Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jun 5, 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

Fuzzy-Based Identification of Transition Cells to Infer Cell Trajectory for Single-Cell Transcriptomics.

Xiang Chen¹, Yibing Ma¹, Yongle Shi¹

¹School of Science, Jiangnan University, Wuxi, China.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|December 13, 2024

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Amidoxime-Based Near-Infrared Fluorescent Sensor for Highly Sensitive Uranium Detection in Living Systems.

Analytical chemistry·2026

Same author

StPedf: Cell trajectory inference of spatial transcriptomics via spatial proximity embedding and spatial density-adaptive fusion.

PLoS computational biology·2026

Same author

The dual correlations between core beliefs changes and emotion regulation among disaster-affected residents: the moderating role of religiousness orientation.

Frontiers in psychology·2026

Same author

scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning.

Communications biology·2026

Same author

Microbial Degradation of Herbicide Residues in Australian Soil: An Overview of Mechanistic Insights and Recent Advancements.

Toxics·2025

Same author

Thermoelectric property regulation of dimer-triangulene molecular junctions based on oxygen edge-modification engineering.

Physical chemistry chemical physics : PCCP·2025

We developed scFCTI, a new fuzzy clustering method for single-cell trajectory inference. It accurately identifies transition cells and reconstructs precise cell development paths, outperforming existing methods.

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) enables computational reconstruction of cell development.
Trajectory inference is vital for understanding cell cycle and differentiation.
Identifying transient cell states remains a challenge.

Purpose of the Study:

To propose a novel single-cell trajectory inference method, scFCTI.
To accurately identify transition cells and refine cell classification.
To achieve more precise single-cell trajectory reconstruction.

Main Methods:

Developed scFCTI, a method utilizing fuzzy clustering for single-cell trajectory inference.
Quantified cell uncertainty to identify cells in unstable states.

Keywords:

fuzzy clustering pseudo-time analysis single-cell transcriptomics trajectory inference transition cells

More Related Videos

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

An Approach to Study Shape-Dependent Transcriptomics at a Single Cell Level

An Approach to Study Shape-Dependent Transcriptomics at a Single Cell Level

Published on: November 2, 2020

Related Experiment Videos

Last Updated: Jun 5, 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

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

An Approach to Study Shape-Dependent Transcriptomics at a Single Cell Level

An Approach to Study Shape-Dependent Transcriptomics at a Single Cell Level

Published on: November 2, 2020

Characterized different cell stages for refined classification.

Main Results:

scFCTI successfully identified unstable cell clusters and transition cells.
The method achieved more accurate cell path reconstruction, including transition states.
Experiments on real and simulated datasets showed scFCTI outperformed state-of-the-art methods.

Conclusions:

scFCTI offers a robust approach for single-cell trajectory inference.
The method enhances the understanding of cell development dynamics.
scFCTI provides more precise reconstruction of cell trajectories with transition paths.