Index by Author

October 2021; 31 (10)
  1. A
  2. B
  3. C
  4. D
  5. E
  6. F
  7. G
  8. H
  9. I
  10. J
  11. K
  12. L
  13. M
  14. N
  15. O
  16. P
  17. Q
  18. R
  19. S
  20. T
  21. U
  22. V
  23. W
  24. X
  25. Y
  26. Z

A

  1. Adey, Andrew C.

  2. Aevermann, Brian

    • A machine learning method for the discovery of minimum marker gene combinations for cell type identification from single-cell RNA sequencing Genome Res. October 2021 31: 1767-1780; Published in Advance June 4, 2021, doi:10.1101/gr.275569.121
      OPEN ACCESS ARTICLE

  3. Aguilar-Fernández, Sergio

  4. Alghamdi, Norah

    • A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data Genome Res. October 2021 31: 1867-1884; Published in Advance July 22, 2021, doi:10.1101/gr.271205.120
      OPEN ACCESS ARTICLE

  5. Amos, Christopher I.

    • Analysis of alternative polyadenylation from single-cell RNA-seq using scDaPars reveals cell subpopulations invisible to gene expression Genome Res. October 2021 31: 1856-1866; Published in Advance May 25, 2021, doi:10.1101/gr.271346.120

  6. Anholt, Robert R.H.

  7. Auerbach, Benjamin J.

    • Applications of single-cell genomics and computational strategies to study common disease and population-level variation Genome Res. October 2021 31: 1728-1741; doi:10.1101/gr.275430.121
      OPEN ACCESS ARTICLE

B

  1. Baker, Brandon M.

  2. Bakken, Trygve

    • A machine learning method for the discovery of minimum marker gene combinations for cell type identification from single-cell RNA sequencing Genome Res. October 2021 31: 1767-1780; Published in Advance June 4, 2021, doi:10.1101/gr.275569.121
      OPEN ACCESS ARTICLE

  3. Bambah-Mukku, Dhananjay

    • Characterizing spatial gene expression heterogeneity in spatially resolved single-cell transcriptomic data with nonuniform cellular densities Genome Res. October 2021 31: 1843-1855; Published in Advance May 25, 2021, doi:10.1101/gr.271288.120
      OPEN ACCESS ARTICLE

  4. Banisch, Torsten U.

    • A single-cell atlas reveals unanticipated cell type complexity in Drosophila ovaries Genome Res. October 2021 31: 1938-1951; Published in Advance August 13, 2021, doi:10.1101/gr.274340.120
      OPEN ACCESS ARTICLE

  5. Barkley, Dalia

    • Cancer cell states and emergent properties of the dynamic tumor system Genome Res. October 2021 31: 1719-1727; doi:10.1101/gr.275308.121

  6. Batlle, Eduard

  7. Bekris, Lynn M.

    • Multimodal single-cell/nucleus RNA sequencing data analysis uncovers molecular networks between disease-associated microglia and astrocytes with implications for drug repurposing in Alzheimer's disease Genome Res. October 2021 31: 1900-1912; Published in Advance February 24, 2021, doi:10.1101/gr.272484.120
      OPEN ACCESS ARTICLE

  8. Bolonduro, Olubusayo

    • Comprehensive characterization of tissue-specific chromatin accessibility in L2 Caenorhabditis elegans nematodes Genome Res. October 2021 31: 1952-1969; Published in Advance April 22, 2021, doi:10.1101/gr.271791.120
      OPEN ACCESS ARTICLE

C

  1. Cao, Sha

    • A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data Genome Res. October 2021 31: 1867-1884; Published in Advance July 22, 2021, doi:10.1101/gr.271205.120
      OPEN ACCESS ARTICLE

  2. Cao, Yaqiang

    • Profiling single-cell histone modifications using indexing chromatin immunocleavage sequencing Genome Res. October 2021 31: 1831-1842; Published in Advance April 14, 2021, doi:10.1101/gr.260893.120
      OPEN ACCESS ARTICLE

  3. Chan, Chia-Ling

  4. Chang, Wennan

    • A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data Genome Res. October 2021 31: 1867-1884; Published in Advance July 22, 2021, doi:10.1101/gr.271205.120
      OPEN ACCESS ARTICLE

