Leveraging heterogeneity across multiple datasets increases cell-mixture deconvolution accuracy and reduces biological and technical biases-Reference-Cited by-同舟云学术

Leveraging heterogeneity across multiple datasets increases cell-mixture deconvolution accuracy and reduces biological and technical biases

Published:2018-11-09 Issue:1 Volume:9 Page:
ISSN:2041-1723
Container-title:Nature Communications
language:en
Short-container-title:Nat Commun

Author:

Vallania Francesco^ORCID,Tam Andrew,Lofgren Shane,Schaffert Steven,Azad Tej D.,Bongen Erika,Haynes Winston^ORCID,Alsup Meia,Alonso Michael,Davis Mark,Engleman Edgar,Khatri Purvesh^ORCID

Publisher

Springer Science and Business Media LLC

Subject

General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry

Link

http://www.nature.com/articles/s41467-018-07242-6.pdf

Reference26 articles.

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4. Gong, T. et al. Optimal deconvolution of transcriptional profiling data using quadratic programming with application to complex clinical blood samples. PLoS ONE 6, e27156 (2011).

5. Gaujoux, R. & Seoighe, C. CellMix: a comprehensive toolbox for gene expression deconvolution. Bioinformatics 29, 2211–2212 (2013).

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