907 related articles for article (PubMed ID: 31262249)
1. Polled Digital Cell Sorter (p-DCS): Automatic identification of hematological cell types from single cell RNA-sequencing clusters.
Domanskyi S; Szedlak A; Hawkins NT; Wang J; Paternostro G; Piermarocchi C
BMC Bioinformatics; 2019 Jul; 20(1):369. PubMed ID: 31262249
[TBL] [Abstract][Full Text] [Related]
2. A multitask clustering approach for single-cell RNA-seq analysis in Recessive Dystrophic Epidermolysis Bullosa.
Zhang H; Lee CAA; Li Z; Garbe JR; Eide CR; Petegrosso R; Kuang R; Tolar J
PLoS Comput Biol; 2018 Apr; 14(4):e1006053. PubMed ID: 29630593
[TBL] [Abstract][Full Text] [Related]
3. Visualization of Single Cell RNA-Seq Data Using t-SNE in R.
Zhou B; Jin W
Methods Mol Biol; 2020; 2117():159-167. PubMed ID: 31960377
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. A component overlapping attribute clustering (COAC) algorithm for single-cell RNA sequencing data analysis and potential pathobiological implications.
Peng H; Zeng X; Zhou Y; Zhang D; Nussinov R; Cheng F
PLoS Comput Biol; 2019 Feb; 15(2):e1006772. PubMed ID: 30779739
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. Autoencoder-based cluster ensembles for single-cell RNA-seq data analysis.
Geddes TA; Kim T; Nan L; Burchfield JG; Yang JYH; Tao D; Yang P
BMC Bioinformatics; 2019 Dec; 20(Suppl 19):660. PubMed ID: 31870278
[TBL] [Abstract][Full Text] [Related]
8. Digital Cell Sorter (DCS): a cell type identification, anomaly detection, and Hopfield landscapes toolkit for single-cell transcriptomics.
Domanskyi S; Hakansson A; Bertus TJ; Paternostro G; Piermarocchi C
PeerJ; 2021; 9():e10670. PubMed ID: 33520459
[TBL] [Abstract][Full Text] [Related]
9. GE-Impute: graph embedding-based imputation for single-cell RNA-seq data.
Wu X; Zhou Y
Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 35901457
[TBL] [Abstract][Full Text] [Related]
10. scHFC: a hybrid fuzzy clustering method for single-cell RNA-seq data optimized by natural computation.
Wang J; Xia J; Tan D; Lin R; Su Y; Zheng CH
Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35136924
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data.
Lin P; Troup M; Ho JW
Genome Biol; 2017 Mar; 18(1):59. PubMed ID: 28351406
[TBL] [Abstract][Full Text] [Related]
13. Learning deep features and topological structure of cells for clustering of scRNA-sequencing data.
Wang H; Ma X
Brief Bioinform; 2022 May; 23(3):. PubMed ID: 35302164
[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. A rank-based marker selection method for high throughput scRNA-seq data.
Vargo AHS; Gilbert AC
BMC Bioinformatics; 2020 Oct; 21(1):477. PubMed ID: 33097004
[TBL] [Abstract][Full Text] [Related]
16. DTWscore: differential expression and cell clustering analysis for time-series single-cell RNA-seq data.
Wang Z; Jin S; Liu G; Zhang X; Wang N; Wu D; Hu Y; Zhang C; Jiang Q; Xu L; Wang Y
BMC Bioinformatics; 2017 May; 18(1):270. PubMed ID: 28535748
[TBL] [Abstract][Full Text] [Related]
17. CBLRR: a cauchy-based bounded constraint low-rank representation method to cluster single-cell RNA-seq data.
Ding Q; Yang W; Luo M; Xu C; Xu Z; Pang F; Cai Y; Anashkina AA; Su X; Chen N; Jiang Q
Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 35870203
[TBL] [Abstract][Full Text] [Related]
18. Single cell RNA-seq data clustering using TF-IDF based methods.
Moussa M; Măndoiu II
BMC Genomics; 2018 Aug; 19(Suppl 6):569. PubMed ID: 30367575
[TBL] [Abstract][Full Text] [Related]
19. ACTINN: automated identification of cell types in single cell RNA sequencing.
Ma F; Pellegrini M
Bioinformatics; 2020 Jan; 36(2):533-538. PubMed ID: 31359028
[TBL] [Abstract][Full Text] [Related]
20. Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data.
Linderman GC; Rachh M; Hoskins JG; Steinerberger S; Kluger Y
Nat Methods; 2019 Mar; 16(3):243-245. PubMed ID: 30742040
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
