Tumor Copy Number Deconvolution Integrating Bulk and Single-Cell Sequencing Data-Reference-Cited by-同舟云学术

Tumor Copy Number Deconvolution Integrating Bulk and Single-Cell Sequencing Data

Published:2020-04-01 Issue:4 Volume:27 Page:565-598
ISSN:1557-8666
Container-title:Journal of Computational Biology
language:en
Short-container-title:Journal of Computational Biology

Author:

Lei Haoyun¹,Lyu Bochuan²,Gertz E. Michael³⁴,Schäffer Alejandro A.³⁴,Shi Xulian⁵,Wu Kui⁵,Li Guibo⁵,Xu Liqin⁵,Hou Yong⁵,Dean Michael⁶,Schwartz Russell¹⁷

Affiliation:

1. Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania.

2. Department of Mathematics, Rose-Hulman Institute of Technology, Terre Haute, Indiana.

3. National Center for Biotechnology Information, U.S. National Institutes of Health, Bethesda, Maryland.

4. Cancer Data Science Laboratory, National Cancer Institute, U.S. National Institutes of Health, Bethesda, Maryland.

5. BGI-Shenzhen, Shenzhen, China.

6. Laboratory of Translational Genomics, Division of Cancer Epidemiology & Genetics, National Cancer Institute, U.S. National Institutes of Health, Gaithersburg, Maryland.

7. Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania.

Publisher

Mary Ann Liebert Inc

Subject

Computational Theory and Mathematics,Computational Mathematics,Genetics,Molecular Biology,Modeling and Simulation

Link

https://www.liebertpub.com/doi/pdf/10.1089/cmb.2019.0302

Reference61 articles.

1. Comprehensive molecular characterization of multifocal glioblastoma proves its monoclonal origin and reveals novel insights into clonal evolution and heterogeneity of glioblastomas

2. Dissecting cancer evolution at the macro-heterogeneity and micro-heterogeneity scale

3. Genome-wide copy number analysis of single cells

4. Algorithms and applications for approximate nonnegative matrix factorization

5. The Somatic Genomic Landscape of Glioblastoma

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