Peijie Zhou

Peijie Zhou

Tenure-Track Assistant Professor

Publications

exFINDER: identify external communication signals using single-cell transcriptomics data

Published in Nucleic Acids Research, 2023

Identifying external signals received by the cells in the single-cell transcriptomics datasets by utilizing the prior knowledge of signaling pathways.

Recommended citation: Changhan He, Peijie Zhou, and Qing Nie. "exFINDER: identify external communication signals using single-cell transcriptomics data." Nucleic Acids Research 51.10 (2023): e58-e58. https://www.nature.com/articles/s41588-023-01298-x

spliceJAC: transition genes and state-specific gene regulation from single-cell transcriptome data

Published in Molecular Systems Biology, 2022

Using attractor Jacobian to infer cell type stability with mRNA splicing dynamics with scRNA-seq data

Recommended citation: Federico Bocci, Peijie Zhou and Qing Nie. spliceJAC: transition genes and state-specific gene regulation from single-cell transcriptome data. https://www.embopress.org/doi/full/10.15252/msb.202211176 https://doi.org/10.15252/msb.202211176

DURIAN: an integrative deconvolution and imputation method for robust signaling analysis of single-cell transcriptomics data

Published in Briefings in Bioinformatics, 2022

We introduce DURIAN (Deconvolution and mUltitask-Regression-based ImputAtioN), an integrative method using iterative scheme between bulk deconvolution and single-cell imputation for recovery of gene expression in single-cell RNA-seq data.

Recommended citation: Karikomi, Matthew, Peijie Zhou, and Qing Nie. "DURIAN: an integrative deconvolution and imputation method for robust signaling analysis of single-cell transcriptomics data." Briefings in Bioinformatics (2022). https://academic.oup.com/bib/advance-article/doi/10.1093/bib/bbac223

Single-Cell RNA-Seq Analysis Reveals the Acquisition of Cancer Stem Cell Traits and Increase of Cell–Cell Signaling during EMT Progression

Published in Cancers, 2021

By integrating multiple analytical tools into a single computational framework, we investigated several single-cell RNA-sequencing (scRNA-seq) datasets and identified the emerging relationship between EMT, acquisition of CSC traits, and cell–cell communication.

Recommended citation: Bocci, F., Zhou, P. and Nie, Q., 2021. Single-Cell RNA-Seq Analysis Reveals the Acquisition of Cancer Stem Cell Traits and Increase of Cell–Cell Signaling during EMT Progression. Cancers, 13(22), p.5726. https://www.mdpi.com/2072-6694/13/22/5726

Dissecting transition cells from single-cell transcriptome data through multiscale stochastic dynamics

Published in Nature Communications, 2021

Using multiscale dynamical system theory to analyze single-cell datasets, quantifying transition paths and dynamical manifold

Recommended citation: Zhou, Peijie, Shuxiong Wang, Tiejun Li, and Qing Nie. Dissecting transition cells from single-cell transcriptome data through multiscale stochastic dynamics. Nat Commun 12, 5609 (2021). https://doi.org/10.1038/s41467-021-25548-w https://doi.org/10.1038/s41467-021-25548-w

Integrating single-cell datasets with ambiguous batch information by incorporating molecular network features

Published in Briefings in Bioinformatics, 2021

Using molecular interaction network features to enhance the analysis of single-cell datasets

Recommended citation: Ji Dong, Peijie Zhou, Yichong Wu, Yidong Chen, Haoling Xie, Yuan Gao, Jiansen Lu, Jingwei Yang, Xiannian Zhang, Lu Wen, Tiejun Li, Fuchou Tang, Integrating single-cell datasets with ambiguous batch information by incorporating molecular network features, Briefings in Bioinformatics, 2021;, bbab366, https://doi.org/10.1093/bib/bbab366 https://academic.oup.com/bib/advance-article/doi/10.1093/bib/bbab366/6373559

On the Mathematics of RNA Velocity I: Theoretical Analysis.