  5. Chen, Bob

    • Automated quality control and cell identification of droplet-based single-cell data using dropkick Genome Res. October 2021 31: 1742-1752; Published in Advance April 9, 2021, doi:10.1101/gr.271908.120
      OPEN ACCESS ARTICLE

  6. Chen, Jiaji

    • Advances in spatial transcriptomic data analysis Genome Res. October 2021 31: 1706-1718; doi:10.1101/gr.275224.121
      OPEN ACCESS ARTICLE

  7. Cheng, Feixiong

    • Multimodal single-cell/nucleus RNA sequencing data analysis uncovers molecular networks between disease-associated microglia and astrocytes with implications for drug repurposing in Alzheimer's disease Genome Res. October 2021 31: 1900-1912; Published in Advance February 24, 2021, doi:10.1101/gr.272484.120
      OPEN ACCESS ARTICLE

  8. Chiang, Chien-Wei

    • Multimodal single-cell/nucleus RNA sequencing data analysis uncovers molecular networks between disease-associated microglia and astrocytes with implications for drug repurposing in Alzheimer's disease Genome Res. October 2021 31: 1900-1912; Published in Advance February 24, 2021, doi:10.1101/gr.272484.120
      OPEN ACCESS ARTICLE

  9. Chin, Vanessa T.

  10. Chou, Angela

  11. Cummings, Jeffrey

    • Multimodal single-cell/nucleus RNA sequencing data analysis uncovers molecular networks between disease-associated microglia and astrocytes with implications for drug repurposing in Alzheimer's disease Genome Res. October 2021 31: 1900-1912; Published in Advance February 24, 2021, doi:10.1101/gr.272484.120
      OPEN ACCESS ARTICLE

  12. Cusanovich, Darren A.

    • Comprehensive characterization of tissue-specific chromatin accessibility in L2 Caenorhabditis elegans nematodes Genome Res. October 2021 31: 1952-1969; Published in Advance April 22, 2021, doi:10.1101/gr.271791.120
      OPEN ACCESS ARTICLE

D

  1. Dang, Pengtao

    • A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data Genome Res. October 2021 31: 1867-1884; Published in Advance July 22, 2021, doi:10.1101/gr.271205.120
      OPEN ACCESS ARTICLE

  2. Daza, Riza M.

    • Comprehensive characterization of tissue-specific chromatin accessibility in L2 Caenorhabditis elegans nematodes Genome Res. October 2021 31: 1952-1969; Published in Advance April 22, 2021, doi:10.1101/gr.271791.120
      OPEN ACCESS ARTICLE

  3. del Rossi, Natalie

    • Advances in spatial transcriptomic data analysis Genome Res. October 2021 31: 1706-1718; doi:10.1101/gr.275224.121
      OPEN ACCESS ARTICLE

  4. Devlin, Bernie

    • Bayesian estimation of cell type–specific gene expression with prior derived from single-cell data Genome Res. October 2021 31: 1807-1818; Published in Advance April 9, 2021, doi:10.1101/gr.268722.120
      OPEN ACCESS ARTICLE

  5. Dries, Ruben

    • Advances in spatial transcriptomic data analysis Genome Res. October 2021 31: 1706-1718; doi:10.1101/gr.275224.121
      OPEN ACCESS ARTICLE

  6. Dulac, Catherine

    • Characterizing spatial gene expression heterogeneity in spatially resolved single-cell transcriptomic data with nonuniform cellular densities Genome Res. October 2021 31: 1843-1855; Published in Advance May 25, 2021, doi:10.1101/gr.271288.120
      OPEN ACCESS ARTICLE

  7. Durham, Timothy J.

    • Comprehensive characterization of tissue-specific chromatin accessibility in L2 Caenorhabditis elegans nematodes Genome Res. October 2021 31: 1952-1969; Published in Advance April 22, 2021, doi:10.1101/gr.271791.120
      OPEN ACCESS ARTICLE

E

  1. Elosua-Bayes, Marc

  2. Erdmann-Pham, Dan D.

F

  1. Fan, Jean

    • Characterizing spatial gene expression heterogeneity in spatially resolved single-cell transcriptomic data with nonuniform cellular densities Genome Res. October 2021 31: 1843-1855; Published in Advance May 25, 2021, doi:10.1101/gr.271288.120
      OPEN ACCESS ARTICLE

