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TASIC: determining branching models from time series single cell data.

Sabrina Rashid¹, Darrell N Kotton², Ziv Bar-Joseph^1,3

¹Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

Bioinformatics (Oxford, England)

|April 6, 2017

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We developed TASIC, a new method for analyzing time series single-cell RNA sequencing data. TASIC accurately reconstructs cell differentiation trajectories and reveals key genes for cell fate determination.

Area of Science:

Computational biology
Genomics
Developmental biology

Background:

Single-cell RNA sequencing (scRNA-Seq) offers insights into cellular differentiation and disease pathways.
Analyzing time-series scRNA-Seq data presents computational challenges due to mixed cell types and unsynchronized cell development.
Accurately reconstructing temporal trajectories from such data is difficult.

Purpose of the Study:

To introduce TASIC, a novel computational method for analyzing time-series scRNA-Seq data.
To address challenges in determining temporal trajectories, cell fate branching, and cell assignments in single-cell experiments.
To provide a robust approach for understanding cellular differentiation dynamics.

Main Methods:

TASIC utilizes a probabilistic graphical model to integrate gene expression and time information.

Related Experiment Videos

This approach enhances robustness against noise and stochastic variations inherent in scRNA-Seq data.

The method reconstructs developmental trajectories and identifies cell fate branching points.

Main Results:

TASIC accurately reconstructs developmental trajectories in both in vitro myoblast differentiation and in vivo lung development datasets.
The method successfully identified key genes involved in cell fate determination.
New insights into a specific lung cell type and its developmental role were obtained.

Conclusions:

TASIC provides a robust and accurate method for analyzing time-series single-cell RNA-Seq data.
The reconstructed models offer valuable insights into developmental processes and cell fate decisions.
TASIC facilitates the discovery of key regulatory genes and cellular mechanisms in development and disease.