메뉴 건너뛰기




Volumn 33, Issue 16, 2017, Pages 2539-2546

Removal of batch effects using distribution-matching residual networks

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGY; CYTOPHOTOMETRY; HUMAN; MACHINE LEARNING; MEASUREMENT ACCURACY; PROCEDURES; SEQUENCE ANALYSIS; SINGLE CELL ANALYSIS; STATISTICS;

EID: 85040695740     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btx196     Document Type: Article
Times cited : (125)

References (27)
  • 2
    • 84876799270 scopus 로고    scopus 로고
    • Normalization of mass cytometry data with bead standards
    • Finck,R. et al. (2013) Normalization of mass cytometry data with bead standards. Cytometry Part A, 83, 483–494.
    • (2013) Cytometry Part A , vol.83 , pp. 483-494
    • Finck, R.1
  • 3
    • 84862277874 scopus 로고    scopus 로고
    • Understanding the difficulty of training deep feed-forward neural networks
    • Glorot,X., and Bengio,Y. (2010) Understanding the difficulty of training deep feed-forward neural networks. In Proceedings of AISTATS, Sardinia, Italy, vol 9, pp. 249–256.
    • (2010) Proceedings of AISTATS, Sardinia, Italy , vol.9 , pp. 249-256
    • Glorot, X.1    Bengio, Y.2
  • 5
    • 84859477054 scopus 로고    scopus 로고
    • A kernel two-sample test
    • Gretton,A. et al. (2012) A kernel two-sample test. J. Mach. Learn. Res., 13, 723–773.
    • (2012) J. Mach. Learn. Res. , vol.13 , pp. 723-773
    • Gretton, A.1
  • 6
    • 77249135054 scopus 로고    scopus 로고
    • Per-channel basis normalization methods for flow cytometry data
    • Hahne,F. et al. (2010) Per-channel basis normalization methods for flow cytometry data. Cytometry Part A, 77, 121–131.
    • (2010) Cytometry Part A , vol.77 , pp. 121-131
    • Hahne, F.1
  • 10
    • 84990050094 scopus 로고    scopus 로고
    • Identity mappings in deep residual networks
    • He,K. et al. (2016). Identity mappings in deep residual networks. European Conference on Computer Vision, pp. 630–645.
    • (2016) European Conference on Computer Vision , pp. 630-645
    • He, K.1
  • 12
    • 84893905629 scopus 로고    scopus 로고
    • Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types
    • Jaitin,D.A. et al. (2014) Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science, 343, 776–779.
    • (2014) Science , vol.343 , pp. 776-779
    • Jaitin, D.A.1
  • 13
    • 33845432928 scopus 로고    scopus 로고
    • Adjusting batch effects in microarray expression data using empirical bayes methods
    • Johnson,W.E. et al. (2007) Adjusting batch effects in microarray expression data using empirical bayes methods. Biostatistics, 8, 118–127.
    • (2007) Biostatistics , vol.8 , pp. 118-127
    • Johnson, W.E.1
  • 14
    • 84992126905 scopus 로고    scopus 로고
    • Standardization and quality control for high-dimensional mass cytometry studies of human samples
    • Kleinsteuber,K. et al. (2016) Standardization and quality control for high-dimensional mass cytometry studies of human samples. Cytometry A, 89, 903–913.
    • (2016) Cytometry A , vol.89 , pp. 903-913
    • Kleinsteuber, K.1
  • 15
    • 84859098571 scopus 로고    scopus 로고
    • The sva package for removing batch effects and other unwanted variation in high-throughput experiments
    • Leek,J.T. et al. (2012) The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics, 28, 882–883.
    • (2012) Bioinformatics , vol.28 , pp. 882-883
    • Leek, J.T.1
  • 16
    • 77956873627 scopus 로고    scopus 로고
    • Tackling the widespread and critical impact of batch effects in high-throughput data
    • Leek,J.T. et al. (2010) Tackling the widespread and critical impact of batch effects in high-throughput data. Nat. Rev. Genet., 11, 733–739.
