vol.08 Analysis of time-course single-cell data for identifying genes that control disease

Researcher: Yuki Kato
Affiliation: Assistant Professor, Department of RNA Biology and Neuroscience, Graduate School of Medicine, Osaka University
Abstract: Organs and tissues consist of cell populations, and genes expressed in these cells can be investigated at a single-cell level. A cell changes from a stem cell into a mature specialized cell, which is called cellular differentiation. It is important to trace changes of gene expression levels during cellular differentiation. For instance, comparing time-course single-cell data of a normal mouse with those of a diseased one by a computational method will reveal which genes can control the disease. In this study, we develop a computational tool for efficiently comparing differentiation trajectories derived from single-cell data for two related experimental conditions. https://github.com/ykat0/capital

Posted : May 01,2020