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本文引用的文献

1
A systematic evaluation of single cell RNA-seq analysis pipelines.单细胞 RNA 测序分析流程的系统评价。
Nat Commun. 2019 Oct 11;10(1):4667. doi: 10.1038/s41467-019-12266-7.
2
Accurate Classification of Differential Expression Patterns in a Bayesian Framework With Robust Normalization for Multi-Group RNA-Seq Count Data.在具有稳健归一化的贝叶斯框架中对多组RNA-Seq计数数据的差异表达模式进行准确分类
Bioinform Biol Insights. 2019 Jul 8;13:1177932219860817. doi: 10.1177/1177932219860817. eCollection 2019.
3
Silhouette Scores for Arbitrary Defined Groups in Gene Expression Data and Insights into Differential Expression Results.基因表达数据中任意定义组的轮廓系数及对差异表达结果的见解。
Biol Proced Online. 2018 Mar 1;20:5. doi: 10.1186/s12575-018-0067-8. eCollection 2018.
4
Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications.观测权重为零膨胀和单细胞应用解锁了批量 RNA-seq 工具。
Genome Biol. 2018 Feb 26;19(1):24. doi: 10.1186/s13059-018-1406-4.
5
powsimR: power analysis for bulk and single cell RNA-seq experiments.powsimR：用于批量和单细胞 RNA-seq 实验的功效分析。
Bioinformatics. 2017 Nov 1;33(21):3486-3488. doi: 10.1093/bioinformatics/btx435.
6
Selecting between-sample RNA-Seq normalization methods from the perspective of their assumptions.从假设的角度选择样本间 RNA-Seq 标准化方法。
Brief Bioinform. 2018 Sep 28;19(5):776-792. doi: 10.1093/bib/bbx008.
7
Pooling across cells to normalize single-cell RNA sequencing data with many zero counts.跨细胞合并以对具有大量零计数的单细胞RNA测序数据进行标准化。
Genome Biol. 2016 Apr 27;17:75. doi: 10.1186/s13059-016-0947-7.
8
How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?RNA测序实验需要多少生物学重复，以及应该使用哪种差异表达工具？
RNA. 2016 Jun;22(6):839-51. doi: 10.1261/rna.053959.115. Epub 2016 Mar 28.
9
Evaluation of methods for differential expression analysis on multi-group RNA-seq count data.多组RNA测序计数数据差异表达分析方法的评估
BMC Bioinformatics. 2015 Nov 4;16:361. doi: 10.1186/s12859-015-0794-7.
10
Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq.脑结构。单细胞 RNA 测序揭示的小鼠皮层和海马中的细胞类型。
Science. 2015 Mar 6;347(6226):1138-42. doi: 10.1126/science.aaa1934. Epub 2015 Feb 19.

Commentary: A Systematic Evaluation of Single Cell RNA-Seq Analysis Pipelines.

作者信息

Kadota Koji, Shimizu Kentaro

机构信息

Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan.

Collaborative Research Institute for Innovative Microbiology, The University of Tokyo, Tokyo, Japan.

出版信息

Front Genet. 2020 Sep 4;11:941. doi: 10.3389/fgene.2020.00941. eCollection 2020.

DOI:10.3389/fgene.2020.00941
PMID:33088280
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7500360/
Abstract
摘要