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Updated: Jul 4, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Reconstructing growth and dynamic trajectories from single-cell transcriptomics data.

Yutong Sha¹, Yuchi Qiu², Peijie Zhou¹

¹Department of Mathematics, University of California, Irvine, Irvine, CA USA.

Nature Machine Intelligence

|January 26, 2024

Summary

This summary is machine-generated.

TIGON reconstructs cellular dynamics from time-series single-cell RNA sequencing (scRNA-seq) data. This dynamic, unbalanced optimal transport algorithm simultaneously models cell trajectories, population growth, and gene regulatory networks.

Keywords:

Data integration Machine learning

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Area of Science:

Computational Biology
Genomics
Systems Biology

Background:

Time-series single-cell RNA sequencing (scRNA-seq) offers insights into cellular dynamics.
Linking scRNA-seq snapshots across time is challenging due to the destructive nature of sequencing.

Purpose of the Study:

To develop a novel algorithm for reconstructing dynamic cellular processes from scRNA-seq data.
To simultaneously infer cell trajectories, population growth, and gene regulatory networks.

Main Methods:

Introduced TIGON, a dynamic, unbalanced optimal transport algorithm.
Utilized a deep learning approach with Wasserstein-Fisher-Rao (WFR) distance for high-dimensional optimal transport.
Evaluated TIGON on simulated and real scRNA-seq datasets.

Main Results:

TIGON accurately predicts cell state transitions and population growth.
Demonstrated the importance of incorporating cell population growth in temporal inference.
Showcased TIGON's ability to reconstruct gene expression at unmeasured time points.

Conclusions:

TIGON provides a robust framework for analyzing dynamic cellular processes using scRNA-seq data.
The algorithm enables advanced applications in temporal gene regulatory network and cell-cell communication inference.