Name
Abstract | ||
|---|---|---|
Cellular programs often exhibit strong heterogeneity and asynchrony in the timing of program execution. Single-cell RNA-seq technology has provided an unprecedented opportunity for characterizing these cellular processes by simultaneously quantifying many parameters at single-cell resolution. Robust trajectory inference is a critical step in the analysis of dynamic temporal gene expression, which can shed light on the mechanisms of normal development and diseases. Here, we present TiC2D, a novel algorithm for cell trajectory inference from single-cell RNA-seq data, which adopts a consensus clustering strategy to precisely cluster cells. To evaluate the power of TiC2D, we compare it with three state-of-the-art methods on four independent single-cell RNA-seq datasets. The results show that TiC2D can accurately infer developmental trajectories from single-cell transcriptome. Furthermore, the reconstructed trajectories enable us to identify key genes involved in cell fate determination and to obtain new insights about their roles at different developmental stages. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TCBB.2021.3061720 | IEEE/ACM Transactions on Computational Biology and Bioinformatics |
Keywords | DocType | Volume |
Algorithms,Cluster Analysis,Consensus,Gene Expression Profiling,RNA-Seq,Sequence Analysis, RNA,Single-Cell Analysis | Journal | 19 |
Issue | ISSN | Citations |
4 | 1545-5963 | 0 |
PageRank | References | Authors |
0.34 | 2 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanglan Gan | 1 | 0 | 2.03 |
| Ning Li | 2 | 0 | 0.34 |
| Cheng Guo | 3 | 0 | 0.68 |
| Guobing Zou | 4 | 95 | 20.12 |
| Jihong Guan | 5 | 657 | 81.13 |
| Shuigeng Zhou | 6 | 2089 | 207.00 |