ArticleNucleic acids research2022
CCPE: cell cycle pseudotime estimation for single cell RNA-seq data.
Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 26 citations in OpenAlex.
- Deciphering Cell Cycle Dynamics and Cell States in Single-cell RNA-seq data with SPAE.bioRxiv : the preprint server for biology · 2026Article
- DCPR: a deep learning framework for circadian phase reconstruction.BMC bioinformatics · 2025Article
- Enhancer remodeling by OTX2 directs specification and patterning of mammalian definitive endoderm.Developmental cell · 2025Article
- A Multi-Layered Framework for Modeling Human Biology: From Basic AI Agents to a Full-Body AI Agent.ArXiv · 2025Article
- Mitigating Cell Cycle Effects in Multi-Omics Data: Solutions and Analytical Frameworks.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Prediction of radiosensitivity in non-small cell lung cancer based on computed tomography and tumor genomics: a multiple real world cohort study.Respiratory research · 2025Article
- Leveraging prior knowledge to infer gene regulatory networks from single-cell RNA-sequencing data.Molecular systems biology · 2025Review
- Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations.Nature methods · 2024Article
- Gene expression clock: an unsupervised deep learning approach for predicting circadian rhythmicity from whole genome expression.Neural computing & applications · 2024Article
- A Cell Cycle-Aware Network for Data Integration and Label Transferring of Single-Cell RNA-Seq and ATAC-Seq.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Multifunctional nanomaterialsMaterials today. Bio · 2024Article
- From G1 to M: a comparative study of methods for identifying cell cycle phases.Briefings in bioinformatics · 2024Review
- scFseCluster: a feature selection-enhanced clustering for single-cell RNA-seq data.Life science alliance · 2023Article
- Complex Analysis of Single-Cell RNA Sequencing Data.Biochemistry. Biokhimiia · 2023Review
- Incorporating cell hierarchy to decipher the functional diversity of single cells.Nucleic acids research · 2023Article
- Benchmarking imputation methods for network inference using a novel method of synthetic scRNA-seq data generation.BMC bioinformatics · 2022Article
- LFSC: A linear fast semi-supervised clustering algorithm that integrates reference-bulk and single-cell transcriptomes.Frontiers in genetics · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
Pseudotime analysis from scRNA-seq data enables to characterize the continuous progression of various biological processes, such as the cell cycle. Cell cycle plays an important role in cell fate decisions and differentiation and is often regarded as a confounder in scRNA-seq data analysis when analyzing the role of other factors. Therefore, accurate prediction of cell cycle pseudotime and identification of cell cycle stages are important steps for characterizing the development-related biological processes. Here, we develop CCPE, a novel cell cycle pseudotime estimation method to characterize cell cycle timing and identify cell cycle phases from scRNA-seq data. CCPE uses a discriminative helix to characterize the circular process of the cell cycle and estimates each cell's pseudotime along the cell cycle. We evaluated the performance of CCPE based on a variety of simulated and real scRNA-seq datasets. Our results indicate that CCPE is an effective method for cell cycle estimation and competitive in various applications compared with other existing methods. CCPE successfully identified cell cycle marker genes and is robust to dropout events in scRNA-seq data. Accurate prediction of the cell cycle using CCPE can also effectively facilitate the removal of cell cycle effects across cell types or conditions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.