, are becoming an increasingly popular way for scientists to probe the heterogeneity and dynamics of biological systems. However, analysis of single cell datasets is a challenging task. The data itself is large and noisy, and choosing the correct tools for analysis requires sifting through literally hundreds of published methods.
PHATED to Be: Yale Researchers Give Shape to Big Data
The lab of Yale’s Smita Krishnaswamy, associate professor of genetics and computer science, has developed a new algorithm called PHATE that overcomes many of the shortcomings of existing data visualization tools, which are more susceptible to noise and distortion in the relationship of data points.