Publications
Lab members are underlined.
† denotes equal contribution * denotes co-corresponding authors.
used Xin Maizie Zhou (X. M. Zhou) for publications after 2022.
Preprints/under review
Brain structure and activity predicting cognitive maturation in adolescence.
J. Zhu, C. M. Garin, X.-L. Qi, A. Machado, Z. Wang, S. B. Hamed, T. R. Stanford, E. Salinas, C. T. Whitlow, A. W. Anderson, X. M. Zhou, F. Calabro, B. Luna, C. Constantinidis.
bioRxiv (2024)
stDyer enables spatial domain clustering with dynamic graph embedding.
K. Xu, Y. Xu, Z. Wang, X. M. Zhou, L. Zhang.
bioRxiv (2024)
Leveraging cross-source heterogeneity to improve the performance of bulk gene expression deconvolution.
W. Shen*, C. Liu, Y. Hu, Y. Lei, H-S. Wong, S. Wu*, X. M. Zhou*.
bioRxiv (2024)
CNVeil enables accurate and robust tumor subclone identification and copy number estimation from single-cell DNA sequencing data.
W. Yuan†, C. Luo†, Y. Hu, L. Zhang, Z.-H. Wen, Y. H. Liu, X. Mallory, X. M. Zhou.
bioRxiv (2024)
MaskGraphene: Advancing joint embedding, clustering, and batch correction for spatial transcriptomics using graph-based self-supervised learning.
Y. Hu, Y. Li, M. Xie, M. Rao, Y. H. Liu, X. M. Zhou.
bioRxiv (2023)
Large indel detection in region-based phased diploid assemblies from linked-reads.
C. Luo, B. A. Peters, X. M. Zhou.
BMC genomics (2024) (under revision) (accepted by ISBRA 2021) (acceptance rate: 17.6%)
Peer-reviewed Publications
2024
VolcanoSV enables accurate and robust structural variant calling in diploid genomes from single-molecule long read sequencing.
C. Luo†, Y. H. Liu†, X. M. Zhou.
Nature Communications (2024) 15:6956. https://doi.org/10.1038/s41467-024-51282-0
Benchmarking clustering, alignment, and integration methods for spatial transcriptomics.
Y. Hu, M. Xie, Y. Li, M. Rao, W. Shen, C. Luo, H. Qin, J. Baek, X. M. Zhou.
Genome Biology (2024) 25:212. https://doi.org/10.1186/s13059-024-03361-0
SCCNAInfer: a robust and accurate tool to infer the absolute copy number on scDNA-seq data.
L. Zhang, X. M. Zhou, X. Mallory.
Bioinformatics (2024) 40(7), btae454. https://doi.org/10.1093/bioinformatics/btae454
Parallel signatures of cognitive maturation in primate antisaccade performance and prefrontal activity.
J. Zhu, X. M. Zhou, C. Constantinidis, E. Salinas, T. R. Stanford.
iScience (2024) 27(8):110488. doi: https://doi.org/10.1016/j.isci.2024.110488.
Tradeoffs in alignment and assembly-based methods for structural variant detection with long-read sequencing data.
Y. H. Liu†, C. Luo†, S. G. Golding, J. B. Ioffe, X. M. Zhou.
Nature Communications (2024) 15:2447. https://doi.org/10.1038/s41467-024-46614-z
2023
Laminar pattern of adolescent development changes in working memory neuronal activity.
J. Zhu, B. Hammond, X. M. Zhou, C. Constantinidis.
J. Neurophysiology (2023) 130(4):980-989.
Editorial: predicting high-risk individuals for common diseases using multi-omics and epidemiological data, volume II.
W. P. Veldsman, X. M. Zhou, Y. Zhang, B. Li, L. Zhang.
Front. Genet. (2023) 14:1280648.
ADEPT: autoencoder with differentially expressed genes and imputation for robust spatial transcriptomics clustering.
Y. Hu†, Y. Zhao†, C. T. Schunk, Y. Ma, T. Derr*, X. M. Zhou*.
RECOMB-Seq 2023, iScience (2023) 26(6):106792.
Haplotyping-assisted diploid assembly and variant detection with linked-reads.
Y. Hu†, C. Yang†, L. Zhang*, X. Zhou*.
Methods Mol Biol (2023) 2590:161-182. doi: 10.1007/978-1-0716-2819-5_1. PMID: 36335499.
2022
Haplotype-phasing of long-read HiFi data to enhance structural variant detection through a Skip-Gram model.
C. Luo, P. A. Datar, Y. H. Liu, X. Zhou.
IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Las Vegas, NV, USA, 2022, pp. 2326-2333, doi: 10.1109/BIBM55620.2022.9995293.
