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Overcoming Expressional Drop-outs in Lineage Reconstruction from Single-Cell RNA-Sequencing Data.

Cell reports

Lu T, Park S, Zhu J, Wang Y, Zhan X, Wang X, Wang L, Zhu H, Wang T.
PMID: 33406427
Cell Rep. 2021 Jan 05;34(1):108589. doi: 10.1016/j.celrep.2020.108589.

Single-cell lineage tracing provides crucial insights into the fates of individual cells. Single-cell RNA sequencing (scRNA-seq) is commonly applied in modern biomedical research, but genetics-based lineage tracing for scRNA-seq data is still unexplored. Variant calling from scRNA-seq data uniquely...

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Lu T, Park S, Zhu J, et al. Overcoming Expressional Drop-outs in Lineage Reconstruction from Single-Cell RNA-Sequencing Data. Cell Rep. 2021;34(1):108589doi: 10.1016/j.celrep.2020.108589.
Lu, T., Park, S., Zhu, J., Wang, Y., Zhan, X., Wang, X., Wang, L., Zhu, H., & Wang, T. (2021). Overcoming Expressional Drop-outs in Lineage Reconstruction from Single-Cell RNA-Sequencing Data. Cell reports, 34(1), 108589. https://doi.org/10.1016/j.celrep.2020.108589
Lu, Tianshi, et al. "Overcoming Expressional Drop-outs in Lineage Reconstruction from Single-Cell RNA-Sequencing Data." Cell reports vol. 34,1 (2021): 108589. doi: https://doi.org/10.1016/j.celrep.2020.108589
Lu T, Park S, Zhu J, Wang Y, Zhan X, Wang X, Wang L, Zhu H, Wang T. Overcoming Expressional Drop-outs in Lineage Reconstruction from Single-Cell RNA-Sequencing Data. Cell Rep. 2021 Jan 05;34(1):108589. doi: 10.1016/j.celrep.2020.108589. PMID: 33406427.
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Inferring genes that escape X-Chromosome inactivation reveals important contribution of variable escape genes to sex-biased diseases.

Genome research

Sauteraud R, Stahl JM, James J, Englebright M, Chen F, Zhan X, Carrel L, Liu DJ.
PMID: 34426515
Genome Res. 2021 Sep;31(9):1629-1637. doi: 10.1101/gr.275677.121. Epub 2021 Aug 23.

The X Chromosome plays an important role in human development and disease. However, functional genomic and disease association studies of X genes greatly lag behind autosomal gene studies, in part owing to the unique biology of X-Chromosome inactivation (XCI)....

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Sauteraud R, Stahl JM, James J, et al. Inferring genes that escape X-Chromosome inactivation reveals important contribution of variable escape genes to sex-biased diseases. Genome Res. 2021;31(9):1629-1637doi: 10.1101/gr.275677.121.
Sauteraud, R., Stahl, J. M., James, J., Englebright, M., Chen, F., Zhan, X., Carrel, L., & Liu, D. J. (2021). Inferring genes that escape X-Chromosome inactivation reveals important contribution of variable escape genes to sex-biased diseases. Genome research, 31(9), 1629-1637. https://doi.org/10.1101/gr.275677.121
Sauteraud, Renan, et al. "Inferring genes that escape X-Chromosome inactivation reveals important contribution of variable escape genes to sex-biased diseases." Genome research vol. 31,9 (2021): 1629-1637. doi: https://doi.org/10.1101/gr.275677.121
Sauteraud R, Stahl JM, James J, Englebright M, Chen F, Zhan X, Carrel L, Liu DJ. Inferring genes that escape X-Chromosome inactivation reveals important contribution of variable escape genes to sex-biased diseases. Genome Res. 2021 Sep;31(9):1629-1637. doi: 10.1101/gr.275677.121. Epub 2021 Aug 23. PMID: 34426515; PMCID: PMC8415373.
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HARMONIES: A Hybrid Approach for Microbiome Networks Inference via Exploiting Sparsity.

Frontiers in genetics

Jiang S, Xiao G, Koh AY, Chen Y, Yao B, Li Q, Zhan X.
PMID: 32582274
Front Genet. 2020 Jun 03;11:445. doi: 10.3389/fgene.2020.00445. eCollection 2020.

The human microbiome is a collection of microorganisms. They form complex communities and collectively affect host health. Recently, the advances in next-generation sequencing technology enable the high-throughput profiling of the human microbiome. This calls for a statistical model to...

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Jiang S, Xiao G, Koh AY, et al. HARMONIES: A Hybrid Approach for Microbiome Networks Inference via Exploiting Sparsity. Front Genet. 2020;11:445doi: 10.3389/fgene.2020.00445.
Jiang, S., Xiao, G., Koh, A. Y., Chen, Y., Yao, B., Li, Q., & Zhan, X. (2020). HARMONIES: A Hybrid Approach for Microbiome Networks Inference via Exploiting Sparsity. Frontiers in genetics, 11445. https://doi.org/10.3389/fgene.2020.00445
Jiang, Shuang, et al. "HARMONIES: A Hybrid Approach for Microbiome Networks Inference via Exploiting Sparsity." Frontiers in genetics vol. 11 (2020): 445. doi: https://doi.org/10.3389/fgene.2020.00445
Jiang S, Xiao G, Koh AY, Chen Y, Yao B, Li Q, Zhan X. HARMONIES: A Hybrid Approach for Microbiome Networks Inference via Exploiting Sparsity. Front Genet. 2020 Jun 03;11:445. doi: 10.3389/fgene.2020.00445. eCollection 2020. PMID: 32582274; PMCID: PMC7283552.
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BayesSMILES: Bayesian Segmentation Modeling for Longitudinal Epidemiological Studies.

medRxiv : the preprint server for health sciences

Jiang S, Zhou Q, Zhan X, Li Q.
PMID: 33469604
medRxiv. 2021 Jan 18; doi: 10.1101/2020.10.06.20208132.

The coronavirus disease of 2019 (COVID-19) is a pandemic. To characterize its disease transmissibility, we propose a Bayesian change point detection model using daily actively infectious cases. Our model builds on a Bayesian Poisson segmented regression model that can...

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Jiang S, Zhou Q, Zhan X, et al. BayesSMILES: Bayesian Segmentation Modeling for Longitudinal Epidemiological Studies. medRxiv. 2021;doi: 10.1101/2020.10.06.20208132.
Jiang, S., Zhou, Q., Zhan, X., & Li, Q. (2021). BayesSMILES: Bayesian Segmentation Modeling for Longitudinal Epidemiological Studies. medRxiv : the preprint server for health sciences, . https://doi.org/10.1101/2020.10.06.20208132
Jiang, Shuang, et al. "BayesSMILES: Bayesian Segmentation Modeling for Longitudinal Epidemiological Studies." medRxiv : the preprint server for health sciences vol. (2021). doi: https://doi.org/10.1101/2020.10.06.20208132
Jiang S, Zhou Q, Zhan X, Li Q. BayesSMILES: Bayesian Segmentation Modeling for Longitudinal Epidemiological Studies. medRxiv. 2021 Jan 18; doi: 10.1101/2020.10.06.20208132. PMID: 33469604; PMCID: PMC7814850.
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Showing 1 to 4 of 4 entries