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Two-phase differential expression analysis for single cell RNA-seq.单细胞 RNA-seq 的两阶段差异表达分析。
Bioinformatics. 2018 Oct 1;34(19):3340-3348. doi: 10.1093/bioinformatics/bty329.
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DECENT: differential expression with capture efficiency adjustmeNT for single-cell RNA-seq data.DECENT：用于单细胞 RNA-seq 数据的捕获效率调整的差异表达分析。
Bioinformatics. 2019 Dec 15;35(24):5155-5162. doi: 10.1093/bioinformatics/btz453.
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DEsingle for detecting three types of differential expression in single-cell RNA-seq data.DEsingle 用于检测单细胞 RNA-seq 数据中的三种差异表达。
Bioinformatics. 2018 Sep 15;34(18):3223-3224. doi: 10.1093/bioinformatics/bty332.
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Scalable preprocessing for sparse scRNA-seq data exploiting prior knowledge.利用先验知识对稀疏 scRNA-seq 数据进行可扩展的预处理。
Bioinformatics. 2018 Jul 1;34(13):i124-i132. doi: 10.1093/bioinformatics/bty293.
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Cell-level somatic mutation detection from single-cell RNA sequencing.单细胞 RNA 测序中单细胞体细胞突变检测
Bioinformatics. 2019 Nov 1;35(22):4679-4687. doi: 10.1093/bioinformatics/btz288.
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CONICS integrates scRNA-seq with DNA sequencing to map gene expression to tumor sub-clones.CONICS 将单细胞 RNA-seq 与 DNA 测序相结合，将基因表达映射到肿瘤亚克隆。
Bioinformatics. 2018 Sep 15;34(18):3217-3219. doi: 10.1093/bioinformatics/bty316.
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ZIAQ: a quantile regression method for differential expression analysis of single-cell RNA-seq data.ZIAQ：一种用于单细胞 RNA-seq 数据差异表达分析的分位数回归方法。
Bioinformatics. 2020 May 1;36(10):3124-3130. doi: 10.1093/bioinformatics/btaa098.
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bayNorm: Bayesian gene expression recovery, imputation and normalization for single-cell RNA-sequencing data.bayNorm：用于单细胞 RNA-seq 数据的贝叶斯基因表达恢复、插补和标准化。
Bioinformatics. 2020 Feb 15;36(4):1174-1181. doi: 10.1093/bioinformatics/btz726.
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PROPER: comprehensive power evaluation for differential expression using RNA-seq.正确的：使用RNA测序对差异表达进行综合能力评估。
Bioinformatics. 2015 Jan 15;31(2):233-41. doi: 10.1093/bioinformatics/btu640. Epub 2014 Oct 1.
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Hierarchical analysis of RNA-seq reads improves the accuracy of allele-specific expression.基于层次分析的 RNA-seq 测序reads 提高了等位基因特异性表达的准确性。
Bioinformatics. 2018 Jul 1;34(13):2177-2184. doi: 10.1093/bioinformatics/bty078.
引用本文的文献
1
iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects.iDESC：基于多个样本的单细胞 RNA 测序数据差异表达识别。
BMC Bioinformatics. 2023 Aug 22;24(1):318. doi: 10.1186/s12859-023-05432-8.
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A high-efficiency differential expression method for cancer heterogeneity using large-scale single-cell RNA-sequencing data.一种利用大规模单细胞RNA测序数据研究癌症异质性的高效差异表达方法。
Front Genet. 2022 Nov 29;13:1063130. doi: 10.3389/fgene.2022.1063130. eCollection 2022.
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SAREV: A review on statistical analytics of single-cell RNA sequencing data.SAREV：单细胞RNA测序数据的统计分析综述
Wiley Interdiscip Rev Comput Stat. 2022 Jul-Aug;14(4). doi: 10.1002/wics.1558. Epub 2021 May 20.
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Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges.单细胞RNA测序数据的差异表达分析：当前的统计方法与突出挑战
Entropy (Basel). 2022 Jul 18;24(7):995. doi: 10.3390/e24070995.