  2. Fischer, Jonathan

  3. Fishel, Melissa

    • A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data Genome Res. October 2021 31: 1867-1884; Published in Advance July 22, 2021, doi:10.1101/gr.271205.120
      OPEN ACCESS ARTICLE

  4. França, Gustavo S.

    • Cancer cell states and emergent properties of the dynamic tumor system Genome Res. October 2021 31: 1719-1727; doi:10.1101/gr.275308.121

G

  1. Gallagher, Richard

  2. Gampala, Silpa

    • A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data Genome Res. October 2021 31: 1867-1884; Published in Advance July 22, 2021, doi:10.1101/gr.271205.120
      OPEN ACCESS ARTICLE

  3. Gao, Weiwu

    • Profiling single-cell histone modifications using indexing chromatin immunocleavage sequencing Genome Res. October 2021 31: 1831-1842; Published in Advance April 14, 2021, doi:10.1101/gr.260893.120
      OPEN ACCESS ARTICLE

  4. Gao, Yipeng

    • Analysis of alternative polyadenylation from single-cell RNA-seq using scDaPars reveals cell subpopulations invisible to gene expression Genome Res. October 2021 31: 1856-1866; Published in Advance May 25, 2021, doi:10.1101/gr.271346.120

  5. Gevirtzman, Louis

    • Comprehensive characterization of tissue-specific chromatin accessibility in L2 Caenorhabditis elegans nematodes Genome Res. October 2021 31: 1952-1969; Published in Advance April 22, 2021, doi:10.1101/gr.271791.120
      OPEN ACCESS ARTICLE

  6. Gupta, Selena

    • A single-cell atlas reveals unanticipated cell type complexity in Drosophila ovaries Genome Res. October 2021 31: 1938-1951; Published in Advance August 13, 2021, doi:10.1101/gr.274340.120
      OPEN ACCESS ARTICLE

  7. Gut, Ivo

H

  1. Hannah, Rachel C.

  2. Hatfield, Jeffrey S.

  3. Heiser, Cody N.

    • Automated quality control and cell identification of droplet-based single-cell data using dropkick Genome Res. October 2021 31: 1742-1752; Published in Advance April 9, 2021, doi:10.1101/gr.271908.120
      OPEN ACCESS ARTICLE

  4. Henriques, Ana

  5. Heyn, Holger

  6. Hodge, Rebecca

    • A machine learning method for the discovery of minimum marker gene combinations for cell type identification from single-cell RNA sequencing Genome Res. October 2021 31: 1767-1780; Published in Advance June 4, 2021, doi:10.1101/gr.275569.121
      OPEN ACCESS ARTICLE

  7. Hong, Justin

  8. Hou, Yuan

    • Multimodal single-cell/nucleus RNA sequencing data analysis uncovers molecular networks between disease-associated microglia and astrocytes with implications for drug repurposing in Alzheimer's disease Genome Res. October 2021 31: 1900-1912; Published in Advance February 24, 2021, doi:10.1101/gr.272484.120
      OPEN ACCESS ARTICLE

  9. Hu, Gang

    • A joint deep learning model enables simultaneous batch effect correction, denoising, and clustering in single-cell transcriptomics Genome Res. October 2021 31: 1753-1766; Published in Advance May 25, 2021, doi:10.1101/gr.271874.120
      OPEN ACCESS ARTICLE

  10. Hu, Jian

    • Applications of single-cell genomics and computational strategies to study common disease and population-level variation Genome Res. October 2021 31: 1728-1741; doi:10.1101/gr.275430.121
      OPEN ACCESS ARTICLE

  11. Huang, Yin

    • Multimodal single-cell/nucleus RNA sequencing data analysis uncovers molecular networks between disease-associated microglia and astrocytes with implications for drug repurposing in Alzheimer's disease Genome Res. October 2021 31: 1900-1912; Published in Advance February 24, 2021, doi:10.1101/gr.272484.120
      OPEN ACCESS ARTICLE

  12. Huang, Zhi

    • A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data Genome Res. October 2021 31: 1867-1884; Published in Advance July 22, 2021, doi:10.1101/gr.271205.120
      OPEN ACCESS ARTICLE

