메뉴 건너뛰기




Volumn 19, Issue 11, 2003, Pages 649-659

Fundamentals of cDNA microarray data analysis

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEMENTARY DNA;

EID: 0242317346     PISSN: 01689525     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.tig.2003.09.015     Document Type: Review
Times cited : (251)

References (58)
  • 3
    • 0036900895 scopus 로고    scopus 로고
    • Fundamentals of experimental design for cDNA microarrays
    • Churchill G.A. Fundamentals of experimental design for cDNA microarrays. Nat. Genet. 32:(Suppl. 2):2002;490-495.
    • (2002) Nat. Genet. , vol.32 , Issue.SUPPL. 2 , pp. 490-495
    • Churchill, G.A.1
  • 4
    • 0036319960 scopus 로고    scopus 로고
    • Design issues for cDNA microarray experiments
    • Yang Y.H., Speed T. Design issues for cDNA microarray experiments. Nat. Rev. Genet. 3:2002;579-588.
    • (2002) Nat. Rev. Genet. , vol.3 , pp. 579-588
    • Yang, Y.H.1    Speed, T.2
  • 5
    • 0037335024 scopus 로고    scopus 로고
    • Experimental design of DNA microarray experiments
    • Simon R.M., Dobbin K. Experimental design of DNA microarray experiments. Biotechniques. 2003;S16-S21.
    • (2003) Biotechniques
    • Simon, R.M.1    Dobbin, K.2
  • 6
    • 0035733719 scopus 로고    scopus 로고
    • Show me the data!
    • Perou C.M. Show me the data! Nat. Genet. 29:2001;373.
    • (2001) Nat. Genet. , vol.29 , pp. 373
    • Perou, C.M.1
  • 7
    • 18344396568 scopus 로고    scopus 로고
    • Minimum information about a microarray experiment (MIAME)-toward standards for microarray data
    • Brazma A., et al. Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat. Genet. 29:2001;365-371.
    • (2001) Nat. Genet. , vol.29 , pp. 365-371
    • Brazma, A.1
  • 8
    • 0037179669 scopus 로고    scopus 로고
    • Microarray standards at last
    • Microarray standards at last. Nature. 419:2002;323.
    • (2002) Nature , vol.419 , pp. 323
  • 9
    • 0035751799 scopus 로고    scopus 로고
    • Analysis of cDNA microarray images
    • Yang Y.H., et al. Analysis of cDNA microarray images. Brief. Bioinform. 2:2001;341-349.
    • (2001) Brief. Bioinform. , vol.2 , pp. 341-349
    • Yang, Y.H.1
  • 10
    • 0036180003 scopus 로고    scopus 로고
    • Fully automatic quantification of microarray image data
    • Jain A.N., et al. Fully automatic quantification of microarray image data. Genome Res. 12:2002;325-332.
    • (2002) Genome Res. , vol.12 , pp. 325-332
    • Jain, A.N.1
  • 11
    • 0036898577 scopus 로고    scopus 로고
    • Microarray data normalization and transformation
    • Quackenbush J. Microarray data normalization and transformation. Nat. Genet. 32:(Suppl.):2002;496-501.
    • (2002) Nat. Genet. , vol.32 , Issue.SUPPL. , pp. 496-501
    • Quackenbush, J.1
  • 12
    • 0036178995 scopus 로고    scopus 로고
    • Control genes and variability: Absence of ubiquitous reference transcripts in diverse mammalian expression studies
    • Lee P.D. Control genes and variability: absence of ubiquitous reference transcripts in diverse mammalian expression studies. Genome Res. 12:2002;292-297.
    • (2002) Genome Res. , vol.12 , pp. 292-297
    • Lee, P.D.1
  • 13
    • 0035699287 scopus 로고    scopus 로고
    • Characterization of variability in large-scale gene expression data: Implications for study design
    • Novak J.P., et al. Characterization of variability in large-scale gene expression data: implications for study design. Genomics. 79:2002;104-113.
    • (2002) Genomics , vol.79 , pp. 104-113
    • Novak, J.P.1
  • 14
    • 0035818559 scopus 로고    scopus 로고
    • Project normal: Defining normal variance in mouse gene expression
    • Pritchard C.C., et al. Project normal: defining normal variance in mouse gene expression. Proc. Natl. Acad. Sci. U. S. A. 98:2001;13266-13271.
