456 related articles for article (PubMed ID: 31992223)
1. Choice of library size normalization and statistical methods for differential gene expression analysis in balanced two-group comparisons for RNA-seq studies.
Li X; Cooper NGF; O'Toole TE; Rouchka EC
BMC Genomics; 2020 Jan; 21(1):75. PubMed ID: 31992223
[TBL] [Abstract][Full Text] [Related]
2. A comparison of per sample global scaling and per gene normalization methods for differential expression analysis of RNA-seq data.
Li X; Brock GN; Rouchka EC; Cooper NGF; Wu D; O'Toole TE; Gill RS; Eteleeb AM; O'Brien L; Rai SN
PLoS One; 2017; 12(5):e0176185. PubMed ID: 28459823
[TBL] [Abstract][Full Text] [Related]
3. Benchmarking differential expression analysis tools for RNA-Seq: normalization-based vs. log-ratio transformation-based methods.
Quinn TP; Crowley TM; Richardson MF
BMC Bioinformatics; 2018 Jul; 19(1):274. PubMed ID: 30021534
[TBL] [Abstract][Full Text] [Related]
4. Comparing the normalization methods for the differential analysis of Illumina high-throughput RNA-Seq data.
Li P; Piao Y; Shon HS; Ryu KH
BMC Bioinformatics; 2015 Oct; 16():347. PubMed ID: 26511205
[TBL] [Abstract][Full Text] [Related]
5. Normalization of RNA-Seq data using adaptive trimmed mean with multi-reference.
Singh V; Kirtipal N; Song B; Lee S
Brief Bioinform; 2024 Mar; 25(3):. PubMed ID: 38770720
[TBL] [Abstract][Full Text] [Related]
6. Gene length corrected trimmed mean of M-values (GeTMM) processing of RNA-seq data performs similarly in intersample analyses while improving intrasample comparisons.
Smid M; Coebergh van den Braak RRJ; van de Werken HJG; van Riet J; van Galen A; de Weerd V; van der Vlugt-Daane M; Bril SI; Lalmahomed ZS; Kloosterman WP; Wilting SM; Foekens JA; IJzermans JNM; ; Martens JWM; Sieuwerts AM
BMC Bioinformatics; 2018 Jun; 19(1):236. PubMed ID: 29929481
[TBL] [Abstract][Full Text] [Related]
7. In Papyro Comparison of TMM (edgeR), RLE (DESeq2), and MRN Normalization Methods for a Simple Two-Conditions-Without-Replicates RNA-Seq Experimental Design.
Maza E
Front Genet; 2016; 7():164. PubMed ID: 27695478
[TBL] [Abstract][Full Text] [Related]
8. Comparison of normalization approaches for gene expression studies completed with high-throughput sequencing.
Abbas-Aghababazadeh F; Li Q; Fridley BL
PLoS One; 2018; 13(10):e0206312. PubMed ID: 30379879
[TBL] [Abstract][Full Text] [Related]
9. Power analysis and sample size estimation for RNA-Seq differential expression.
Ching T; Huang S; Garmire LX
RNA; 2014 Nov; 20(11):1684-96. PubMed ID: 25246651
[TBL] [Abstract][Full Text] [Related]
10. A combined approach with gene-wise normalization improves the analysis of RNA-seq data in human breast cancer subtypes.
Li X; Rouchka EC; Brock GN; Yan J; O'Toole TE; Tieri DA; Cooper NGF
PLoS One; 2018; 13(8):e0201813. PubMed ID: 30089167
[TBL] [Abstract][Full Text] [Related]
11. Statistical detection of differentially expressed genes based on RNA-seq: from biological to phylogenetic replicates.
Gu X
Brief Bioinform; 2016 Mar; 17(2):243-8. PubMed ID: 26108230
[TBL] [Abstract][Full Text] [Related]
12. An evaluation of RNA-seq differential analysis methods.
Li D; Zand MS; Dye TD; Goniewicz ML; Rahman I; Xie Z
PLoS One; 2022; 17(9):e0264246. PubMed ID: 36112652
[TBL] [Abstract][Full Text] [Related]
13. A workflow for simplified analysis of ATAC-cap-seq data in R.
Shrestha RK; Ding P; Jones JDG; MacLean D
Gigascience; 2018 Jul; 7(7):. PubMed ID: 29961827
[TBL] [Abstract][Full Text] [Related]
14. Sample size calculations for the differential expression analysis of RNA-seq data using a negative binomial regression model.
Li X; Wu D; Cooper NGF; Rai SN
Stat Appl Genet Mol Biol; 2019 Jan; 18(1):. PubMed ID: 30667368
[TBL] [Abstract][Full Text] [Related]
15. Comparison of normalization and differential expression analyses using RNA-Seq data from 726 individual Drosophila melanogaster.
Lin Y; Golovnina K; Chen ZX; Lee HN; Negron YL; Sultana H; Oliver B; Harbison ST
BMC Genomics; 2016 Jan; 17():28. PubMed ID: 26732976
[TBL] [Abstract][Full Text] [Related]
16. XBSeq2: a fast and accurate quantification of differential expression and differential polyadenylation.
Liu Y; Wu P; Zhou J; Johnson-Pais TL; Lai Z; Chowdhury WH; Rodriguez R; Chen Y
BMC Bioinformatics; 2017 Oct; 18(Suppl 11):384. PubMed ID: 28984183
[TBL] [Abstract][Full Text] [Related]
17. Normalization of Single-Cell RNA-Seq Data.
Risso D
Methods Mol Biol; 2021; 2284():303-329. PubMed ID: 33835450
[TBL] [Abstract][Full Text] [Related]
18. Comparison of Normalization Methods for Analysis of TempO-Seq Targeted RNA Sequencing Data.
Bushel PR; Ferguson SS; Ramaiahgari SC; Paules RS; Auerbach SS
Front Genet; 2020; 11():594. PubMed ID: 32655620
[TBL] [Abstract][Full Text] [Related]
19. aFold - using polynomial uncertainty modelling for differential gene expression estimation from RNA sequencing data.
Yang W; Rosenstiel P; Schulenburg H
BMC Genomics; 2019 May; 20(1):364. PubMed ID: 31077153
[TBL] [Abstract][Full Text] [Related]
20. SARTools: A DESeq2- and EdgeR-Based R Pipeline for Comprehensive Differential Analysis of RNA-Seq Data.
Varet H; Brillet-Guéguen L; Coppée JY; Dillies MA
PLoS One; 2016; 11(6):e0157022. PubMed ID: 27280887
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
