A0621
Title: Pseudotime analysis for time-series single-cell sequencing and imaging data
Authors: Gang Li - University of Washington at Seattle (United States) [presenting]
Abstract: Many single-cell RNA-sequencing studies have collected time-series data to investigate transcriptional changes concerning various notions of biological time, such as cell differentiation, embryonic development, and response to stimulus. Accordingly, several unsupervised and supervised computational methods have been developed to construct single-cell pseudotime embeddings for extracting the temporal order of transcriptional cell states from these time-series scRNA-seq datasets. However, existing methods, such as psupertime, suffer from low predictive accuracy, and this problem becomes even worse when we try to generalize to other data types, such as scATAC-seq or microscopy images. Sceptic, a support vector machine model is proposed for supervised pseudotime analysis. Sceptic is demonstrated to achieve significantly improved prediction power (accuracy improved by 1.4 38.9\%) for six publicly available scRNA-seq data sets over state-of-the-art methods, and Sceptic also works well for single-nucleus image data.