  13. Hughey, Jacob J.

    • Automated quality control and cell identification of droplet-based single-cell data using dropkick Genome Res. October 2021 31: 1742-1752; Published in Advance April 9, 2021, doi:10.1101/gr.271908.120
      OPEN ACCESS ARTICLE

J

  1. Jones, Scott

K

  1. Kaczorowski, Dominik

  2. Kelley, David R.

  3. Keshk, Mohamed

    • A machine learning method for the discovery of minimum marker gene combinations for cell type identification from single-cell RNA sequencing Genome Res. October 2021 31: 1767-1780; Published in Advance June 4, 2021, doi:10.1101/gr.275569.121
      OPEN ACCESS ARTICLE

  4. Khan, Mohammed Muzamil

    • Advances in spatial transcriptomic data analysis Genome Res. October 2021 31: 1706-1718; doi:10.1101/gr.275224.121
      OPEN ACCESS ARTICLE

  5. Kimmel, Jacob C.

  6. Ku, Wai Lim

    • Profiling single-cell histone modifications using indexing chromatin immunocleavage sequencing Genome Res. October 2021 31: 1831-1842; Published in Advance April 14, 2021, doi:10.1101/gr.260893.120
      OPEN ACCESS ARTICLE

L

  1. Lakkis, Justin

    • A joint deep learning model enables simultaneous batch effect correction, denoising, and clustering in single-cell transcriptomics Genome Res. October 2021 31: 1753-1766; Published in Advance May 25, 2021, doi:10.1101/gr.271874.120
      OPEN ACCESS ARTICLE

  2. Lathia, Justin

    • Multimodal single-cell/nucleus RNA sequencing data analysis uncovers molecular networks between disease-associated microglia and astrocytes with implications for drug repurposing in Alzheimer's disease Genome Res. October 2021 31: 1900-1912; Published in Advance February 24, 2021, doi:10.1101/gr.272484.120
      OPEN ACCESS ARTICLE

  3. Lau, Ken S.

    • Automated quality control and cell identification of droplet-based single-cell data using dropkick Genome Res. October 2021 31: 1742-1752; Published in Advance April 9, 2021, doi:10.1101/gr.271908.120
      OPEN ACCESS ARTICLE

  4. Lee, Mary P.

  5. Lehmann, Ruth

    • A single-cell atlas reveals unanticipated cell type complexity in Drosophila ovaries Genome Res. October 2021 31: 1938-1951; Published in Advance August 13, 2021, doi:10.1101/gr.274340.120
      OPEN ACCESS ARTICLE

  6. Lein, Ed

    • A machine learning method for the discovery of minimum marker gene combinations for cell type identification from single-cell RNA sequencing Genome Res. October 2021 31: 1767-1780; Published in Advance June 4, 2021, doi:10.1101/gr.275569.121
      OPEN ACCESS ARTICLE

  7. Lelieveldt, Boudewijn

    • A machine learning method for the discovery of minimum marker gene combinations for cell type identification from single-cell RNA sequencing Genome Res. October 2021 31: 1767-1780; Published in Advance June 4, 2021, doi:10.1101/gr.275569.121
      OPEN ACCESS ARTICLE

  8. Leverenz, James B.

    • Multimodal single-cell/nucleus RNA sequencing data analysis uncovers molecular networks between disease-associated microglia and astrocytes with implications for drug repurposing in Alzheimer's disease Genome Res. October 2021 31: 1900-1912; Published in Advance February 24, 2021, doi:10.1101/gr.272484.120
      OPEN ACCESS ARTICLE

  9. Li, Lang

    • Multimodal single-cell/nucleus RNA sequencing data analysis uncovers molecular networks between disease-associated microglia and astrocytes with implications for drug repurposing in Alzheimer's disease Genome Res. October 2021 31: 1900-1912; Published in Advance February 24, 2021, doi:10.1101/gr.272484.120
      OPEN ACCESS ARTICLE

  10. Li, Lei

    • Analysis of alternative polyadenylation from single-cell RNA-seq using scDaPars reveals cell subpopulations invisible to gene expression Genome Res. October 2021 31: 1856-1866; Published in Advance May 25, 2021, doi:10.1101/gr.271346.120

