cycleX: multi-dimensional pseudotime reveals cell cycle and differentiation relationship of dendritic cell progenitors

Tan, Yong; Zhang, Xiaomeng; Chen, Jinmiao

doi:10.1101/222372

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…Within the sub-ventricular zone (SVZ), it is estimated that 80% of adult neural stem cells undergo symmetric differentiation, and 20% undergo symmetric proliferation with little evidence of asymmetric divisions. To date, only one computational method, CycleX [27], attempts to decipher such a relationship between the two developmental processes.…”

Section: Introductionmentioning

confidence: 99%

redPATH: Reconstructing the Pseudo Development Time of Cell Lineages in Single-Cell RNA-Seq Data and Applications in Cancer

Xie

Liu

Chen

et al. 2020

Preprint

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Recent advancement of single-cell RNA-seq technology facilitates the study of cell lineages in developmental processes as well as cancer. In this manuscript, we developed a computational method, called redPATH, to reconstruct the pseudo developmental time of cell lineages using a consensus asymmetric Hamiltonian path algorithm. Besides, we implemented a novel approach to visualize the trajectory development of cells and visualization methods to provide biological insights. We validated the performance of redPATH by segmenting different stages of cell development on multiple neural stem cell and cancerous datasets, as well as other single-cell transcriptome data. In particular, we identified a subpopulation of malignant glioma cells, which are stem cell-like. These cells express known proliferative markers such as GFAP (also identified ATP1A2, IGFBPL1, ALDOC) and remain silenced in quiescent markers such as ID3. Furthermore, MCL1 is identified as a significant gene that regulates cell apoptosis, and CSF1R confirms previous studies for re-programming macrophages to control tumor growth. In conclusion, redPATH is a comprehensive tool for analyzing single-cell RNA-Seq datasets along a pseudo developmental time. The software is available via http://github.com/tinglab/redPATH.

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Section: Introductionmentioning

confidence: 99%

redPATH: Reconstructing the Pseudo Development Time of Cell Lineages in Single-Cell RNA-Seq Data and Applications in Cancer

Xie

Liu

Chen

et al. 2020

Preprint

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“…Furthermore, f-scLVM [ 20 ] models scRNA-seq data with gene set annotation to capture cell-level heterogeneity, allowing for the identification of cyclic genes and the removal of the cell cycle effect. CycleX [ 21 ] extends the traditional pseudotime analysis to multi-dimensional level, revealing the expression behavior along the cell cycle. Additionally, reCAT [ 22 ] constructs a time series from scRNA-seq data using a method based on the traveling salesman problem.…”

Section: Methods For Cell Cycle Identificationmentioning

confidence: 99%

From G1 to M: a comparative study of methods for identifying cell cycle phases

Guo,

Chen

2024

Briefings in Bioinformatics

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Accurate identification of cell cycle phases in single-cell RNA-sequencing (scRNA-seq) data is crucial for biomedical research. Many methods have been developed to tackle this challenge, employing diverse approaches to predict cell cycle phases. In this review article, we delve into the standard processes in identifying cell cycle phases within scRNA-seq data and present several representative methods for comparison. To rigorously assess the accuracy of these methods, we propose an error function and employ multiple benchmarking datasets encompassing human and mouse data. Our evaluation results reveal a key finding: the fit between the reference data and the dataset being analyzed profoundly impacts the effectiveness of cell cycle phase identification methods. Therefore, researchers must carefully consider the compatibility between the reference data and their dataset to achieve optimal results. Furthermore, we explore the potential benefits of incorporating benchmarking data with multiple known cell cycle phases into the analysis. Merging such data with the target dataset shows promise in enhancing prediction accuracy. By shedding light on the accuracy and performance of cell cycle phase prediction methods across diverse datasets, this review aims to motivate and guide future methodological advancements. Our findings offer valuable insights for researchers seeking to improve their understanding of cellular dynamics through scRNA-seq analysis, ultimately fostering the development of more robust and widely applicable cell cycle identification methods.

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redPATH: Reconstructing the Pseudo Development Time of Cell Lineages in Single-Cell RNA-Seq Data and Applications in Cancer

Xie

Liu

Chen

et al. 2021

Genomics, Proteomics &Amp; Bioinformatics

View full text Add to dashboard Cite

The recent advancement of single-cell RNA sequencing (scRNA-seq) technologies facilitates the study of cell lineages in developmental processes and cancer. In this study, we developed a computational method, called redPATH, to reconstruct the pseudo developmental time of cell lineages using a consensus asymmetric Hamiltonian path algorithm. Besides, we developed a novel approach to visualize the trajectory development and implemented visualization methods to provide biological insights. We validated the performance of redPATH by segmenting different stages of cell development on multiple neural stem cell and cancer datasets, as well as other single-cell transcriptome data. In particular, we identified a stem cell-like subpopulation in malignant glioma cells. These cells express known proliferative markers, such as GFAP , ATP1A2 , IGFBPL1 , and ALDOC , and remain silenced for quiescent markers such as ID3 . Furthermore, we identified MCL1 as a significant gene that regulates cell apoptosis and CSF1R for reprogramming macrophages to control tumor growth. In conclusion, redPATH is a comprehensive tool for analyzing scRNA-seq datasets along the pseudo developmental time. redPATH is available at https://github.com/tinglabs/redPATH .

show abstract

cycleX: multi-dimensional pseudotime reveals cell cycle and differentiation relationship of dendritic cell progenitors

Cited by 3 publications

References 16 publications

redPATH: Reconstructing the Pseudo Development Time of Cell Lineages in Single-Cell RNA-Seq Data and Applications in Cancer

redPATH: Reconstructing the Pseudo Development Time of Cell Lineages in Single-Cell RNA-Seq Data and Applications in Cancer

From G1 to M: a comparative study of methods for identifying cell cycle phases

redPATH: Reconstructing the Pseudo Development Time of Cell Lineages in Single-Cell RNA-Seq Data and Applications in Cancer

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