Ziqi Zhang

S1214 Coda building

756 W Peachtree St NW

Atlanta, GA, 30308

I’m a Ph.D. student in the School of Computational Science and Engineering, Georgia Institute of Technology. My advisor is Dr. Xiuwei Zhang.

I’m generally interested in developing machine learning algorithms to study cell mechanisms through high-throughput multi-modal biological data. My main research focuses are:

Knowledge transfer and data imputation across modalities, including sequencing omics, perturbation conditions, and species, from single-cell multi-omics data and spatial transcriptomic data.
Studying cell regulatory mechanisms, including gene regulatory networks, cross-modalities association networks, and cross-species molecular functional similarity networks, through single-cell sequencing data with graph learning algorithms.
Constructing single-cell foundation model from large-scale single-cell sequencing atlas, and learning multi-purpose cell representation for various downstream tasks, including cell type annotation, data imputation, and perturbation prediction.

My contact information:

Email: ziqi.zhang@gatech.edu
Address: CODA Building, IDEaS, Georgia Tech

My CV: Download through the link.

news

Jan 30, 2025	Our paper scMultiSim: simulation of single-cell multi-omics and spatial data guided by gene regulatory networks and cell–cell interactions is accepted by Nature Methods, please check it out with the link.
May 20, 2024	I will be interning at Genentech South San Francisco office this summer, I will be primarily working on spatial transcriptomics. Looking forward to meeting you there.
Jan 30, 2024	Our paper scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data is accepted by Nature Communications, please check it out with the link.
Jan 14, 2023	Our paper scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection is accepted by Nature Communications, please check it out with the link.
Jun 28, 2022	Our paper scDART: integrating unmatched scRNA-seq and scATAC-seq data and learning cross-modality relationship simultaneously is accepted by Genome Biology, please check it out with the link.

selected publications

scDisInFact

scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data

Ziqi Zhang, Xinye Zhao, Mehak Bindra, and 2 more authors

Nature Communications, 2024

Bib

@article{zhang2024scdisinfact,
  title = {scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data},
  author = {Zhang, Ziqi and Zhao, Xinye and Bindra, Mehak and Qiu, Peng and Zhang, Xiuwei},
  journal = {Nature Communications},
  volume = {15},
  number = {1},
  pages = {912},
  year = {2024},
  publisher = {Nature Publishing Group UK London},
}

scMoMaT

scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection

Ziqi Zhang, Haoran Sun, Ragunathan Mariappan, and 7 more authors

Nature Communications, 2023

Bib

@article{zhang2023scmomat,
  title = {scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection},
  author = {Zhang, Ziqi and Sun, Haoran and Mariappan, Ragunathan and Chen, Xi and Chen, Xinyu and Jain, Mika S and Efremova, Mirjana and Teichmann, Sarah A and Rajan, Vaibhav and Zhang, Xiuwei},
  journal = {Nature Communications},
  volume = {14},
  number = {1},
  pages = {384},
  year = {2023},
  publisher = {Nature Publishing Group UK London},
}

CeSpGRN

Inferring cell-specific gene regulatory networks from single cell gene expression data

Ziqi Zhang, Jongseok Han, Le Song, and 1 more author

BioRxiv, 2022

Bib

@article{zhang2022GRN,
  title = {Inferring cell-specific gene regulatory networks from single cell gene expression data},
  author = {Zhang, Ziqi and Han, Jongseok and Song, Le and Zhang, Xiuwei},
  journal = {BioRxiv},
  year = {2022},
  publisher = {Cold Spring Harbor Laboratory},
}

CellPath

Inference of high-resolution trajectories in single-cell RNA-seq data by using RNA velocity

Ziqi Zhang and Xiuwei Zhang

Cell Reports Methods, 2021

Bib

@article{zhang2021inference,
  title = {Inference of high-resolution trajectories in single-cell RNA-seq data by using RNA velocity},
  author = {Zhang, Ziqi and Zhang, Xiuwei},
  journal = {Cell Reports Methods},
  volume = {1},
  number = {6},
  pages = {100095},
  year = {2021},
  publisher = {Elsevier},
}

scDART

scDART: integrating unmatched scRNA-seq and scATAC-seq data and learning cross-modality relationship simultaneously

Ziqi Zhang, Chengkai Yang, and Xiuwei Zhang

Genome Biology, Jun 2022

DOI Bib

@article{Zhang2022scDART,
  title = {scDART: integrating unmatched scRNA-seq and scATAC-seq data and learning cross-modality relationship simultaneously},
  author = {Zhang, Ziqi and Yang, Chengkai and Zhang, Xiuwei},
  journal = {Genome Biology},
  year = {2022},
  month = jun,
  day = {27},
  volume = {23},
  number = {1},
  pages = {139},
  issn = {1474-760X},
  doi = {10.1186/s13059-022-02706-x},
  url = {https://doi.org/10.1186/s13059-022-02706-x},
}

VeloSim

VeloSim: Simulating single cell gene-expression and RNA velocity

Ziqi Zhang and Xiuwei Zhang

BioRxiv, Jun 2021

Bib

@article{zhang2021velosim,
  title = {VeloSim: Simulating single cell gene-expression and RNA velocity},
  author = {Zhang, Ziqi and Zhang, Xiuwei},
  journal = {BioRxiv},
  year = {2021},
  publisher = {Cold Spring Harbor Laboratory},
}