  11. Li, Mingyao

    • Applications of single-cell genomics and computational strategies to study common disease and population-level variation Genome Res. October 2021 31: 1728-1741; doi:10.1101/gr.275430.121
      OPEN ACCESS ARTICLE
    • A joint deep learning model enables simultaneous batch effect correction, denoising, and clustering in single-cell transcriptomics Genome Res. October 2021 31: 1753-1766; Published in Advance May 25, 2021, doi:10.1101/gr.271874.120
      OPEN ACCESS ARTICLE

  12. Li, Wei

    • Analysis of alternative polyadenylation from single-cell RNA-seq using scDaPars reveals cell subpopulations invisible to gene expression Genome Res. October 2021 31: 1856-1866; Published in Advance May 25, 2021, doi:10.1101/gr.271346.120

  13. Li, Xiangjie

    • A joint deep learning model enables simultaneous batch effect correction, denoising, and clustering in single-cell transcriptomics Genome Res. October 2021 31: 1753-1766; Published in Advance May 25, 2021, doi:10.1101/gr.271874.120
      OPEN ACCESS ARTICLE

  14. Liu, Jie

  15. Lobanov, Alexei

  16. Lorden, Patricia

  17. Lu, Xiaoyu

    • A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data Genome Res. October 2021 31: 1867-1884; Published in Advance July 22, 2021, doi:10.1101/gr.271205.120
      OPEN ACCESS ARTICLE

M

  1. Ma, Qin

    • A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data Genome Res. October 2021 31: 1867-1884; Published in Advance July 22, 2021, doi:10.1101/gr.271205.120
      OPEN ACCESS ARTICLE

  2. Mackay, Trudy F.C.

  3. Marchese, Domenica

  4. Massoni-Badosa, Ramon

  5. Mereu, Elisabetta

  6. Miller, Brendan F.

    • Characterizing spatial gene expression heterogeneity in spatially resolved single-cell transcriptomic data with nonuniform cellular densities Genome Res. October 2021 31: 1843-1855; Published in Advance May 25, 2021, doi:10.1101/gr.271288.120
      OPEN ACCESS ARTICLE

  7. Miller, Jeremy

    • A machine learning method for the discovery of minimum marker gene combinations for cell type identification from single-cell RNA sequencing Genome Res. October 2021 31: 1767-1780; Published in Advance June 4, 2021, doi:10.1101/gr.275569.121
      OPEN ACCESS ARTICLE

  8. Mokashi, Sneha S.

  9. Moutinho, Catia

N

  1. Navin, Nicholas E.

  2. Nieto, Juan

  3. Nieto, Paula

  4. Noble, William Stafford

    • Comprehensive characterization of tissue-specific chromatin accessibility in L2 Caenorhabditis elegans nematodes Genome Res. October 2021 31: 1952-1969; Published in Advance April 22, 2021, doi:10.1101/gr.271791.120
      OPEN ACCESS ARTICLE

  5. Novotny, Mark

    • A machine learning method for the discovery of minimum marker gene combinations for cell type identification from single-cell RNA sequencing Genome Res. October 2021 31: 1767-1780; Published in Advance June 4, 2021, doi:10.1101/gr.275569.121
      OPEN ACCESS ARTICLE

O

  1. Ohnuki, Hidetaka

P

  1. Pan, Huize

    • A joint deep learning model enables simultaneous batch effect correction, denoising, and clustering in single-cell transcriptomics Genome Res. October 2021 31: 1753-1766; Published in Advance May 25, 2021, doi:10.1101/gr.271874.120
      OPEN ACCESS ARTICLE

  2. Pan, Lixia

    • Profiling single-cell histone modifications using indexing chromatin immunocleavage sequencing Genome Res. October 2021 31: 1831-1842; Published in Advance April 14, 2021, doi:10.1101/gr.260893.120
      OPEN ACCESS ARTICLE

  3. Pieper, Andrew A.

    • Multimodal single-cell/nucleus RNA sequencing data analysis uncovers molecular networks between disease-associated microglia and astrocytes with implications for drug repurposing in Alzheimer's disease Genome Res. October 2021 31: 1900-1912; Published in Advance February 24, 2021, doi:10.1101/gr.272484.120
      OPEN ACCESS ARTICLE

