TY  - JOUR
T1  - A Time-Course Multi-Clustering Method for Single-Cell Trajectory Inference
AU  - Jang, Jaeyeon 
AU  - Jung, Inuk 
JO  - Journal of KIISE, JOK
PY  - 2022
DA  - 2022/1/14
DO  - 10.5626/JOK.2022.49.10.838
KW  - single-cell trajectory inference
KW  - gene expression pattern
KW  - multi-clustering method
KW  - time-series data analysis
AB  - From time-series single-cell transcriptome data, gene expression information can be generated to observe the timing of significant cell differentiation changes while accounting for important biological phenomena in relation to experimental conditions. Due to recent surge of time-series single-cell transcriptome data, studies on various dynamic variation in cells such as cell cycle and cell differentiation have been actively conducted. Particularly, time series analysis at single-cell level for cell differentiation is advantageous for biological interpretation compared to a single time point as it is possible to observe changes in the time axis. In this paper, we proposed a multi-clustering method to infer cell trajectory by considering time information at the genetic-level of time-series single-cell transcriptome data. Analyses of gene expression data on the development of human neuron cell differentiation using this method showed similar results to biological results uncovered in a previous study.