333 related articles for article (PubMed ID: 26743507)
1. OEFinder: a user interface to identify and visualize ordering effects in single-cell RNA-seq data.
Leng N; Choi J; Chu LF; Thomson JA; Kendziorski C; Stewart R
Bioinformatics; 2016 May; 32(9):1408-10. PubMed ID: 26743507
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. visnormsc: A Graphical User Interface to Normalize Single-cell RNA Sequencing Data.
Tang L; Zhou N
Interdiscip Sci; 2018 Sep; 10(3):636-640. PubMed ID: 29280088
[TBL] [Abstract][Full Text] [Related]
4. SCell: integrated analysis of single-cell RNA-seq data.
Diaz A; Liu SJ; Sandoval C; Pollen A; Nowakowski TJ; Lim DA; Kriegstein A
Bioinformatics; 2016 Jul; 32(14):2219-20. PubMed ID: 27153637
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. Falco: a quick and flexible single-cell RNA-seq processing framework on the cloud.
Yang A; Troup M; Lin P; Ho JW
Bioinformatics; 2017 Mar; 33(5):767-769. PubMed ID: 28025200
[TBL] [Abstract][Full Text] [Related]
7. 2DImpute: imputation in single-cell RNA-seq data from correlations in two dimensions.
Zhu K; Anastassiou D
Bioinformatics; 2020 Jun; 36(11):3588-3589. PubMed ID: 32108864
[TBL] [Abstract][Full Text] [Related]
8. DEsingle for detecting three types of differential expression in single-cell RNA-seq data.
Miao Z; Deng K; Wang X; Zhang X
Bioinformatics; 2018 Sep; 34(18):3223-3224. PubMed ID: 29688277
[TBL] [Abstract][Full Text] [Related]
9. Cerebro: interactive visualization of scRNA-seq data.
Hillje R; Pelicci PG; Luzi L
Bioinformatics; 2020 Apr; 36(7):2311-2313. PubMed ID: 31764967
[TBL] [Abstract][Full Text] [Related]
10. SPARSim single cell: a count data simulator for scRNA-seq data.
Baruzzo G; Patuzzi I; Di Camillo B
Bioinformatics; 2020 Mar; 36(5):1468-1475. PubMed ID: 31598633
[TBL] [Abstract][Full Text] [Related]
11. RNASeqGUI: a GUI for analysing RNA-Seq data.
Russo F; Angelini C
Bioinformatics; 2014 Sep; 30(17):2514-6. PubMed ID: 24812338
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. A Single-Cell Sequencing Guide for Immunologists.
See P; Lum J; Chen J; Ginhoux F
Front Immunol; 2018; 9():2425. PubMed ID: 30405621
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. DIMM-SC: a Dirichlet mixture model for clustering droplet-based single cell transcriptomic data.
Sun Z; Wang T; Deng K; Wang XF; Lafyatis R; Ding Y; Hu M; Chen W
Bioinformatics; 2018 Jan; 34(1):139-146. PubMed ID: 29036318
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. V-SVA: an R Shiny application for detecting and annotating hidden sources of variation in single-cell RNA-seq data.
Lawlor N; Marquez EJ; Lee D; Ucar D
Bioinformatics; 2020 Jun; 36(11):3582-3584. PubMed ID: 32119082
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. iS-CellR: a user-friendly tool for analyzing and visualizing single-cell RNA sequencing data.
Patel MV
Bioinformatics; 2018 Dec; 34(24):4305-4306. PubMed ID: 29982379
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
