92. Evans C, Hardin J, Stoebel DM. Selecting between-sample RNA-Seq normalization methods
from the perspective of their assumptions. Brief Bioinform. 2018;19(5):776–92.
93. Li P, Piao Y, Shon HS, Ryu KH. Comparing the normalization methods for the differential
analysis of Illumina high-throughput RNA-Seq data. BMC Bioinform. 2015;16:347.
94. Costa-Silva J, Domingues D, Lopes FM. RNA-Seq differential expression analysis: an extended
review and a software tool. PLoS One. 2017;12(12):e0190152.
95. Soneson C, Delorenzi M. A comparison of methods for differential expression analysis of RNAseq data. BMC Bioinform. 2013;14:91.
96. Zhou Y, Zhu J, Tong T, Wang J, Lin B, Zhang J. A statistical normalization method and
differential expression analysis for RNA-seq data between different species. BMC Bioinform.
2019;20(1):163.
97. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq
data with DESeq2. Genome Biol. 2014;15(12):550.
98. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential
expression analysis of digital gene expression data. Bioinformatics. 2010;26(1):139–40.
99. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential
expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43
(7):e47.
100. Huang Y, Pan J, Chen D, Zheng J, Qiu F, Li F, et al. Identification and functional analysis of
differentially expressed genes in poorly differentiated hepatocellular carcinoma using RNA-seq.
Oncotarget. 2017;8(22):35973–83.
101. Martin D, Brun C, Remy E, Mouren P, Thieffry D, Jacq B. GOToolBox: functional analysis of
gene datasets based on Gene ontology. Genome Biol. 2004;5(12):R101.
102. Boyle EI, Weng S, Gollub J, Jin H, Botstein D, Cherry JM, et al. GO::TermFinder–open source
software for accessing gene ontology information and finding significantly enriched Gene
Ontology terms associated with a list of genes. Bioinformatics. 2004;20(18):3710–5.
103. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set
enrichment analysis: a knowledge-based approach for interpreting genome-wide expression
profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545–50.
104. Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes
among gene clusters. OMICS. 2012;16(5):284–7.
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175
from the perspective of their assumptions. Brief Bioinform. 2018;19(5):776–92.
93. Li P, Piao Y, Shon HS, Ryu KH. Comparing the normalization methods for the differential
analysis of Illumina high-throughput RNA-Seq data. BMC Bioinform. 2015;16:347.
94. Costa-Silva J, Domingues D, Lopes FM. RNA-Seq differential expression analysis: an extended
review and a software tool. PLoS One. 2017;12(12):e0190152.
95. Soneson C, Delorenzi M. A comparison of methods for differential expression analysis of RNAseq data. BMC Bioinform. 2013;14:91.
96. Zhou Y, Zhu J, Tong T, Wang J, Lin B, Zhang J. A statistical normalization method and
differential expression analysis for RNA-seq data between different species. BMC Bioinform.
2019;20(1):163.
97. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq
data with DESeq2. Genome Biol. 2014;15(12):550.
98. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential
expression analysis of digital gene expression data. Bioinformatics. 2010;26(1):139–40.
99. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential
expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43
(7):e47.
100. Huang Y, Pan J, Chen D, Zheng J, Qiu F, Li F, et al. Identification and functional analysis of
differentially expressed genes in poorly differentiated hepatocellular carcinoma using RNA-seq.
Oncotarget. 2017;8(22):35973–83.
101. Martin D, Brun C, Remy E, Mouren P, Thieffry D, Jacq B. GOToolBox: functional analysis of
gene datasets based on Gene ontology. Genome Biol. 2004;5(12):R101.
102. Boyle EI, Weng S, Gollub J, Jin H, Botstein D, Cherry JM, et al. GO::TermFinder–open source
software for accessing gene ontology information and finding significantly enriched Gene
Ontology terms associated with a list of genes. Bioinformatics. 2004;20(18):3710–5.
103. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set
enrichment analysis: a knowledge-based approach for interpreting genome-wide expression
profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545–50.
104. Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes
among gene clusters. OMICS. 2012;16(5):284–7.
11 Design and Analysis of RNA Sequencing Data
175