126 related articles for article (PubMed ID: 31483480)
1. Large scale maximum average power multiple inference on time-course count data with application to RNA-seq analysis.
Cao M; Zhou W; Breidt FJ; Peers G
Biometrics; 2020 Mar; 76(1):9-22. PubMed ID: 31483480
[TBL] [Abstract][Full Text] [Related]
2. An optimal test with maximum average power while controlling FDR with application to RNA-seq data.
Si Y; Liu P
Biometrics; 2013 Sep; 69(3):594-605. PubMed ID: 23889143
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. Power analysis for RNA-Seq differential expression studies using generalized linear mixed effects models.
Yu L; Fernandez S; Brock G
BMC Bioinformatics; 2020 May; 21(1):198. PubMed ID: 32429934
[TBL] [Abstract][Full Text] [Related]
5. Sample size calculation while controlling false discovery rate for differential expression analysis with RNA-sequencing experiments.
Bi R; Liu P
BMC Bioinformatics; 2016 Mar; 17():146. PubMed ID: 27029470
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. Robust identification of differentially expressed genes from RNA-seq data.
Shahjaman M; Manir Hossain Mollah M; Rezanur Rahman M; Islam SMS; Nurul Haque Mollah M
Genomics; 2020 Mar; 112(2):2000-2010. PubMed ID: 31756426
[TBL] [Abstract][Full Text] [Related]
8. A two-step integrated approach to detect differentially expressed genes in RNA-Seq data.
Al Mahi N; Begum M
J Bioinform Comput Biol; 2016 Dec; 14(6):1650034. PubMed ID: 27774870
[TBL] [Abstract][Full Text] [Related]
9. On the utility of RNA sample pooling to optimize cost and statistical power in RNA sequencing experiments.
Takele Assefa A; Vandesompele J; Thas O
BMC Genomics; 2020 Apr; 21(1):312. PubMed ID: 32306892
[TBL] [Abstract][Full Text] [Related]
10. Power analysis for RNA-Seq differential expression studies.
Yu L; Fernandez S; Brock G
BMC Bioinformatics; 2017 May; 18(1):234. PubMed ID: 28468606
[TBL] [Abstract][Full Text] [Related]
11. LFCseq: a nonparametric approach for differential expression analysis of RNA-seq data.
Lin B; Zhang LF; Chen X
BMC Genomics; 2014; 15 Suppl 10(Suppl 10):S7. PubMed ID: 25560842
[TBL] [Abstract][Full Text] [Related]
12. Subject level clustering using a negative binomial model for small transcriptomic studies.
Li Q; Noel-MacDonnell JR; Koestler DC; Goode EL; Fridley BL
BMC Bioinformatics; 2018 Dec; 19(1):474. PubMed ID: 30541426
[TBL] [Abstract][Full Text] [Related]
13. A sparse negative binomial mixture model for clustering RNA-seq count data.
Li Y; Rahman T; Ma T; Tang L; Tseng GC
Biostatistics; 2022 Dec; 24(1):68-84. PubMed ID: 34363675
[TBL] [Abstract][Full Text] [Related]
14. Modifying SAMseq to account for asymmetry in the distribution of effect sizes when identifying differentially expressed genes.
Kotoka E; Orr M
Stat Appl Genet Mol Biol; 2017 Nov; 16(5-6):291-312. PubMed ID: 29077555
[TBL] [Abstract][Full Text] [Related]
15. Methods for time series analysis of RNA-seq data with application to human Th17 cell differentiation.
Äijö T; Butty V; Chen Z; Salo V; Tripathi S; Burge CB; Lahesmaa R; Lähdesmäki H
Bioinformatics; 2014 Jun; 30(12):i113-20. PubMed ID: 24931974
[TBL] [Abstract][Full Text] [Related]
16. QNB: differential RNA methylation analysis for count-based small-sample sequencing data with a quad-negative binomial model.
Liu L; Zhang SW; Huang Y; Meng J
BMC Bioinformatics; 2017 Aug; 18(1):387. PubMed ID: 28859631
[TBL] [Abstract][Full Text] [Related]
17. Variance component testing for identifying differentially expressed genes in RNA-seq data.
Yang S; Shao F; Duan W; Zhao Y; Chen F
PeerJ; 2017; 5():e3797. PubMed ID: 28929020
[TBL] [Abstract][Full Text] [Related]
18. Statistical inference for time course RNA-Seq data using a negative binomial mixed-effect model.
Sun X; Dalpiaz D; Wu D; S Liu J; Zhong W; Ma P
BMC Bioinformatics; 2016 Aug; 17(1):324. PubMed ID: 27565575
[TBL] [Abstract][Full Text] [Related]
19. Differential expression analysis of RNA sequencing data by incorporating non-exonic mapped reads.
Chen HI; Liu Y; Zou Y; Lai Z; Sarkar D; Huang Y; Chen Y
BMC Genomics; 2015; 16 Suppl 7(Suppl 7):S14. PubMed ID: 26099631
[TBL] [Abstract][Full Text] [Related]
20. Joint between-sample normalization and differential expression detection through ℓ
Liu K; Shen L; Jiang H
BMC Bioinformatics; 2019 Dec; 20(Suppl 16):593. PubMed ID: 31787074
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
