149 related articles for article (PubMed ID: 34196686)
1. CStreet: a computed Cell State trajectory inference method for time-series single-cell RNA sequencing data.
Zhao C; Xiu W; Hua Y; Zhang N; Zhang Y
Bioinformatics; 2021 Nov; 37(21):3774-3780. PubMed ID: 34196686
[TBL] [Abstract][Full Text] [Related]
2. Entropy-based inference of transition states and cellular trajectory for single-cell transcriptomics.
Gan Y; Guo C; Guo W; Xu G; Zou G
Brief Bioinform; 2022 Jul; 23(4):. PubMed ID: 35696651
[TBL] [Abstract][Full Text] [Related]
3. Tempora: Cell trajectory inference using time-series single-cell RNA sequencing data.
Tran TN; Bader GD
PLoS Comput Biol; 2020 Sep; 16(9):e1008205. PubMed ID: 32903255
[TBL] [Abstract][Full Text] [Related]
4. A machine learning-based method for automatically identifying novel cells in annotating single-cell RNA-seq data.
Li Z; Wang Y; Ganan-Gomez I; Colla S; Do KA
Bioinformatics; 2022 Oct; 38(21):4885-4892. PubMed ID: 36083008
[TBL] [Abstract][Full Text] [Related]
5. scShaper: an ensemble method for fast and accurate linear trajectory inference from single-cell RNA-seq data.
Smolander J; Junttila S; Venäläinen MS; Elo LL
Bioinformatics; 2022 Feb; 38(5):1328-1335. PubMed ID: 34888622
[TBL] [Abstract][Full Text] [Related]
6. Single-cell RNA sequencing data analysis based on non-uniform ε-neighborhood network.
Jia J; Chen L
Bioinformatics; 2022 Apr; 38(9):2459-2465. PubMed ID: 35188181
[TBL] [Abstract][Full Text] [Related]
7. scEpath: energy landscape-based inference of transition probabilities and cellular trajectories from single-cell transcriptomic data.
Jin S; MacLean AL; Peng T; Nie Q
Bioinformatics; 2018 Jun; 34(12):2077-2086. PubMed ID: 29415263
[TBL] [Abstract][Full Text] [Related]
8. scGAC: a graph attentional architecture for clustering single-cell RNA-seq data.
Cheng Y; Ma X
Bioinformatics; 2022 Apr; 38(8):2187-2193. PubMed ID: 35176138
[TBL] [Abstract][Full Text] [Related]
9. scWMC: weighted matrix completion-based imputation of scRNA-seq data via prior subspace information.
Su Y; Wang F; Zhang S; Liang Y; Wong KC; Li X
Bioinformatics; 2022 Sep; 38(19):4537-4545. PubMed ID: 35984287
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. scBGEDA: deep single-cell clustering analysis via a dual denoising autoencoder with bipartite graph ensemble clustering.
Wang Y; Yu Z; Li S; Bian C; Liang Y; Wong KC; Li X
Bioinformatics; 2023 Feb; 39(2):. PubMed ID: 36734596
[TBL] [Abstract][Full Text] [Related]
12. Identify, quantify and characterize cellular communication from single-cell RNA sequencing data with scSeqComm.
Baruzzo G; Cesaro G; Di Camillo B
Bioinformatics; 2022 Mar; 38(7):1920-1929. PubMed ID: 35043939
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. 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]
15. Vaeda computationally annotates doublets in single-cell RNA sequencing data.
Schriever H; Kostka D
Bioinformatics; 2023 Jan; 39(1):. PubMed ID: 36342203
[TBL] [Abstract][Full Text] [Related]
16. A robust and accurate single-cell data trajectory inference method using ensemble pseudotime.
Zhang Y; Tran D; Nguyen T; Dascalu SM; Harris FC
BMC Bioinformatics; 2023 Feb; 24(1):55. PubMed ID: 36803767
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. Model-based branching point detection in single-cell data by K-branches clustering.
Chlis NK; Wolf FA; Theis FJ
Bioinformatics; 2017 Oct; 33(20):3211-3219. PubMed ID: 28582478
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference.
Aubin-Frankowski PC; Vert JP
Bioinformatics; 2020 Sep; 36(18):4774-4780. PubMed ID: 33026066
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
