137 related articles for article (PubMed ID: 35652265)
1. InterCellDB: A User-Defined Database for Inferring Intercellular Networks.
Jin Z; Zhang X; Dai X; Huang J; Hu X; Zhang J; Shi L
Adv Sci (Weinh); 2022 Aug; 9(22):e2200045. PubMed ID: 35652265
[TBL] [Abstract][Full Text] [Related]
2. Meta-Analysis of Human Cancer Single-Cell RNA-Seq Datasets Using the IMMUcan Database.
Camps J; Noël F; Liechti R; Massenet-Regad L; Rigade S; Götz L; Hoffmann C; Amblard E; Saichi M; Ibrahim MM; Pollard J; Medvedovic J; Roider HG; Soumelis V
Cancer Res; 2023 Feb; 83(3):363-373. PubMed ID: 36459564
[TBL] [Abstract][Full Text] [Related]
3. CellCommuNet: an atlas of cell-cell communication networks from single-cell RNA sequencing of human and mouse tissues in normal and disease states.
Ma Q; Li Q; Zheng X; Pan J
Nucleic Acids Res; 2024 Jan; 52(D1):D597-D606. PubMed ID: 37850657
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. CellCall: integrating paired ligand-receptor and transcription factor activities for cell-cell communication.
Zhang Y; Liu T; Hu X; Wang M; Wang J; Zou B; Tan P; Cui T; Dou Y; Ning L; Huang Y; Rao S; Wang D; Zhao X
Nucleic Acids Res; 2021 Sep; 49(15):8520-8534. PubMed ID: 34331449
[TBL] [Abstract][Full Text] [Related]
6. CellTalkDB: a manually curated database of ligand-receptor interactions in humans and mice.
Shao X; Liao J; Li C; Lu X; Cheng J; Fan X
Brief Bioinform; 2021 Jul; 22(4):. PubMed ID: 33147626
[TBL] [Abstract][Full Text] [Related]
7. Independent component analysis based gene co-expression network inference (ICAnet) to decipher functional modules for better single-cell clustering and batch integration.
Wang W; Tan H; Sun M; Han Y; Chen W; Qiu S; Zheng K; Wei G; Ni T
Nucleic Acids Res; 2021 May; 49(9):e54. PubMed ID: 33619563
[TBL] [Abstract][Full Text] [Related]
8. Deciphering ligand-receptor-mediated intercellular communication based on ensemble deep learning and the joint scoring strategy from single-cell transcriptomic data.
Peng L; Tan J; Xiong W; Zhang L; Wang Z; Yuan R; Li Z; Chen X
Comput Biol Med; 2023 Sep; 163():107137. PubMed ID: 37364528
[TBL] [Abstract][Full Text] [Related]
9. Single-cell RNA sequencing in cancer research.
Zhang Y; Wang D; Peng M; Tang L; Ouyang J; Xiong F; Guo C; Tang Y; Zhou Y; Liao Q; Wu X; Wang H; Yu J; Li Y; Li X; Li G; Zeng Z; Tan Y; Xiong W
J Exp Clin Cancer Res; 2021 Mar; 40(1):81. PubMed ID: 33648534
[TBL] [Abstract][Full Text] [Related]
10. Forward single-cell sequencing into clinical application: Understanding of cancer microenvironment at single-cell solution.
Liu X; Powell CA; Wang X
Clin Transl Med; 2022 Apr; 12(4):e782. PubMed ID: 35474615
[TBL] [Abstract][Full Text] [Related]
11. Data Analysis in Single-Cell Transcriptome Sequencing.
Gao S
Methods Mol Biol; 2018; 1754():311-326. PubMed ID: 29536451
[TBL] [Abstract][Full Text] [Related]
12. TMExplorer: A tumour microenvironment single-cell RNAseq database and search tool.
Christensen E; Naidas A; Chen D; Husic M; Shooshtari P
PLoS One; 2022; 17(9):e0272302. PubMed ID: 36084081
[TBL] [Abstract][Full Text] [Related]
13. Inferring microenvironmental regulation of gene expression from single-cell RNA sequencing data using scMLnet with an application to COVID-19.
Cheng J; Zhang J; Wu Z; Sun X
Brief Bioinform; 2021 Mar; 22(2):988-1005. PubMed ID: 33341869
[TBL] [Abstract][Full Text] [Related]
14. Identification of Intercellular Signaling Changes Across Conditions and Their Influence on Intracellular Signaling Response From Multiple Single-Cell Datasets.
Hao M; Zou X; Jin S
Front Genet; 2021; 12():751158. PubMed ID: 34858473
[TBL] [Abstract][Full Text] [Related]
15. Intercellular Communication-Related Molecular Subtypes and a Gene Signature Identified by the Single-Cell RNA Sequencing Combined with a Transcriptomic Analysis.
Guan P; Cai W; Wu K; Jiang F; Wu J; Zhai X; Zeng M
Dis Markers; 2022; 2022():6837849. PubMed ID: 35620271
[TBL] [Abstract][Full Text] [Related]
16. Single-cell RNA sequencing in breast cancer: Understanding tumor heterogeneity and paving roads to individualized therapy.
Ding S; Chen X; Shen K
Cancer Commun (Lond); 2020 Aug; 40(8):329-344. PubMed ID: 32654419
[TBL] [Abstract][Full Text] [Related]
17. TISCH2: expanded datasets and new tools for single-cell transcriptome analyses of the tumor microenvironment.
Han Y; Wang Y; Dong X; Sun D; Liu Z; Yue J; Wang H; Li T; Wang C
Nucleic Acids Res; 2023 Jan; 51(D1):D1425-D1431. PubMed ID: 36321662
[TBL] [Abstract][Full Text] [Related]
18. Cellinker: a platform of ligand-receptor interactions for intercellular communication analysis.
Zhang Y; Liu T; Wang J; Zou B; Li L; Yao L; Chen K; Ning L; Wu B; Zhao X; Wang D
Bioinformatics; 2021 Jan; ():. PubMed ID: 33471060
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. SPASCER: spatial transcriptomics annotation at single-cell resolution.
Fan Z; Luo Y; Lu H; Wang T; Feng Y; Zhao W; Kim P; Zhou X
Nucleic Acids Res; 2023 Jan; 51(D1):D1138-D1149. PubMed ID: 36243975
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