Published in CSIAM Trans. Appl. Math., 2021

Deriving the analytical solution of the RNA velocity model from both deterministic and stochastic point of view; presenting the parameter inference framework based on the maximum likelihood estimate; proving the continuum limit of different downstream kernel-based methods

Recommended citation: Tiejun Li, Jifan Shi, Yichong Wu & Peijie Zhou. (2021). On the Mathematics of RNA Velocity I: Theoretical Analysis. CSIAM Transactions on Applied Mathematics. 2 (1).1-55. doi:10.4208/csiam-am.SO-2020-0001 https://global-sci.org/intro/article_detail/csiam-am/18653.html

Inference of Intercellular Communications and Multilayer Gene-Regulations of Epithelial–Mesenchymal Transition From Single-Cell Transcriptomic Data

Published in Frontiers in Genetics, 2021

Using single-cell transcriptomic datasets, we infer the cell–cell communications and the multilayer gene–gene regulation networks to analyze and visualize the complex cellular crosstalk and the underlying gene regulatory dynamics along epithelial-mesenchymal transitions (EMT)

Recommended citation: Yutong Sha, Shuxiong Wang, Federico Bocci, Peijie Zhou and Qing Nie (2021) Inference of Intercellular Communications and Multilayer Gene-Regulations of Epithelial–Mesenchymal Transition From Single-Cell Transcriptomic Data. Front. Genet. 11:604585. doi: 10.3389/fgene.2020.604585 https://www.frontiersin.org/articles/10.3389/fgene.2020.604585/full

Stochasticity Triggers Activation of the S-phase Checkpoint Pathway in Budding Yeast

Published in Physical Review X, 2021

Combining single-cell experiment data analysis and stochastic dynamical system modeling, our results provide the direct evidence of in vivo stochastic cell-state transitions in single cells.

Recommended citation: Peijie Zhou, Xin Gao, Xiaoli Li, Linxi Li, Caoyuan Niu, Qi Ouyang, Huiqiang Lou, Tiejun Li and Fangting Li. "Stochasticity Triggers Activation of the S-phase Checkpoint Pathway in Budding Yeast". Physical Review X 11 (2021): 011004. https://journals.aps.org/prx/abstract/10.1103/PhysRevX.11.011004

Divergent Resistance Mechanisms to Immunotherapy Explain Responses in Different Skin Cancers

Published in Cancers, 2020

Combining single-cell transcriptome data analysis and stochastic dynamics modeling, we understand the mechanism of different response to immunotherapy between two types of skin cancers – melanoma and basal cell carcinoma (BCC).

Recommended citation: Dollinger, Emmanuel; Bergman, Daniel; Zhou, Peijie; Atwood, Scott X.; Nie, Qing. 2020. "Divergent Resistance Mechanisms to Immunotherapy Explain Responses in Different Skin Cancers" Cancers 12, no. 10: 2946. https://www.mdpi.com/2072-6694/12/10/2946

Inference and multiscale model of epithelial-to-mesenchymal transition via single-cell transcriptomic data

Published in Nucleic Acids Research, 2020

We combine unsupervised machine learning and dynamical systems modeling to study the single-cell transcriptomic datasets from epithelial-mesenchymal transitions (EMT).

Recommended citation: Yutong Sha, Shuxiong Wang, Peijie Zhou, and Qing Nie. "Inference and multiscale model of epithelial-to-mesenchymal transition via single-cell transcriptomic data." Nucleic acids research 48, no. 17 (2020): 9505-9520. https://academic.oup.com/nar/article/48/17/9505/5900115?login=true

Construction of the landscape for multi-stable systems: Potential landscape, quasi-potential, A-type integral and beyond

Published in The Journal of Chemical Physics, 2016

From applied mathematics perspective, we investigate the theoretical relations between different proposals to construct energy landscape function for bio-chemical system .

Recommended citation: Peijie Zhou and Tiejun Li. "Construction of the landscape for multi-stable systems: Potential landscape, quasi-potential, A-type integral and beyond." The Journal of Chemical Physics 144.9 (2016): 094109. https://aip.scitation.org/doi/abs/10.1063/1.4943096