Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments-Reference-Cited by-同舟云学术

Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments

Published:2019-05-27 Issue:6 Volume:16 Page:479-487
ISSN:1548-7091
Container-title:Nature Methods
language:en
Short-container-title:Nat Methods

Author:

Tian Luyi^ORCID,Dong Xueyi,Freytag Saskia,Lê Cao Kim-Anh^ORCID,Su Shian,JalalAbadi Abolfazl,Amann-Zalcenstein Daniela,Weber Tom S.^ORCID,Seidi Azadeh,Jabbari Jafar S.,Naik Shalin H.^ORCID,Ritchie Matthew E.^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Cell Biology,Molecular Biology,Biochemistry,Biotechnology

Link

http://www.nature.com/articles/s41592-019-0425-8.pdf

Reference58 articles.

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2. Yip, S. H., Sham, P. C. & Wang, J. Evaluation of tools for highly variable gene discovery from single-cell RNA-seq data. Brief. Bioinform. https://doi.org/10.1093/bib/bby011 (2018).

3. Soneson, C. & Robinson, M. D. Bias, robustness and scalability in single-cell differential expression analysis. Nat. Methods 15, 255–261 (2018).

4. Freytag, S., Tian, L., Lönnstedt, I., Ng, M. & Bahlo, M. Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data. F1000Res. 7, 1297 (2018).

5. Duò, A., Robinson, M. D. & Soneson, C. A systematic performance evaluation of clustering methods for single-cell RNA-seq data. F1000Res. 7, 1141 (2018).

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