175 related articles for article (PubMed ID: 35941103)
1. Scarf enables a highly memory-efficient analysis of large-scale single-cell genomics data.
Dhapola P; Rodhe J; Olofzon R; Bonald T; Erlandsson E; Soneji S; Karlsson G
Nat Commun; 2022 Aug; 13(1):4616. PubMed ID: 35941103
[TBL] [Abstract][Full Text] [Related]
2. PARC: ultrafast and accurate clustering of phenotypic data of millions of single cells.
Stassen SV; Siu DMD; Lee KCM; Ho JWK; So HKH; Tsia KK
Bioinformatics; 2020 May; 36(9):2778-2786. PubMed ID: 31971583
[TBL] [Abstract][Full Text] [Related]
3. SSCC: A Novel Computational Framework for Rapid and Accurate Clustering Large-scale Single Cell RNA-seq Data.
Ren X; Zheng L; Zhang Z
Genomics Proteomics Bioinformatics; 2019 Apr; 17(2):201-210. PubMed ID: 31202000
[TBL] [Abstract][Full Text] [Related]
4. EpiCarousel: memory- and time-efficient identification of metacells for atlas-level single-cell chromatin accessibility data.
Li S; Li Y; Sun Y; Li Y; Chen X; Tang S; Chen S
Bioinformatics; 2024 Mar; 40(4):. PubMed ID: 38588573
[TBL] [Abstract][Full Text] [Related]
5. Single-cell RNA-seq interpretations using evolutionary multiobjective ensemble pruning.
Li X; Zhang S; Wong KC
Bioinformatics; 2019 Aug; 35(16):2809-2817. PubMed ID: 30596898
[TBL] [Abstract][Full Text] [Related]
6. UINMF performs mosaic integration of single-cell multi-omic datasets using nonnegative matrix factorization.
Kriebel AR; Welch JD
Nat Commun; 2022 Feb; 13(1):780. PubMed ID: 35140223
[TBL] [Abstract][Full Text] [Related]
7. Hopper: a mathematically optimal algorithm for sketching biological data.
DeMeo B; Berger B
Bioinformatics; 2020 Jul; 36(Suppl_1):i236-i241. PubMed ID: 32657375
[TBL] [Abstract][Full Text] [Related]
8. SuperCellCyto: enabling efficient analysis of large scale cytometry datasets.
Putri GH; Howitt G; Marsh-Wakefield F; Ashhurst TM; Phipson B
Genome Biol; 2024 Apr; 25(1):89. PubMed ID: 38589921
[TBL] [Abstract][Full Text] [Related]
9. Efficient and precise single-cell reference atlas mapping with Symphony.
Kang JB; Nathan A; Weinand K; Zhang F; Millard N; Rumker L; Moody DB; Korsunsky I; Raychaudhuri S
Nat Commun; 2021 Oct; 12(1):5890. PubMed ID: 34620862
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. JOINT for large-scale single-cell RNA-sequencing analysis via soft-clustering and parallel computing.
Cui T; Wang T
BMC Genomics; 2021 Jan; 22(1):47. PubMed ID: 33430769
[TBL] [Abstract][Full Text] [Related]
12. scGate: marker-based purification of cell types from heterogeneous single-cell RNA-seq datasets.
Andreatta M; Berenstein AJ; Carmona SJ
Bioinformatics; 2022 Apr; 38(9):2642-2644. PubMed ID: 35258562
[TBL] [Abstract][Full Text] [Related]
13. Scellpam: an R package/C++ library to perform parallel partitioning around medoids on scRNAseq data sets.
Domingo J; Leon T; Dura E
BMC Bioinformatics; 2023 Sep; 24(1):342. PubMed ID: 37710192
[TBL] [Abstract][Full Text] [Related]
14. SCHNEL: scalable clustering of high dimensional single-cell data.
Abdelaal T; de Raadt P; Lelieveldt BPF; Reinders MJT; Mahfouz A
Bioinformatics; 2020 Dec; 36(Suppl_2):i849-i856. PubMed ID: 33381821
[TBL] [Abstract][Full Text] [Related]
15. GMHCC: high-throughput analysis of biomolecular data using graph-based multiple hierarchical consensus clustering.
Lu Y; Yu Z; Wang Y; Ma Z; Wong KC; Li X
Bioinformatics; 2022 May; 38(11):3020-3028. PubMed ID: 35451457
[TBL] [Abstract][Full Text] [Related]
16. PyLiger: scalable single-cell multi-omic data integration in Python.
Lu L; Welch JD
Bioinformatics; 2022 May; 38(10):2946-2948. PubMed ID: 35561174
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. 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]
19. LSMMD-MA: scaling multimodal data integration for single-cell genomics data analysis.
Meng-Papaxanthos L; Zhang R; Li G; Cuturi M; Noble WS; Vert JP
Bioinformatics; 2023 Jul; 39(7):. PubMed ID: 37421399
[TBL] [Abstract][Full Text] [Related]
20. VPAC: Variational projection for accurate clustering of single-cell transcriptomic data.
Chen S; Hua K; Cui H; Jiang R
BMC Bioinformatics; 2019 May; 20(Suppl 7):0. PubMed ID: 31074382
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