A comprehensive investigation of statistical and machine learning approaches for predicting complex human diseases on genomic variants.
C. Wang, J. Zhang, X. Zhou, L. Zhang.
Briefings in Bioinformatics (2022) bbac552. https://doi.org/10.1093/bib/bbac552
Automated filtering of genome-wide large deletions through an ensemble deep learning framework.
Y. Hu, S. Mangal, L. Zhang, X. Zhou.
Methods (2022) 206, 77-86. https://doi.org/10.1016/j.ymeth.2022.08.001
Graphing cell relations in spatial transcriptomics.
X. Zhou.
Nature Computational Science (2022) 2, 354-355. https://doi.org/10.1038/s43588-022-00269-2
Identification of cell types in multiplexed in situ images by combining protein expression and spatial information using CELESTA reveals novel spatial biology.
W. Zhang, I. Li, N. E. Reticker-Flynn, Z. Good, S. Chang, N. Samusik, S. Saumyaa, Y. Li, X. Zhou, et al.
Nature Methods (2022) 9, 759–769. https://doi.org/10.1038/s41592-022-01498-z
Strong gamma frequency oscillations in the adolescent prefrontal cortex.
Z. Wang, B. Singh, X. Zhou*, C. Constantinidis*. (* co-corresponding and co-senior authors)
Journal of Neuroscience (2022) 42 (14) 2917-2929. https://doi.org/10.1523/JNEUROSCI.1604-21.2022
J. Wagner, N. D. Olson…, X. Zhou,…, J. Zook.
Cell Genomics (2022) 2(5), 100128. https://doi.org/10.1016/j.xgen.2022.100128
Neural mechanisms of working memory accuracy revealed by recurrent neural networks.
Y. Xie, Y. H. Liu, C. Constantinidis, X. Zhou.
Front. Syst. Neurosci. (2022) 16:760864. https://doi.org/10.3389/fnsys.2022.760864
A Bayesian factorization method to recover single-cell RNA sequencing data.
Z.-H. Wen, J. L. Langsam, L. Zhang, W. Shen*, X. Zhou*.
Cell Reports Methods (2022) 2, 100133. https://doi.org/10.1016/j.crmeth.2021.100133
2021
An ensemble deep learning framework to refine large deletions in linked-reads.
Y. Hu, S. V. Mangal, L. Zhang, X. Zhou.
IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2021), pp. 288-295, doi: 10.1109/BIBM52615.2021.9669571. (acceptance rate: 19.3%)
The epithelial and stromal immune microenvironment in gastric cancer: a comprehensive analysis reveals prognostic factors with digital cytometry.
W. Shen, G. Wang, G. R. Cooper, Y. Jiang, X. Zhou.
Cancers (Basel) (2021) 13(21):5382. https://doi.org/10.3390/cancers13215382
Emergence of prefrontal neuron maturation properties by training recurrent neural networks in cognitive tasks.
Y. H. Liu, J. Zhu, C. Constantinidis, X. Zhou.
iScience (2021) 24(10):103178. https://doi.org/10.1016/j.isci.2021.103178
Editorial: predicting high-risk individuals for common diseases using multi-omics and epidemiological data.
D. Chowdhury, X. Zhou, B. Li, Y. Zhang, W. K. Cheung, A. Lyu, L. Zhang.
Front. Genet. (2021) 12:737598.
DeepDRIM: a deep neural network to reconstruct cell-type-specific gene regulatory network using single-cell RNA-Seq Data.
J. Chen, C. Cheong, L. Lan, X. Zhou, J. Liu, A. Lyu, W. K. Cheung, L. Zhang.
Briefings in Bioinformatics (2021), 22(6):bbab325. https://doi.org/10.1093/bib/bbab325
Text mining of gene-phenotype associations reveals new phenotypic profiles of autism-associated genes.
S. Li†, Z. Guo†, J. B. Ioffe, Y. Hu, Y. Zhen*, X. Zhou*. († equal contribution)
Scientific Reports (2021) 11(1):15269. https://doi.org/10.1038/s41598-021-94742-z
Aquila_stLFR: diploid genome assembly based structural variant calling package for stLFR linked-reads.
Y.H. Liu, G. L. Grubbs, L. Zhang, X. Fang, D. L. Dill, A. Sidow, X. Zhou.
Bioinformatics Advances (2021) 1, vbab007. https://doi.org/10.1093/bioadv/vbab007
2020 and earlier
Aquila enables reference-assisted diploid personal genome assembly and comprehensive variant detection based on linked-reads.
X. Zhou*, L. Zhang, Z. Weng, D. L. Dill, A. Sidow*. (* co-corresponding authors)
Nature Communications (2021) 12:1077.
A comprehensive investigation of metagenome assembly by linked-read sequencing.
