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 *

139 related articles for article (PubMed ID: 33927748)

  • 1. CBA: Cluster-Guided Batch Alignment for Single Cell RNA-seq.
    Yu W; Mahfouz A; Reinders MJT
    Front Genet; 2021; 12():644211. PubMed ID: 33927748
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Batch alignment of single-cell transcriptomics data using deep metric learning.
    Yu X; Xu X; Zhang J; Li X
    Nat Commun; 2023 Feb; 14(1):960. PubMed ID: 36810607
    [TBL] [Abstract][Full Text] [Related]  

  • 3. deepMNN: Deep Learning-Based Single-Cell RNA Sequencing Data Batch Correction Using Mutual Nearest Neighbors.
    Zou B; Zhang T; Zhou R; Jiang X; Yang H; Jin X; Bai Y
    Front Genet; 2021; 12():708981. PubMed ID: 34447413
    [TBL] [Abstract][Full Text] [Related]  

  • 4. SMNN: batch effect correction for single-cell RNA-seq data via supervised mutual nearest neighbor detection.
    Yang Y; Li G; Qian H; Wilhelmsen KC; Shen Y; Li Y
    Brief Bioinform; 2021 May; 22(3):. PubMed ID: 32591778
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A benchmark of batch-effect correction methods for single-cell RNA sequencing data.
    Tran HTN; Ang KS; Chevrier M; Zhang X; Lee NYS; Goh M; Chen J
    Genome Biol; 2020 Jan; 21(1):12. PubMed ID: 31948481
    [TBL] [Abstract][Full Text] [Related]  

  • 6. BATMAN: Fast and Accurate Integration of Single-Cell RNA-Seq Datasets via Minimum-Weight Matching.
    Mandric I; Hill BL; Freund MK; Thompson M; Halperin E
    iScience; 2020 Jun; 23(6):101185. PubMed ID: 32504875
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Batch correction of single-cell sequencing data via an autoencoder architecture.
    Danino R; Nachman I; Sharan R
    Bioinform Adv; 2024; 4(1):vbad186. PubMed ID: 38213820
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A comparison of methods accounting for batch effects in differential expression analysis of UMI count based single cell RNA sequencing.
    Chen W; Zhang S; Williams J; Ju B; Shaner B; Easton J; Wu G; Chen X
    Comput Struct Biotechnol J; 2020; 18():861-873. PubMed ID: 32322368
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 10. Comparison of Scanpy-based algorithms to remove the batch effect from single-cell RNA-seq data.
    Li J; Yu C; Ma L; Wang J; Guo G
    Cell Regen; 2020 Jul; 9(1):10. PubMed ID: 32632608
    [TBL] [Abstract][Full Text] [Related]  

  • 11. REBET: a method to determine the number of cell clusters based on batch effect removal.
    Fang ZY; Lin CX; Xu YP; Li HD; Xu QS
    Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34131702
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Beaconet: A Reference-Free Method for Integrating Multiple Batches of Single-Cell Transcriptomic Data in Original Molecular Space.
    Xu H; Ye Y; Duan R; Gao Y; Hu Y; Gao L
    Adv Sci (Weinh); 2024 Jul; 11(26):e2306770. PubMed ID: 38711214
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A multitask clustering approach for single-cell RNA-seq analysis in Recessive Dystrophic Epidermolysis Bullosa.
    Zhang H; Lee CAA; Li Z; Garbe JR; Eide CR; Petegrosso R; Kuang R; Tolar J
    PLoS Comput Biol; 2018 Apr; 14(4):e1006053. PubMed ID: 29630593
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Batch effect detection and correction in RNA-seq data using machine-learning-based automated assessment of quality.
    Sprang M; Andrade-Navarro MA; Fontaine JF
    BMC Bioinformatics; 2022 Jul; 23(Suppl 6):279. PubMed ID: 35836114
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. 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]  

  • 17. DISCERN: deep single-cell expression reconstruction for improved cell clustering and cell subtype and state detection.
    Hausmann F; Ergen C; Khatri R; Marouf M; Hänzelmann S; Gagliani N; Huber S; Machart P; Bonn S
    Genome Biol; 2023 Sep; 24(1):212. PubMed ID: 37730638
    [TBL] [Abstract][Full Text] [Related]  

  • 18. BBKNN: fast batch alignment of single cell transcriptomes.
    Polański K; Young MD; Miao Z; Meyer KB; Teichmann SA; Park JE
    Bioinformatics; 2020 Feb; 36(3):964-965. PubMed ID: 31400197
    [TBL] [Abstract][Full Text] [Related]  

  • 19. scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data.
    Zhang Z; Zhao X; Qiu P; Zhang X
    bioRxiv; 2023 May; ():. PubMed ID: 37205545
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Joint learning dimension reduction and clustering of single-cell RNA-sequencing data.
    Wu W; Ma X
    Bioinformatics; 2020 Jun; 36(12):3825-3832. PubMed ID: 32246821
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.