Tempora: Cell trajectory inference using time-series single-cell RNA sequencing data

• DOI: 10.1371/journal.pcbi.1008205
• Publication Date: 2020-09-09T17:33:37Z
• Authors: Thinh N. Tran; Gary D. Bader
• Keywords: Interpretability; Snapshot (computer storage); Single-Cell Analysis

Single-cell RNA sequencing (scRNA-seq) can map cell types, states and transitions during dynamic biological processes such as tissue development regeneration. Many trajectory inference methods have been developed to order cells by their progression through a process. However, when time series data is available, most of these do not consider the available information ordering are instead designed work only on single scRNA-seq snapshot. We present Tempora, novel method that orders using from time-series data. In performance comparison tests, Tempora inferred known developmental lineages three diverse sets, beating state art in accuracy speed. works at level clusters (types) uses pathway help identify type relationships. This approach increases gene expression signal cells, processing speed, interpretability trajectory. Our results demonstrate utility combination supervise for based analysis.