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Updated: Mar 19, 2026

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

Deciphering Cell Cycle Dynamics and Cell States in Single-cell RNA-seq data with SPAE.

Jiahao Yi¹, Jiajia Liu², Peng Guo¹

¹Bioinformatics and Biomedical Big Data Mining Laboratory, Department of Medical Informatics, School of Biology and Engineering, Guizhou Medical University, Anshun, Guizhou 561100, China.

Biorxiv : the Preprint Server for Biology

|March 18, 2026

Summary

This summary is machine-generated.

We developed SPAE, an autoencoder model, to accurately characterize cell cycle dynamics in single-cell RNA sequencing (scRNA-seq) data. This method improves cell cycle analysis and facilitates the removal of cell cycle effects from gene expression data.

Keywords:

Autoencoder Cell cycle dynamics Cell cycle effects scRNA-seq

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Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Published on: January 7, 2020

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) reveals cellular heterogeneity and complex biological processes.
Analyzing cell cycle dynamics in scRNA-seq data is challenging due to data complexity and subtle cell state differences.

Purpose of the Study:

To develop an accurate and robust method for characterizing cell cycle dynamics and cell states in scRNA-seq data.
To address the limitations of existing methods in cell cycle analysis.

Main Methods:

Development of the integrated Sinusoidal and Piecewise AutoEncoder (SPAE), an autoencoder-based piecewise linear model.
Application of SPAE to scRNA-seq data for cell cycle characterization and effect removal.

Main Results:

SPAE demonstrated improved accuracy and robustness in characterizing cell cycle dynamics compared to existing methods.
SPAE accurately predicted cancer cell cycle transitions.
SPAE effectively facilitated the removal of cell cycle effects from gene expression data.

Conclusions:

SPAE is a powerful tool for analyzing cell cycle dynamics in scRNA-seq data.
The method enhances the understanding of cellular heterogeneity and disease pathogenesis.
SPAE is available for non-commercial use.