L. Zhang, X. Fang, H. Liao, Z. Zhang, X. Zhou, L. Han, Y. Chen, Q. Qiu, S. C. Li.
Microbiome (2020) 8(1), 1-11.
C. Chin, J. Wagner,…, X. Zhou,…, J. Zook.
Nature Communications (2020) 11:4794.
De novo diploid genome assembly for genome-wide structural variant detection.
L. Zhang†, X. Zhou†, Z. Weng, A. Sidow. († equal contribution)
NAR Genomics and Bioinformatics (2020) 2(1)1-10.
Plasticity of persistent activity and its constraints.
S. Li, X. Zhou, C. Constantinidis, X.-L. Qi.
Frontiers in Neural Circuits (2020) 14: 15.
Assessment of human diploid genome assembly with 10x Linked-Reads data.
L. Zhang†, X. Zhou†, Z. Weng, A. Sidow. († equal contribution)
GigaScience (2019) 8:1-11.
Assessment of network module identification across complex diseases.
S. Choobdar et al.
Nature Methods (2019) 16:843-852.
X. Zhou, S. Batzoglou, A. Sidow, L. Zhang.
BMC genomics (2018) 19 (1), 467.
Anterior-posterior gradient of plasticity in primate prefrontal cortex.
M. Riley, X.-L. Qi, X. Zhou, C. Constantinidis.
Nature Communications (2018) 9(1):3790.
Fixation target representation in prefrontal cortex during the anti-saccade task.
X. Zhou, C. Constantinidis.
J. Neurophysiology (2017) 117:2152-2162.
Neural correlates of working memeory development in adolescent primates.
X. Zhou, D. Zhu, X.-L. Qi, S. H. Li, S.G. King, E. Salinas, T. R. Stanford, C. Constantinidis.
Nature Communications (2016) 7:13423.
Behavioral response inhibition and maturation of goal representation in prefrontal cortex after puberty.
X. Zhou, D. Zhu, S. G. King, C. J. Lees, A. J. Bennett, E. Salinas, T. R. Stanford, C. Constantinidis.
Proc. Natl. Acad. Sci. USA (2016) 113(12):3353-8.
Distinct roles of the prefrontal and posterior parietal cortices in response inhibition.
X. Zhou, X.-L. Qi, C. Constantinidis.
Cell Reports (2016) 14(12):2765-73.
An evolutionary strategy for resilient cyber defense.
E. W. Fulp, H. D. Gage, D. J. John, M. McNiece, W. H. Turkett, and X. Zhou [alphabetical].
In Proceedings of the IEEE Global Communications Conference (GLOBECOM) (2015).
Age-dependent changes in prefrontal intrinsic connectivity.
X. Zhou, D. Zhu, F. Katsuki, X.-L. Qi, C. J. Lees, A. J. Bennett, E. Salinas, T. R. Stanford, C. Constantinidis.
Proc. Natl. Acad. Sci. USA (2014) 111(10):3853-3858.
Working memory performance and neural activity in the prefrontal cortex of peri-pubertal monkeys.
X. Zhou, D. Zhu, X.-L. Qi, C. J. Lees, A. J. Bennett, E. Salinas, T. R. Stanford, C. Constantinidis.
J. Neurophysiology (2013) 110:2648-2660.
Neurons with inverted tuning during the delay periods of working memory tasks in the dorsal prefrontal and posterior parietal cortex.
X. Zhou, F. Katsuki, X.-L. Qi, and C. Constantinidis.
J. Neurophysiology (2012) 108:31-38.
Cholinergic modulation of working memory activity in primate prefrontal cortex.
X. Zhou, X.-L. Qi, K. Douglas, K. Palaninathan, H. S. Kang, J. J. Buccafusco, D. T. Blake, C. Constantinidis.
J. Neurophysiology (2011) 106:2180:8.
Comparison of neural activity related to working memory in primate dorsolateral prefrontal and posterior parietal cortex.
X.-L. Qi, F. Katsuki, T. Meyer, J. B. Rawley, X. Zhou, K. Douglas, C. Constantinidis.
Front. Syst. Neurosci. (2010) 4:12.
Book Chapters
X. Zhou, E. Salinas, T. R. Stanford, C. Constantinidis. Dynamic interactions in prefrontal functional connectivity during adolescence. In: Advances in Cognitive Neurodynamics (V) (2016) R. Wang and X. Pan, Editors. Springer. pp. 193-197
X.-L. Qi, X. Zhou, C. Constantinidis. Neurophysiological mechanisms of working memory: cortical specialization & plasticity. In: Attention and Performance XXV (2015). Jolicoeur P., Lefebvre C. and Martinez-Trujillo J., Editors. Academic Press. pp. 171-186