Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling-Reference-Cited by-同舟云学术

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Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling

Published:2019-09-09 Issue:10 Volume:16 Page:1007-1015
ISSN:1548-7091
Container-title:Nature Methods
language:en
Short-container-title:Nat Methods

Author:

Zhang Allen W.^ORCID,O’Flanagan Ciara,Chavez Elizabeth A.,Lim Jamie L. P.,Ceglia Nicholas,McPherson Andrew,Wiens Matt,Walters Pascale,Chan Tim,Hewitson Brittany,Lai Daniel^ORCID,Mottok Anja,Sarkozy Clementine,Chong Lauren,Aoki Tomohiro^ORCID,Wang Xuehai,Weng Andrew P,McAlpine Jessica N.,Aparicio Samuel^ORCID,Steidl Christian,Campbell Kieran R.^ORCID,Shah Sohrab P.^ORCID

Funder

Gouvernement du Canada | Canadian Institutes of Health Research

Canadian Cancer Society Research Institute

Cancer Research UK

University of British Columbia

Publisher

Springer Science and Business Media LLC

Subject

Cell Biology,Molecular Biology,Biochemistry,Biotechnology

Link

http://www.nature.com/articles/s41592-019-0529-1.pdf

Reference51 articles.

1. Schaum, N. et al. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 562, 367–372 (2018).

2. Kiselev, V. Y. et al. SC3: consensus clustering of single-cell RNA-seq data. Nat. Methods 14, 483–486 (2017).

3. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

4. Žurauskienė, J. & Yau, C. pcaReduce: hierarchical clustering of single cell transcriptional profiles. BMC Bioinformatics 17, 140 (2016).

5. Levine, J. H. et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).

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