  4. Piulats, Josep M.

  5. Planas-Rigol, Ester

  6. Pour, Maayan

    • Cancer cell states and emergent properties of the dynamic tumor system Genome Res. October 2021 31: 1719-1727; doi:10.1101/gr.275308.121

  7. Powell, Joseph E.

R

  1. Rao, Anjali

    • Cancer cell states and emergent properties of the dynamic tumor system Genome Res. October 2021 31: 1719-1727; doi:10.1101/gr.275308.121

  2. Reilly, Muredach P.

    • Applications of single-cell genomics and computational strategies to study common disease and population-level variation Genome Res. October 2021 31: 1728-1741; doi:10.1101/gr.275430.121
      OPEN ACCESS ARTICLE
    • A joint deep learning model enables simultaneous batch effect correction, denoising, and clustering in single-cell transcriptomics Genome Res. October 2021 31: 1753-1766; Published in Advance May 25, 2021, doi:10.1101/gr.271874.120
      OPEN ACCESS ARTICLE

  3. Roeder, Kathryn

    • Bayesian estimation of cell type–specific gene expression with prior derived from single-cell data Genome Res. October 2021 31: 1807-1818; Published in Advance April 9, 2021, doi:10.1101/gr.268722.120
      OPEN ACCESS ARTICLE

  4. Rozenblatt-Rosen, Orit

  5. Rubio-Perez, Carlota

  6. Ruiz, Sara

S

  1. Salvany, Maria

  2. Scheuermann, Richard H.

    • A machine learning method for the discovery of minimum marker gene combinations for cell type identification from single-cell RNA sequencing Genome Res. October 2021 31: 1767-1780; Published in Advance June 4, 2021, doi:10.1101/gr.275569.121
      OPEN ACCESS ARTICLE

  3. Seoane, Joan

  4. Shankar, Vijay

  5. Shendure, Jay

    • Comprehensive characterization of tissue-specific chromatin accessibility in L2 Caenorhabditis elegans nematodes Genome Res. October 2021 31: 1952-1969; Published in Advance April 22, 2021, doi:10.1101/gr.271791.120
      OPEN ACCESS ARTICLE

  6. Sistig, Adriana

    • Advances in spatial transcriptomic data analysis Genome Res. October 2021 31: 1706-1718; doi:10.1101/gr.275224.121
      OPEN ACCESS ARTICLE

  7. Slaidina, Maija

    • A single-cell atlas reveals unanticipated cell type complexity in Drosophila ovaries Genome Res. October 2021 31: 1938-1951; Published in Advance August 13, 2021, doi:10.1101/gr.274340.120
      OPEN ACCESS ARTICLE

  8. Song, Yun S.

T

  1. Tosato, Giovanna

  2. Trincado, Juan L.

U

  1. Ungar, Lyle

    • A joint deep learning model enables simultaneous batch effect correction, denoising, and clustering in single-cell transcriptomics Genome Res. October 2021 31: 1753-1766; Published in Advance May 25, 2021, doi:10.1101/gr.271874.120
      OPEN ACCESS ARTICLE

V

  1. Venzon, David J.

W

  1. Waldhaus, Joerg

  2. Wan, Changlin

    • A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data Genome Res. October 2021 31: 1867-1884; Published in Advance July 22, 2021, doi:10.1101/gr.271205.120
      OPEN ACCESS ARTICLE

  3. Wang, David

    • A joint deep learning model enables simultaneous batch effect correction, denoising, and clustering in single-cell transcriptomics Genome Res. October 2021 31: 1753-1766; Published in Advance May 25, 2021, doi:10.1101/gr.271874.120
      OPEN ACCESS ARTICLE

  4. Wang, Jiashi

    • A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data Genome Res. October 2021 31: 1867-1884; Published in Advance July 22, 2021, doi:10.1101/gr.271205.120
      OPEN ACCESS ARTICLE

  5. Wang, Jiebiao

    • Bayesian estimation of cell type–specific gene expression with prior derived from single-cell data Genome Res. October 2021 31: 1807-1818; Published in Advance April 9, 2021, doi:10.1101/gr.268722.120
      OPEN ACCESS ARTICLE

