A0635
Title: Zero-inflated smoothing spline for single-cell data
Authors: Xiaoxiao Sun - University of Arizona (United States) [presenting]
Abstract: Trajectory inference methods aim to order single cells along a trajectory based on their gene expression pattern in single-cell data. Such analyses provide new opportunities to investigate cellular dynamic processes such as cell cycle and cell differentiation, which are critical to study disease progression. However, due to the low capturing and sequencing efficiency of single-cell sequencing techniques, there is a common dropout issue, referring to the presence of excessive zero counts in the data. This dropout issue poses a challenge for traditional smoothing spline techniques to analyze single-cell trajectory data. To address this, a zero-inflated smoothing spline method specifically has been developed for single-cell trajectory data.
