These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

241 related articles for article (PubMed ID: 35963997)

  • 1. Metacells untangle large and complex single-cell transcriptome networks.
    Bilous M; Tran L; Cianciaruso C; Gabriel A; Michel H; Carmona SJ; Pittet MJ; Gfeller D
    BMC Bioinformatics; 2022 Aug; 23(1):336. PubMed ID: 35963997
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Building and analyzing metacells in single-cell genomics data.
    Bilous M; Hérault L; Gabriel AA; Teleman M; Gfeller D
    Mol Syst Biol; 2024 Jul; 20(7):744-766. PubMed ID: 38811801
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Metacell-2: a divide-and-conquer metacell algorithm for scalable scRNA-seq analysis.
    Ben-Kiki O; Bercovich A; Lifshitz A; Tanay A
    Genome Biol; 2022 Apr; 23(1):100. PubMed ID: 35440087
    [TBL] [Abstract][Full Text] [Related]  

  • 4. An accessible, interactive GenePattern Notebook for analysis and exploration of single-cell transcriptomic data.
    Mah CK; Wenzel AT; Juarez EF; Tabor T; Reich MM; Mesirov JP
    F1000Res; 2018; 7():1306. PubMed ID: 31316748
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Metacell-based differential expression analysis identifies cell type specific temporal gene response programs in COVID-19 patient PBMCs.
    O'Leary K; Zheng D
    NPJ Syst Biol Appl; 2024 Apr; 10(1):36. PubMed ID: 38580667
    [TBL] [Abstract][Full Text] [Related]  

  • 6. scNPF: an integrative framework assisted by network propagation and network fusion for preprocessing of single-cell RNA-seq data.
    Ye W; Ji G; Ye P; Long Y; Xiao X; Li S; Su Y; Wu X
    BMC Genomics; 2019 May; 20(1):347. PubMed ID: 31068142
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Analysis and Visualization of Single-Cell Sequencing Data with Scanpy and MetaCell: A Tutorial.
    Li Y; Sun C; Romanova DY; Wu DO; Fang R; Moroz LL
    Methods Mol Biol; 2024; 2757():383-445. PubMed ID: 38668977
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A systematic evaluation of single-cell RNA-sequencing imputation methods.
    Hou W; Ji Z; Ji H; Hicks SC
    Genome Biol; 2020 Aug; 21(1):218. PubMed ID: 32854757
    [TBL] [Abstract][Full Text] [Related]  

  • 9. MCProj: metacell projection for interpretable and quantitative use of transcriptional atlases.
    Ben-Kiki O; Bercovich A; Lifshitz A; Raz O; Brook D; Tanay A
    Genome Biol; 2023 Oct; 24(1):220. PubMed ID: 37798781
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Single-Cell RNA Sequencing Analysis: A Step-by-Step Overview.
    Slovin S; Carissimo A; Panariello F; Grimaldi A; Bouché V; Gambardella G; Cacchiarelli D
    Methods Mol Biol; 2021; 2284():343-365. PubMed ID: 33835452
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Multi-level cellular and functional annotation of single-cell transcriptomes using scPipeline.
    Mikolajewicz N; Gacesa R; Aguilera-Uribe M; Brown KR; Moffat J; Han H
    Commun Biol; 2022 Oct; 5(1):1142. PubMed ID: 36307536
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Machine learning and statistical methods for clustering single-cell RNA-sequencing data.
    Petegrosso R; Li Z; Kuang R
    Brief Bioinform; 2020 Jul; 21(4):1209-1223. PubMed ID: 31243426
    [TBL] [Abstract][Full Text] [Related]  

  • 13. MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions.
    Baran Y; Bercovich A; Sebe-Pedros A; Lubling Y; Giladi A; Chomsky E; Meir Z; Hoichman M; Lifshitz A; Tanay A
    Genome Biol; 2019 Oct; 20(1):206. PubMed ID: 31604482
    [TBL] [Abstract][Full Text] [Related]  

  • 14. scIMC: a platform for benchmarking comparison and visualization analysis of scRNA-seq data imputation methods.
    Dai C; Jiang Y; Yin C; Su R; Zeng X; Zou Q; Nakai K; Wei L
    Nucleic Acids Res; 2022 May; 50(9):4877-4899. PubMed ID: 35524568
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Data Analysis in Single-Cell Transcriptome Sequencing.
    Gao S
    Methods Mol Biol; 2018; 1754():311-326. PubMed ID: 29536451
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A flexible network-based imputing-and-fusing approach towards the identification of cell types from single-cell RNA-seq data.
    Qi Y; Guo Y; Jiao H; Shang X
    BMC Bioinformatics; 2020 Jun; 21(1):240. PubMed ID: 32527285
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A Bayesian mixture model for clustering droplet-based single-cell transcriptomic data from population studies.
    Sun Z; Chen L; Xin H; Jiang Y; Huang Q; Cillo AR; Tabib T; Kolls JK; Bruno TC; Lafyatis R; Vignali DAA; Chen K; Ding Y; Hu M; Chen W
    Nat Commun; 2019 Apr; 10(1):1649. PubMed ID: 30967541
    [TBL] [Abstract][Full Text] [Related]  

  • 18. DIMM-SC: a Dirichlet mixture model for clustering droplet-based single cell transcriptomic data.
    Sun Z; Wang T; Deng K; Wang XF; Lafyatis R; Ding Y; Hu M; Chen W
    Bioinformatics; 2018 Jan; 34(1):139-146. PubMed ID: 29036318
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Respiratory epithelial cell types, states and fates in the era of single-cell RNA-sequencing.
    Dudchenko O; Ordovas-Montanes J; Bingle CD
    Biochem J; 2023 Jul; 480(13):921-939. PubMed ID: 37410389
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Autoencoder-based cluster ensembles for single-cell RNA-seq data analysis.
    Geddes TA; Kim T; Nan L; Burchfield JG; Yang JYH; Tao D; Yang P
    BMC Bioinformatics; 2019 Dec; 20(Suppl 19):660. PubMed ID: 31870278
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 13.