190 related articles for article (PubMed ID: 36250795)
1. AIscEA: unsupervised integration of single-cell gene expression and chromatin accessibility via their biological consistency.
Jafari E; Johnson T; Wang Y; Liu Y; Huang K; Wang Y
Bioinformatics; 2022 Nov; 38(23):5236-5244. PubMed ID: 36250795
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. scAMACE: model-based approach to the joint analysis of single-cell data on chromatin accessibility, gene expression and methylation.
Wangwu J; Sun Z; Lin Z
Bioinformatics; 2021 Nov; 37(21):3874-3880. PubMed ID: 34086847
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. 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]
6. 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]
7. ShinyArchR.UiO: user-friendly,integrative and open-source tool for visualization of single-cell ATAC-seq data using ArchR.
Sharma A; Akshay A; Rogne M; Eskeland R
Bioinformatics; 2022 Jan; 38(3):834-836. PubMed ID: 34586377
[TBL] [Abstract][Full Text] [Related]
8. sciCAN: single-cell chromatin accessibility and gene expression data integration via cycle-consistent adversarial network.
Xu Y; Begoli E; McCord RP
NPJ Syst Biol Appl; 2022 Sep; 8(1):33. PubMed ID: 36089620
[TBL] [Abstract][Full Text] [Related]
9. Chromatin accessibility prediction via convolutional long short-term memory networks with k-mer embedding.
Min X; Zeng W; Chen N; Chen T; Jiang R
Bioinformatics; 2017 Jul; 33(14):i92-i101. PubMed ID: 28881969
[TBL] [Abstract][Full Text] [Related]
10. Profiling Chromatin Accessibility at Single-cell Resolution.
Sinha S; Satpathy AT; Zhou W; Ji H; Stratton JA; Jaffer A; Bahlis N; Morrissy S; Biernaskie JA
Genomics Proteomics Bioinformatics; 2021 Apr; 19(2):172-190. PubMed ID: 33581341
[TBL] [Abstract][Full Text] [Related]
11. Unsupervised topological alignment for single-cell multi-omics integration.
Cao K; Bai X; Hong Y; Wan L
Bioinformatics; 2020 Jul; 36(Suppl_1):i48-i56. PubMed ID: 32657382
[TBL] [Abstract][Full Text] [Related]
12. ALTRE: workflow for defining ALTered Regulatory Elements using chromatin accessibility data.
Baskin E; Farouni R; Mathé EA
Bioinformatics; 2017 Mar; 33(5):740-742. PubMed ID: 28011773
[TBL] [Abstract][Full Text] [Related]
13. Integration of scATAC-Seq with scRNA-Seq Data.
Berest I; Tangherloni A
Methods Mol Biol; 2023; 2584():293-310. PubMed ID: 36495457
[TBL] [Abstract][Full Text] [Related]
14. ASURAT: functional annotation-driven unsupervised clustering of single-cell transcriptomes.
Iida K; Kondo J; Wibisana JN; Inoue M; Okada M
Bioinformatics; 2022 Sep; 38(18):4330-4336. PubMed ID: 35924984
[TBL] [Abstract][Full Text] [Related]
15. Optimal transport improves cell-cell similarity inference in single-cell omics data.
Huizing GJ; Peyré G; Cantini L
Bioinformatics; 2022 Apr; 38(8):2169-2177. PubMed ID: 35157031
[TBL] [Abstract][Full Text] [Related]
16. scNCL: transferring labels from scRNA-seq to scATAC-seq data with neighborhood contrastive regularization.
Yan X; Zheng R; Chen J; Li M
Bioinformatics; 2023 Aug; 39(8):. PubMed ID: 37584660
[TBL] [Abstract][Full Text] [Related]
17. Single-cell RNA-seq data semi-supervised clustering and annotation via structural regularized domain adaptation.
Chen L; He Q; Zhai Y; Deng M
Bioinformatics; 2021 May; 37(6):775-784. PubMed ID: 33098418
[TBL] [Abstract][Full Text] [Related]
18. SD2: spatially resolved transcriptomics deconvolution through integration of dropout and spatial information.
Li H; Li H; Zhou J; Gao X
Bioinformatics; 2022 Oct; 38(21):4878-4884. PubMed ID: 36063455
[TBL] [Abstract][Full Text] [Related]
19. On the problem of confounders in modeling gene expression.
Schmidt F; Schulz MH
Bioinformatics; 2019 Feb; 35(4):711-719. PubMed ID: 30084962
[TBL] [Abstract][Full Text] [Related]
20. Ensemble deep learning of embeddings for clustering multimodal single-cell omics data.
Yu L; Liu C; Yang JYH; Yang P
Bioinformatics; 2023 Jun; 39(6):. PubMed ID: 37314966
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]