238 related articles for article (PubMed ID: 32840568)
1. Systematic determination of the mitochondrial proportion in human and mice tissues for single-cell RNA-sequencing data quality control.
Osorio D; Cai JJ
Bioinformatics; 2021 May; 37(7):963-967. PubMed ID: 32840568
[TBL] [Abstract][Full Text] [Related]
2. scQCEA: a framework for annotation and quality control report of single-cell RNA-sequencing data.
Nassiri I; Fairfax B; Lee A; Wu Y; Buck D; Piazza P
BMC Genomics; 2023 Jul; 24(1):381. PubMed ID: 37415108
[TBL] [Abstract][Full Text] [Related]
3. miQC: An adaptive probabilistic framework for quality control of single-cell RNA-sequencing data.
Hippen AA; Falco MM; Weber LM; Erkan EP; Zhang K; Doherty JA; Vähärautio A; Greene CS; Hicks SC
PLoS Comput Biol; 2021 Aug; 17(8):e1009290. PubMed ID: 34428202
[TBL] [Abstract][Full Text] [Related]
4. Cell-level somatic mutation detection from single-cell RNA sequencing.
Vu TN; Nguyen HN; Calza S; Kalari KR; Wang L; Pawitan Y
Bioinformatics; 2019 Nov; 35(22):4679-4687. PubMed ID: 31028395
[TBL] [Abstract][Full Text] [Related]
5. Joint learning dimension reduction and clustering of single-cell RNA-sequencing data.
Wu W; Ma X
Bioinformatics; 2020 Jun; 36(12):3825-3832. PubMed ID: 32246821
[TBL] [Abstract][Full Text] [Related]
6. scDoc: correcting drop-out events in single-cell RNA-seq data.
Ran D; Zhang S; Lytal N; An L
Bioinformatics; 2020 Aug; 36(15):4233-4239. PubMed ID: 32365169
[TBL] [Abstract][Full Text] [Related]
7. Random forest based similarity learning for single cell RNA sequencing data.
Pouyan MB; Kostka D
Bioinformatics; 2018 Jul; 34(13):i79-i88. PubMed ID: 29950006
[TBL] [Abstract][Full Text] [Related]
8. Quality control in scRNA-Seq can discriminate pacemaker cells: the mtRNA bias.
Galow AM; Kussauer S; Wolfien M; Brunner RM; Goldammer T; David R; Hoeflich A
Cell Mol Life Sci; 2021 Oct; 78(19-20):6585-6592. PubMed ID: 34427691
[TBL] [Abstract][Full Text] [Related]
9. FBA: feature barcoding analysis for single cell RNA-Seq.
Duan J; Hon GC
Bioinformatics; 2021 Nov; 37(22):4266-4268. PubMed ID: 33999185
[TBL] [Abstract][Full Text] [Related]
10. DECENT: differential expression with capture efficiency adjustmeNT for single-cell RNA-seq data.
Ye C; Speed TP; Salim A
Bioinformatics; 2019 Dec; 35(24):5155-5162. PubMed ID: 31197307
[TBL] [Abstract][Full Text] [Related]
11. Quality control of single-cell RNA-seq by SinQC.
Jiang P; Thomson JA; Stewart R
Bioinformatics; 2016 Aug; 32(16):2514-6. PubMed ID: 27153613
[TBL] [Abstract][Full Text] [Related]
12. ASAP: a web-based platform for the analysis and interactive visualization of single-cell RNA-seq data.
Gardeux V; David FPA; Shajkofci A; Schwalie PC; Deplancke B
Bioinformatics; 2017 Oct; 33(19):3123-3125. PubMed ID: 28541377
[TBL] [Abstract][Full Text] [Related]
13. scPNMF: sparse gene encoding of single cells to facilitate gene selection for targeted gene profiling.
Song D; Li K; Hemminger Z; Wollman R; Li JJ
Bioinformatics; 2021 Jul; 37(Suppl_1):i358-i366. PubMed ID: 34252925
[TBL] [Abstract][Full Text] [Related]
14. CONICS integrates scRNA-seq with DNA sequencing to map gene expression to tumor sub-clones.
Müller S; Cho A; Liu SJ; Lim DA; Diaz A
Bioinformatics; 2018 Sep; 34(18):3217-3219. PubMed ID: 29897414
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. ZIAQ: a quantile regression method for differential expression analysis of single-cell RNA-seq data.
Zhang W; Wei Y; Zhang D; Xu EY
Bioinformatics; 2020 May; 36(10):3124-3130. PubMed ID: 32053182
[TBL] [Abstract][Full Text] [Related]
17. A statistical simulator scDesign for rational scRNA-seq experimental design.
Li WV; Li JJ
Bioinformatics; 2019 Jul; 35(14):i41-i50. PubMed ID: 31510652
[TBL] [Abstract][Full Text] [Related]
18. scHinter: imputing dropout events for single-cell RNA-seq data with limited sample size.
Ye P; Ye W; Ye C; Li S; Ye L; Ji G; Wu X
Bioinformatics; 2020 Feb; 36(3):789-797. PubMed ID: 31392316
[TBL] [Abstract][Full Text] [Related]
19. ClusterMap: compare multiple single cell RNA-Seq datasets across different experimental conditions.
Gao X; Hu D; Gogol M; Li H
Bioinformatics; 2019 Sep; 35(17):3038-3045. PubMed ID: 30649203
[TBL] [Abstract][Full Text] [Related]
20. Scalable preprocessing for sparse scRNA-seq data exploiting prior knowledge.
Mukherjee S; Zhang Y; Fan J; Seelig G; Kannan S
Bioinformatics; 2018 Jul; 34(13):i124-i132. PubMed ID: 29949988
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
