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 *

200 related articles for article (PubMed ID: 34651173)

  • 1. Building the mega single-cell transcriptome ocular meta-atlas.
    Swamy VS; Fufa TD; Hufnagel RB; McGaughey DM
    Gigascience; 2021 Oct; 10(10):. PubMed ID: 34651173
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A simple, scalable approach to building a cross-platform transcriptome atlas.
    Angel PW; Rajab N; Deng Y; Pacheco CM; Chen T; Lê Cao KA; Choi J; Wells CA
    PLoS Comput Biol; 2020 Sep; 16(9):e1008219. PubMed ID: 32986694
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Scalable batch-correction approach for integrating large-scale single-cell transcriptomes.
    Shen X; Shen H; Wu D; Feng M; Hu J; Liu J; Yang Y; Yang M; Li Y; Shi L; Chen K; Li X
    Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 35947966
    [TBL] [Abstract][Full Text] [Related]  

  • 4. HUSCH: an integrated single-cell transcriptome atlas for human tissue gene expression visualization and analyses.
    Shi X; Yu Z; Ren P; Dong X; Ding X; Song J; Zhang J; Li T; Wang C
    Nucleic Acids Res; 2023 Jan; 51(D1):D1029-D1037. PubMed ID: 36318258
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Sincast: a computational framework to predict cell identities in single-cell transcriptomes using bulk atlases as references.
    Deng Y; Choi J; Lê Cao KA
    Brief Bioinform; 2022 May; 23(3):. PubMed ID: 35362513
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Integrating single-cell RNA-seq datasets with substantial batch effects.
    Hrovatin K; Moinfar AA; Zappia L; Lapuerta AT; Lengerich B; Kellis M; Theis FJ
    bioRxiv; 2024 Feb; ():. PubMed ID: 37961672
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Creation of a Single Cell RNASeq Meta-Atlas to Define Human Liver Immune Homeostasis.
    Rocque B; Barbetta A; Singh P; Goldbeck C; Helou DG; Loh YE; Ung N; Lee J; Akbari O; Emamaullee J
    Front Immunol; 2021; 12():679521. PubMed ID: 34335581
    [TBL] [Abstract][Full Text] [Related]  

  • 8. The Human Eye Transcriptome Atlas: A searchable comparative transcriptome database for healthy and diseased human eye tissue.
    Wolf J; Boneva S; Schlecht A; Lapp T; Auw-Haedrich C; Lagrèze W; Agostini H; Reinhard T; Schlunck G; Lange C
    Genomics; 2022 Mar; 114(2):110286. PubMed ID: 35124170
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Risk-conscious correction of batch effects: maximising information extraction from high-throughput genomic datasets.
    Oytam Y; Sobhanmanesh F; Duesing K; Bowden JC; Osmond-McLeod M; Ross J
    BMC Bioinformatics; 2016 Sep; 17(1):332. PubMed ID: 27585881
    [TBL] [Abstract][Full Text] [Related]  

  • 10. SelectBCM tool: a batch evaluation framework to select the most appropriate batch-correction methods for bulk transcriptome analysis.
    Mishra M; Barck L; Moreno P; Heger G; Song Y; Thornton JM; Papatheodorou I
    NAR Genom Bioinform; 2023 Mar; 5(1):lqad014. PubMed ID: 36879900
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Network meta-analysis correlates with analysis of merged independent transcriptome expression data.
    Winter C; Kosch R; Ludlow M; Osterhaus ADME; Jung K
    BMC Bioinformatics; 2019 Mar; 20(1):144. PubMed ID: 30876387
    [TBL] [Abstract][Full Text] [Related]  

  • 12. GLOBE: a contrastive learning-based framework for integrating single-cell transcriptome datasets.
    Yan X; Zheng R; Li M
    Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 35901449
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Deep sequencing reveals cell-type-specific patterns of single-cell transcriptome variation.
    Dueck H; Khaladkar M; Kim TK; Spaethling JM; Francis C; Suresh S; Fisher SA; Seale P; Beck SG; Bartfai T; Kuhn B; Eberwine J; Kim J
    Genome Biol; 2015 Jun; 16(1):122. PubMed ID: 26056000
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Integration and transfer learning of single-cell transcriptomes via cFIT.
    Peng M; Li Y; Wamsley B; Wei Y; Roeder K
    Proc Natl Acad Sci U S A; 2021 Mar; 118(10):. PubMed ID: 33658382
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Automatic cell-type harmonization and integration across Human Cell Atlas datasets.
    Xu C; Prete M; Webb S; Jardine L; Stewart BJ; Hoo R; He P; Meyer KB; Teichmann SA
    Cell; 2023 Dec; 186(26):5876-5891.e20. PubMed ID: 38134877
    [TBL] [Abstract][Full Text] [Related]  

  • 16. JOINTLY: interpretable joint clustering of single-cell transcriptomes.
    Møller AF; Madsen JGS
    Nat Commun; 2023 Dec; 14(1):8473. PubMed ID: 38123569
    [TBL] [Abstract][Full Text] [Related]  

  • 17. SCIBER: a simple method for removing batch effects from single-cell RNA-sequencing data.
    Gan D; Li J
    Bioinformatics; 2023 Jan; 39(1):. PubMed ID: 36548380
    [TBL] [Abstract][Full Text] [Related]  

  • 18. [Web-based gene expression analysis-paving the way to decode healthy and diseased ocular tissue].
    Wolf J; Lapp T; Reinhard T; Agostini H; Schlunck G; Lange C
    Ophthalmologie; 2022 Sep; 119(9):929-936. PubMed ID: 35194679
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Integration of quantitated expression estimates from polyA-selected and rRNA-depleted RNA-seq libraries.
    Bush SJ; McCulloch MEB; Summers KM; Hume DA; Clark EL
    BMC Bioinformatics; 2017 Jun; 18(1):301. PubMed ID: 28610557
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Spectacle: An interactive resource for ocular single-cell RNA sequencing data analysis.
    Voigt AP; Whitmore SS; Lessing ND; DeLuca AP; Tucker BA; Stone EM; Mullins RF; Scheetz TE
    Exp Eye Res; 2020 Nov; 200():108204. PubMed ID: 32910939
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.