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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...

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Updated: Jul 14, 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

Deep learning models for cell cycle phase prediction from single-cell RNA sequencing data.

Halima Akhter^1,2, Debra Piktel², Laura F Gibson³

¹Lane Department of Computer Science and Electrical Engineering, West Virginia University, 1220 Evansdale Drive, Morgantown, WV 26506, United States.

Briefings in Bioinformatics

|July 12, 2026

Summary

Deep learning models accurately predict cell cycle phases from single-cell RNA sequencing (scRNA-seq) data, improving disease analysis. Performance depends on cell cycle marker recovery rates in training data.

Keywords:

cell cycle phases cross-species validation deep learning model interpretability 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

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Last Updated: Jul 14, 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

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
Biotechnology

Background:

Accurate cell cycle phase prediction is crucial for single-cell RNA sequencing (scRNA-seq) analysis, impacting disease research like cancer.
Existing methods require evaluation for robustness and scalability across diverse biological samples.

Purpose of the Study:

To evaluate traditional and deep learning models for cell cycle phase prediction using scRNA-seq data.
To assess model performance across various datasets with experimentally verified labels.

Main Methods:

Trained and evaluated machine learning (AdaBoost, Random Forest, LightGBM) and deep learning (DNNs, CNNs, ensembles) models.
Utilized consensus phase labels from four tools (CellCycleScore, ccAFv2, Revelio, Tricycle) on diverse scRNA-seq datasets.
Validated performance on independent datasets with experimentally verified cell cycle labels.

Main Results:

Deep learning models, particularly ensembles, demonstrated strong cross-dataset prediction accuracy.
REH-trained models achieved up to 74.35% accuracy on independent datasets.
SHapley Additive exPlanations (SHAP) confirmed cell-cycle-related gene importance; lower marker recovery reduced performance.

Conclusions:

Deep learning offers a robust and scalable solution for cell cycle phase prediction from scRNA-seq data.
Model performance is influenced by the quality and completeness of cell cycle marker information in the training data.