306 related articles for article (PubMed ID: 31022373)
1. Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq.
Cole MB; Risso D; Wagner A; DeTomaso D; Ngai J; Purdom E; Dudoit S; Yosef N
Cell Syst; 2019 Apr; 8(4):315-328.e8. PubMed ID: 31022373
[TBL] [Abstract][Full Text] [Related]
2. PsiNorm: a scalable normalization for single-cell RNA-seq data.
Borella M; Martello G; Risso D; Romualdi C
Bioinformatics; 2021 Dec; 38(1):164-172. PubMed ID: 34499096
[TBL] [Abstract][Full Text] [Related]
3. Normalization of Single-Cell RNA-Seq Data.
Risso D
Methods Mol Biol; 2021; 2284():303-329. PubMed ID: 33835450
[TBL] [Abstract][Full Text] [Related]
4. Data normalization for addressing the challenges in the analysis of single-cell transcriptomic datasets.
Cuevas-Diaz Duran R; Wei H; Wu J
BMC Genomics; 2024 May; 25(1):444. PubMed ID: 38711017
[TBL] [Abstract][Full Text] [Related]
5. RNASeqR: An R Package for Automated Two-Group RNA-Seq Analysis Workflow.
Chao KH; Hsiao YW; Lee YF; Lee CY; Lai LC; Tsai MH; Lu TP; Chuang EY
IEEE/ACM Trans Comput Biol Bioinform; 2021; 18(5):2023-2031. PubMed ID: 31796413
[TBL] [Abstract][Full Text] [Related]
6. DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics.
Ghannoum S; Leoncio Netto W; Fantini D; Ragan-Kelley B; Parizadeh A; Jonasson E; Ståhlberg A; Farhan H; Köhn-Luque A
Int J Mol Sci; 2021 Jan; 22(3):. PubMed ID: 33573289
[TBL] [Abstract][Full Text] [Related]
7. scNPF: an integrative framework assisted by network propagation and network fusion for preprocessing of single-cell RNA-seq data.
Ye W; Ji G; Ye P; Long Y; Xiao X; Li S; Su Y; Wu X
BMC Genomics; 2019 May; 20(1):347. PubMed ID: 31068142
[TBL] [Abstract][Full Text] [Related]
8. scruff: an R/Bioconductor package for preprocessing single-cell RNA-sequencing data.
Wang Z; Hu J; Johnson WE; Campbell JD
BMC Bioinformatics; 2019 May; 20(1):222. PubMed ID: 31046658
[TBL] [Abstract][Full Text] [Related]
9. scPipe: A flexible R/Bioconductor preprocessing pipeline for single-cell RNA-sequencing data.
Tian L; Su S; Dong X; Amann-Zalcenstein D; Biben C; Seidi A; Hilton DJ; Naik SH; Ritchie ME
PLoS Comput Biol; 2018 Aug; 14(8):e1006361. PubMed ID: 30096152
[TBL] [Abstract][Full Text] [Related]
10. bayNorm: Bayesian gene expression recovery, imputation and normalization for single-cell RNA-sequencing data.
Tang W; Bertaux F; Thomas P; Stefanelli C; Saint M; Marguerat S; Shahrezaei V
Bioinformatics; 2020 Feb; 36(4):1174-1181. PubMed ID: 31584606
[TBL] [Abstract][Full Text] [Related]
11. ascend: R package for analysis of single-cell RNA-seq data.
Senabouth A; Lukowski SW; Hernandez JA; Andersen SB; Mei X; Nguyen QH; Powell JE
Gigascience; 2019 Aug; 8(8):. PubMed ID: 31505654
[TBL] [Abstract][Full Text] [Related]
12. popsicleR: A R Package for Pre-processing and Quality Control Analysis of Single Cell RNA-seq Data.
Grandi F; Caroli J; Romano O; Marchionni M; Forcato M; Bicciato S
J Mol Biol; 2022 Jun; 434(11):167560. PubMed ID: 35662457
[TBL] [Abstract][Full Text] [Related]
13. How to design a single-cell RNA-sequencing experiment: pitfalls, challenges and perspectives.
Dal Molin A; Di Camillo B
Brief Bioinform; 2019 Jul; 20(4):1384-1394. PubMed ID: 29394315
[TBL] [Abstract][Full Text] [Related]
14. Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R.
McCarthy DJ; Campbell KR; Lun AT; Wills QF
Bioinformatics; 2017 Apr; 33(8):1179-1186. PubMed ID: 28088763
[TBL] [Abstract][Full Text] [Related]
15. Clustering methods for single-cell RNA-sequencing expression data: performance evaluation with varying sample sizes and cell compositions.
Suner A
Stat Appl Genet Mol Biol; 2019 Aug; 18(5):. PubMed ID: 31646845
[TBL] [Abstract][Full Text] [Related]
16. NDRindex: a method for the quality assessment of single-cell RNA-Seq preprocessing data.
Xiao R; Lu G; Guo W; Jin S
BMC Bioinformatics; 2020 Dec; 21(Suppl 16):540. PubMed ID: 33323107
[TBL] [Abstract][Full Text] [Related]
17. Sincell: an R/Bioconductor package for statistical assessment of cell-state hierarchies from single-cell RNA-seq.
Juliá M; Telenti A; Rausell A
Bioinformatics; 2015 Oct; 31(20):3380-2. PubMed ID: 26099264
[TBL] [Abstract][Full Text] [Related]
18. Impact of similarity metrics on single-cell RNA-seq data clustering.
Kim T; Chen IR; Lin Y; Wang AY; Yang JYH; Yang P
Brief Bioinform; 2019 Nov; 20(6):2316-2326. PubMed ID: 30137247
[TBL] [Abstract][Full Text] [Related]
19. Dimensionality Reduction of Single-Cell RNA-Seq Data.
Linderman GC
Methods Mol Biol; 2021; 2284():331-342. PubMed ID: 33835451
[TBL] [Abstract][Full Text] [Related]
20. scONE-seq: A single-cell multi-omics method enables simultaneous dissection of phenotype and genotype heterogeneity from frozen tumors.
Yu L; Wang X; Mu Q; Tam SST; Loi DSC; Chan AKY; Poon WS; Ng HK; Chan DTM; Wang J; Wu AR
Sci Adv; 2023 Jan; 9(1):eabp8901. PubMed ID: 36598983
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