    • (2010) Nat. Rev. Genet. , vol.11 , pp. 733-739
    • Leek, J.T.1
  • 17
    • 34848914038 scopus 로고    scopus 로고
    • Capturing heterogeneity in gene expression studies by surrogate variable analysis
    • Leek,J.T., and Storey,J.D. (2007) Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet., 3, e161.
    • (2007) PLoS Genet. , vol.3 , pp. e161
    • Leek, J.T.1    Storey, J.D.2
  • 18
    • 85040961635 scopus 로고    scopus 로고
    • arXiv preprint
    • Li,S. et al. (2016). Demystifying resnet. arXiv preprint arXiv:1611.01186.
    • (2016) Demystifying Resnet
    • Li, S.1
  • 19
    • 84970016114 scopus 로고    scopus 로고
    • Generative moment matching networks
    • Lille, France
    • Li,Y. et al. (2015). Generative moment matching networks. In International Conference on Machine Learning, Lille, France, pp. 1718–1727.
    • (2015) International Conference on Machine Learning , pp. 1718-1727
    • Li, Y.1
  • 20
    • 85019790380 scopus 로고    scopus 로고
    • Evaluation of methods in removing batch effects on RNA-seq data
    • Liu,Q., and Markatou,M. (2016) Evaluation of methods in removing batch effects on rna-seq data. Infect. Dis. Transl. Med., 2, 3–9.
    • (2016) Infect. Dis. Transl. Med. , vol.2 , pp. 3-9
    • Liu, Q.1    Markatou, M.2
  • 21
    • 84929684999 scopus 로고    scopus 로고
    • Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets
    • Macosko,E.Z. et al. (2015) Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell, 161, 1202–1214.
    • (2015) Cell , vol.161 , pp. 1202-1214
    • Macosko, E.Z.1
  • 22
    • 84959901445 scopus 로고    scopus 로고
    • Methods that remove batch effects while retaining group differences May lead to exaggerated confidence in downstream analy-ses
    • Nygaard,V. et al. (2016) Methods that remove batch effects while retaining group differences May lead to exaggerated confidence in downstream analy-ses. Biostatistics, 17, 29–39.
    • (2016) Biostatistics , vol.17 , pp. 29-39
    • Nygaard, V.1
  • 23
    • 84887101406 scopus 로고    scopus 로고
    • Smart-seq2 for sensitive full-length transcriptome profiling in single cells
    • Picelli,S. et al. (2013) Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods, 10, 1096–1098.
    • (2013) Nat. Methods , vol.10 , pp. 1096-1098
    • Picelli, S.1
  • 24
    • 84983741021 scopus 로고    scopus 로고
    • Comprehensive classification of retinal bipolar neurons by single-cell transcriptomics
    • Shekhar,K. et al. (2016) Comprehensive classification of retinal bipolar neurons by single-cell transcriptomics. Cell, 166, 1308–1323.
    • (2016) Cell , vol.166 , pp. 1308-1323
    • Shekhar, K.1
  • 25
    • 84965146271 scopus 로고    scopus 로고
    • Mass cytometry: Single cells, many features
    • Spitzer,M.H., and Nolan,G.P. (2016) Mass cytometry: Single cells, many features. Cell, 165, 780–791.
    • (2016) Cell , vol.165 , pp. 780-791
    • Spitzer, M.H.1    Nolan, G.P.2
  • 26
    • 84893343292 scopus 로고    scopus 로고
    • Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
    • Tieleman,T., and Hinton,G. (2012) Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning, vol. 4, pp. 26–31.
    • (2012) COURSERA: Neural Networks for Machine Learning , vol.4 , pp. 26-31
    • Tieleman, T.1    Hinton, G.2
  • 27
    • 56449089103 scopus 로고    scopus 로고
    • Extracting and composing robust features with denoising autoencoders
    • Helsinki, Finland, ACM
    • Vincent,P. et al. (2008). Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland, pp. 1096–1103. ACM.
    • (2008) Proceedings of The 25th International Conference on Machine Learning , pp. 1096-1103
    • Vincent, P.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.