  6. Wang, Kui

    • A joint deep learning model enables simultaneous batch effect correction, denoising, and clustering in single-cell transcriptomics Genome Res. October 2021 31: 1753-1766; Published in Advance May 25, 2021, doi:10.1101/gr.271874.120
      OPEN ACCESS ARTICLE

  7. Wang, Shuze

  8. Wang, Victoria M.

    • Automated quality control and cell identification of droplet-based single-cell data using dropkick Genome Res. October 2021 31: 1742-1752; Published in Advance April 9, 2021, doi:10.1101/gr.271908.120
      OPEN ACCESS ARTICLE

  9. Waterston, Robert H.

    • Comprehensive characterization of tissue-specific chromatin accessibility in L2 Caenorhabditis elegans nematodes Genome Res. October 2021 31: 1952-1969; Published in Advance April 22, 2021, doi:10.1101/gr.271791.120
      OPEN ACCESS ARTICLE

X

  1. Xu, Jielin

    • Multimodal single-cell/nucleus RNA sequencing data analysis uncovers molecular networks between disease-associated microglia and astrocytes with implications for drug repurposing in Alzheimer's disease Genome Res. October 2021 31: 1900-1912; Published in Advance February 24, 2021, doi:10.1101/gr.272484.120
      OPEN ACCESS ARTICLE

Y

  1. Yanai, Itai

    • Cancer cell states and emergent properties of the dynamic tumor system Genome Res. October 2021 31: 1719-1727; doi:10.1101/gr.275308.121

  2. Yuan, Guo-Cheng

    • Advances in spatial transcriptomic data analysis Genome Res. October 2021 31: 1706-1718; doi:10.1101/gr.275224.121
      OPEN ACCESS ARTICLE

Z

  1. Zang, Yong

    • A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data Genome Res. October 2021 31: 1867-1884; Published in Advance July 22, 2021, doi:10.1101/gr.271205.120
      OPEN ACCESS ARTICLE

  2. Zhang, Chi

    • A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data Genome Res. October 2021 31: 1867-1884; Published in Advance July 22, 2021, doi:10.1101/gr.271205.120
      OPEN ACCESS ARTICLE

  3. Zhang, Nancy R.

  4. Zhang, Pengyue

    • Multimodal single-cell/nucleus RNA sequencing data analysis uncovers molecular networks between disease-associated microglia and astrocytes with implications for drug repurposing in Alzheimer's disease Genome Res. October 2021 31: 1900-1912; Published in Advance February 24, 2021, doi:10.1101/gr.272484.120
      OPEN ACCESS ARTICLE

  5. Zhang, Yuanchao

    • A joint deep learning model enables simultaneous batch effect correction, denoising, and clustering in single-cell transcriptomics Genome Res. October 2021 31: 1753-1766; Published in Advance May 25, 2021, doi:10.1101/gr.271874.120
      OPEN ACCESS ARTICLE

  6. Zhang, Yun

    • A machine learning method for the discovery of minimum marker gene combinations for cell type identification from single-cell RNA sequencing Genome Res. October 2021 31: 1767-1780; Published in Advance June 4, 2021, doi:10.1101/gr.275569.121
      OPEN ACCESS ARTICLE

  7. Zhao, Keji

    • Profiling single-cell histone modifications using indexing chromatin immunocleavage sequencing Genome Res. October 2021 31: 1831-1842; Published in Advance April 14, 2021, doi:10.1101/gr.260893.120
      OPEN ACCESS ARTICLE

  8. Zhou, Yadi

    • Multimodal single-cell/nucleus RNA sequencing data analysis uncovers molecular networks between disease-associated microglia and astrocytes with implications for drug repurposing in Alzheimer's disease Genome Res. October 2021 31: 1900-1912; Published in Advance February 24, 2021, doi:10.1101/gr.272484.120
      OPEN ACCESS ARTICLE

  9. Zhuang, Xiaowei

    • Characterizing spatial gene expression heterogeneity in spatially resolved single-cell transcriptomic data with nonuniform cellular densities Genome Res. October 2021 31: 1843-1855; Published in Advance May 25, 2021, doi:10.1101/gr.271288.120
      OPEN ACCESS ARTICLE

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