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Differential expression of single-cell RNA-seq data using Tweedie models.基于 Tweedie 模型的单细胞 RNA-seq 数据差异表达分析。
Stat Med. 2022 Aug 15;41(18):3492-3510. doi: 10.1002/sim.9430. Epub 2022 Jun 2.
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A Comprehensive Survey of Statistical Approaches for Differential Expression Analysis in Single-Cell RNA Sequencing Studies.单细胞 RNA 测序研究中差异表达分析的统计方法综合综述。
Genes (Basel). 2021 Dec 2;12(12):1947. doi: 10.3390/genes12121947.
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Network Modeling in Biology: Statistical Methods for Gene and Brain Networks.生物学中的网络建模：基因与大脑网络的统计方法
Stat Sci. 2021 Feb;36(1):89-108. doi: 10.1214/20-sts792.
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Non-linear Normalization for Non-UMI Single Cell RNA-Seq.非UMI单细胞RNA测序的非线性归一化
Front Genet. 2021 Apr 9;12:612670. doi: 10.3389/fgene.2021.612670. eCollection 2021.
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Mixed Distribution Models Based on Single-Cell RNA Sequencing Data.基于单细胞 RNA 测序数据的混合分布模型。
Interdiscip Sci. 2021 Sep;13(3):362-370. doi: 10.1007/s12539-021-00427-6. Epub 2021 Mar 22.
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A hierarchical Bayesian mixture model for inferring the expression state of genes in transcriptomes.用于推断转录组中基因表达状态的分层贝叶斯混合模型。
Proc Natl Acad Sci U S A. 2020 Aug 11;117(32):19339-19346. doi: 10.1073/pnas.1919748117. Epub 2020 Jul 24.
本文引用的文献
1
SAVER: gene expression recovery for single-cell RNA sequencing.SAVER：单细胞 RNA 测序的基因表达恢复。
Nat Methods. 2018 Jul;15(7):539-542. doi: 10.1038/s41592-018-0033-z. Epub 2018 Jun 25.
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CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data.CIDR：通过对单细胞RNA测序数据进行插补实现超快速且准确的聚类
Genome Biol. 2017 Mar 28;18(1):59. doi: 10.1186/s13059-017-1188-0.
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SC3: consensus clustering of single-cell RNA-seq data.SC3：单细胞RNA测序数据的一致性聚类
Nat Methods. 2017 May;14(5):483-486. doi: 10.1038/nmeth.4236. Epub 2017 Mar 27.
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Single-cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes.单细胞转录组鉴定人类胰岛细胞特征并揭示2型糖尿病中细胞类型特异性表达变化。
Genome Res. 2017 Feb;27(2):208-222. doi: 10.1101/gr.212720.116. Epub 2016 Nov 18.
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A statistical approach for identifying differential distributions in single-cell RNA-seq experiments.一种用于识别单细胞RNA测序实验中差异分布的统计方法。
Genome Biol. 2016 Oct 25;17(1):222. doi: 10.1186/s13059-016-1077-y.
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Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics.通过单细胞转录组学对视网膜双极神经元进行综合分类
Cell. 2016 Aug 25;166(5):1308-1323.e30. doi: 10.1016/j.cell.2016.07.054.
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GiniClust: detecting rare cell types from single-cell gene expression data with Gini index.基尼聚类：利用基尼指数从单细胞基因表达数据中检测稀有细胞类型。
Genome Biol. 2016 Jul 1;17(1):144. doi: 10.1186/s13059-016-1010-4.
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Fast and accurate single-cell RNA-seq analysis by clustering of transcript-compatibility counts.通过转录本兼容性计数聚类实现快速准确的单细胞RNA测序分析
Genome Biol. 2016 May 26;17(1):112. doi: 10.1186/s13059-016-0970-8.
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Beta-Poisson model for single-cell RNA-seq data analyses.单细胞 RNA-seq 数据分析的 Beta-Poisson 模型。
Bioinformatics. 2016 Jul 15;32(14):2128-35. doi: 10.1093/bioinformatics/btw202. Epub 2016 Apr 19.
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Design and computational analysis of single-cell RNA-sequencing experiments.单细胞RNA测序实验的设计与计算分析
Genome Biol. 2016 Apr 7;17:63. doi: 10.1186/s13059-016-0927-y.
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