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Akademický článek

RNA splicing programs define tissue compartments and cell types at single-cell resolution

Autor: Julia Eve Olivieri, Roozbeh Dehghannasiri, Peter L Wang, SoRi Jang, Antoine de Morree, Serena Y Tan, Jingsi Ming, Angela Ruohao Wu, Tabula Sapiens Consortium, Stephen R Quake, Mark A Krasnow, Julia Salzman

Publikováno v: eLife, Vol 10 (2021)

The extent splicing is regulated at single-cell resolution has remained controversial due to both available data and methods to interpret it. We apply the SpliZ, a new statistical approach, to detect cell-type-specific splicing in >110K cells from 12

Externí odkaz: https://doaj.org/article/541656ebdf3d4406a1040ea2c1b0a374

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scPI: A Scalable Framework for Probabilistic Inference in Single-Cell RNA-Sequencing Data Analysis

Autor: Jingsi Ming, Jia Zhao, Can Yang

Publikováno v: Statistics in Biosciences.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::1f8553d20de776a4b5f3b66eba8e87d5
https://doi.org/10.1007/s12561-022-09335-9

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Adversarial domain translation networks for fast and accurate integration of large-scale atlas-level single-cell datasets

Autor: Can Yang, Angela Ruohao Wu, Jingsi Ming, Zhixiang Lin, Gefei Wang, Yang Wang, Jia Zhao

The rapid emergence of large-scale atlas-level single-cell RNA-sequencing (scRNA-seq) datasets from various sources presents remarkable opportunities for broad and deep biological investigations through integrative analyses. However, harmonizing such

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a2169e3a26cdab5c832e4a09712bebfd
https://doi.org/10.1101/2021.11.16.468892

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Author response: RNA splicing programs define tissue compartments and cell types at single-cell resolution

Autor: Peter L. Wang, Serena Y. Tan, Julia Salzman, Angela Ruohao Wu, Mark A. Krasnow, Jingsi Ming, Julia Eve Olivieri, Antoine de Morrée, SoRi Jang, Roozbeh Dehghannasiri, Stephen R. Quake

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::5674660e8a4a4009d84dc2ad452268fe
https://doi.org/10.7554/elife.70692.sa2

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RNA splicing programs define tissue compartments and cell types at single cell resolution

Autor: Angela Ruohao Wu, Julia Eve Olivieri, Peter L. Wang, Stephen R. Quake, Mark A. Krasnow, Roozbeh Dehghannasiri, Julia Salzman, SoRi Jang, Antoine de Morrée, Serena Y. Tan, Jingsi Ming

More than 95% of human genes are alternatively spliced. Yet, the extent splicing is regulated at single-cell resolution has remained controversial due to both available data and methods to interpret it. We apply the SpliZ, a new statistical approach

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::4a8207e02b83d25f51a1a66b1b925809
https://doi.org/10.1101/2021.05.01.442281

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FIRM: Flexible Integration of single-cell RNA-sequencing data for large-scale Multi-tissue cell atlas datasets

Autor: Can Yang, Angela Ruohao Wu, Xiang Wan, Jia Zhao, Zhixiang Lin, Jingsi Ming

Single-cell RNA-sequencing (scRNA-seq) is being used extensively to measure the mRNA expression of individual cells from deconstructed tissues, organs, and even entire organisms to generate cell atlas references, leading to discoveries of novel cell

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::60ad760c06dc2084f630aa7089f0aaff
https://doi.org/10.1101/2020.06.02.129031

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LSMM: a statistical approach to integrating functional annotations with genome-wide association studies

Autor: Xiang Wan, Can Yang, Jingsi Ming, Jin Liu, Mingwei Dai, Mingxuan Cai

Publikováno v: Bioinformatics. 34:2788-2796

Motivation Thousands of risk variants underlying complex phenotypes (quantitative traits and diseases) have been identified in genome-wide association studies (GWAS). However, there are still two major challenges towards deepening our understanding o

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::93e08194403879dbd7382baa70aa359f
https://doi.org/10.1093/bioinformatics/bty187

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Bayesian weighted Mendelian randomization for causal inference based on summary statistics

Autor: Gang Chen, Xianghong Hu, Jingsi Ming, Jin Liu, Can Yang, Jia Zhao

Publikováno v: Bioinformatics (Oxford, England). 36(5)

Motivation The results from Genome-Wide Association Studies (GWAS) on thousands of phenotypes provide an unprecedented opportunity to infer the causal effect of one phenotype (exposure) on another (outcome). Mendelian randomization (MR), an instrumen

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::07ac9efd404754ce0b0dd1f9f7132ffc
https://pubmed.ncbi.nlm.nih.gov/31593215

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Akademický článek

RNA splicing programs define tissue compartments and cell types at single-cell resolution.

Autor: Olivieri, Julia Eve, Dehghannasiri, Roozbeh, Wang, Peter L., SoRi Jang, de Morree, Antoine, Tan, Serena Y., Jingsi Ming, Wu, Angela Ruohao, Consortium, Tabula Sapiens, Quake, Stephen R., Krasnow, Mark A., Salzman, Julia

Publikováno v: eLife; 11/2/2021, p1-32, 32p

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BIVAS: A scalable Bayesian method for bi-level variable selection with applications

Autor: Mingxuan Cai, Can Yang, Jingsi Ming, Jin Liu, Heng Peng, Mingwei Dai

In this paper, we consider a Bayesian bi-level variable selection problem in high-dimensional regressions. In many practical situations, it is natural to assign group membership to each predictor. Examples include that genetic variants can be grouped

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::13e9523ccdcb960cac70e95f9415eeb4
http://arxiv.org/abs/1803.10439

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