TiC2D: Trajectory Inference From Single-Cell RNA-Seq Data Using Consensus Clustering

Yanglan Gan

doi:10.1109/TCBB.2021.3061720

TiC2D: Trajectory Inference From Single-Cell RNA-Seq Data Using Consensus Clustering

2022-07-01

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DOI：10.1109/TCBB.2021.3061720

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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.

Keywords:

Trajectory

Clustering algorithms

Partitioning algorithms

Gene expression

Diseases

Computer science

Manifolds

Trajectory inference

consensus clustering

gene partition

single-cell transcriptome

TiC2D: Trajectory Inference From Single-Cell RNA-Seq Data Using Consensus Clustering

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