241 related articles for article (PubMed ID: 33097004)
21. 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]
22. Accurate feature selection improves single-cell RNA-seq cell clustering.
Su K; Yu T; Wu H
Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33611426
[TBL] [Abstract][Full Text] [Related]
23. Beyond benchmarking and towards predictive models of dataset-specific single-cell RNA-seq pipeline performance.
Fang C; Selega A; Campbell KR
Genome Biol; 2024 Jun; 25(1):159. PubMed ID: 38886757
[TBL] [Abstract][Full Text] [Related]
24. Deep enhanced constraint clustering based on contrastive learning for scRNA-seq data.
Gan Y; Chen Y; Xu G; Guo W; Zou G
Brief Bioinform; 2023 Jul; 24(4):. PubMed ID: 37313714
[TBL] [Abstract][Full Text] [Related]
25. 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]
26. 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]
27. 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]
28. Minnow: a principled framework for rapid simulation of dscRNA-seq data at the read level.
Sarkar H; Srivastava A; Patro R
Bioinformatics; 2019 Jul; 35(14):i136-i144. PubMed ID: 31510649
[TBL] [Abstract][Full Text] [Related]
29. On the use of QDE-SVM for gene feature selection and cell type classification from scRNA-seq data.
Ng GYL; Tan SC; Ong CS
PLoS One; 2023; 18(10):e0292961. PubMed ID: 37856458
[TBL] [Abstract][Full Text] [Related]
30. IBRAP: integrated benchmarking single-cell RNA-sequencing analytical pipeline.
Knight CH; Khan F; Patel A; Gill US; Okosun J; Wang J
Brief Bioinform; 2023 Mar; 24(2):. PubMed ID: 36847692
[TBL] [Abstract][Full Text] [Related]
31. CTISL: a dynamic stacking multi-class classification approach for identifying cell types from single-cell RNA-seq data.
Wang X; Chai Z; Li S; Liu Y; Li C; Jiang Y; Liu Q
Bioinformatics; 2024 Feb; 40(2):. PubMed ID: 38317054
[TBL] [Abstract][Full Text] [Related]
32. Data Analysis in Single-Cell Transcriptome Sequencing.
Gao S
Methods Mol Biol; 2018; 1754():311-326. PubMed ID: 29536451
[TBL] [Abstract][Full Text] [Related]
33. bigSCale: an analytical framework for big-scale single-cell data.
Iacono G; Mereu E; Guillaumet-Adkins A; Corominas R; Cuscó I; Rodríguez-Esteban G; Gut M; Pérez-Jurado LA; Gut I; Heyn H
Genome Res; 2018 Jun; 28(6):878-890. PubMed ID: 29724792
[TBL] [Abstract][Full Text] [Related]
34. 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]
35. SoCube: an innovative end-to-end doublet detection algorithm for analyzing scRNA-seq data.
Zhang H; Lu M; Lin G; Zheng L; Zhang W; Xu Z; Zhu F
Brief Bioinform; 2023 May; 24(3):. PubMed ID: 36941114
[TBL] [Abstract][Full Text] [Related]
36. Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data.
Mieth B; Hockley JRF; Görnitz N; Vidovic MM; Müller KR; Gutteridge A; Ziemek D
Sci Rep; 2019 Dec; 9(1):20353. PubMed ID: 31889137
[TBL] [Abstract][Full Text] [Related]
37. Single-cell RNA-seq clustering: datasets, models, and algorithms.
Peng L; Tian X; Tian G; Xu J; Huang X; Weng Y; Yang J; Zhou L
RNA Biol; 2020 Jun; 17(6):765-783. PubMed ID: 32116127
[TBL] [Abstract][Full Text] [Related]
38. Identifying Cell Subpopulations and Their Genetic Drivers from Single-Cell RNA-Seq Data Using a Biclustering Approach.
Shi F; Huang H
J Comput Biol; 2017 Jul; 24(7):663-674. PubMed ID: 28657835
[TBL] [Abstract][Full Text] [Related]
39. 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]
40. 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]
[Previous] [Next] [New Search]