    • (2001) Proc. Natl. Acad. Sci. U. S. A. , vol.98 , pp. 13266-13271
    • Pritchard, C.C.1
  • 15
    • 0036574916 scopus 로고    scopus 로고
    • Statistical issues with microarrays: Processing and analysis
    • Nadon R., Shoemaker J. Statistical issues with microarrays: processing and analysis. Trends Genet. 18:2002;265-271.
    • (2002) Trends Genet. , vol.18 , pp. 265-271
    • Nadon, R.1    Shoemaker, J.2
  • 16
    • 0034730124 scopus 로고    scopus 로고
    • Importance of replication in microarray gene expression studies: Statistical methods and evidence from repetitive cDNA hybridizations
    • Lee M.L., et al. Importance of replication in microarray gene expression studies: statistical methods and evidence from repetitive cDNA hybridizations. Proc. Natl. Acad. Sci. U. S. A. 97:2000;9834-9839.
    • (2000) Proc. Natl. Acad. Sci. U. S. A. , vol.97 , pp. 9834-9839
    • Lee, M.L.1
  • 17
  • 18
    • 0041423883 scopus 로고    scopus 로고
    • SAM thresholding and false discovery rates for detecting differential gene expression in DNA microarrays
    • G. et al. Parmigiani. Springer
    • Storey J.D., Tibshirani R. SAM thresholding and false discovery rates for detecting differential gene expression in DNA microarrays. Parmigiani G., et al. The Analysis of Gene Expression Data: Methods and Software. 2003;Springer.
    • (2003) The Analysis of Gene Expression Data: Methods and Software
    • Storey, J.D.1    Tibshirani, R.2
  • 19
    • 0034927555 scopus 로고    scopus 로고
    • Analysis of variance for gene expression microarray data
    • Kerr M.K., et al. Analysis of variance for gene expression microarray data. J. Comput. Biol. 7:2000;819-837.
    • (2000) J. Comput. Biol. , vol.7 , pp. 819-837
    • Kerr, M.K.1
  • 20
    • 0035827588 scopus 로고    scopus 로고
    • Improved statistical inference from DNA microarray data using analysis of variance and a Bayesian statistical framework. Analysis of global gene expression in Escherichia coli K12
    • Long A.D., et al. Improved statistical inference from DNA microarray data using analysis of variance and a Bayesian statistical framework. Analysis of global gene expression in Escherichia coli K12. J. Biol. Chem. 276:2001;19937-19944.
    • (2001) J. Biol. Chem. , vol.276 , pp. 19937-19944
    • Long, A.D.1
  • 21
    • 0034948896 scopus 로고    scopus 로고
    • A Bayesian framework for the analysis of microarray expression data: Regularized t test and statistical inferences of gene changes
    • Baldi P., Long A.D. A Bayesian framework for the analysis of microarray expression data: regularized t test and statistical inferences of gene changes. Bioinformatics. 17:2001;509-519.
    • (2001) Bioinformatics , vol.17 , pp. 509-519
    • Baldi, P.1    Long, A.D.2
  • 22
    • 0034807713 scopus 로고    scopus 로고
    • Analysing gene expression data from DNA microarrays to identify candidate genes
    • Wu T.D. Analysing gene expression data from DNA microarrays to identify candidate genes. J. Pathol. 195:2001;53-65.
    • (2001) J. Pathol. , vol.195 , pp. 53-65
    • Wu, T.D.1
  • 23
    • 0036376993 scopus 로고    scopus 로고
    • Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments
    • Dudoit S., et al. Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Stat. Sinica. 12:2002;111-139.
    • (2002) Stat. Sinica , vol.12 , pp. 111-139
    • Dudoit, S.1
  • 24
    • 14844364789 scopus 로고    scopus 로고
    • Post-analysis follow-up and validation of microarray experiments
    • Chuaqui R.F., et al. Post-analysis follow-up and validation of microarray experiments. Nat. Genet. 32:(Suppl. 2):2002;509-514.
    • (2002) Nat. Genet. , vol.32 , Issue.SUPPL. 2 , pp. 509-514
    • Chuaqui, R.F.1
  • 25
    • 0037433040 scopus 로고    scopus 로고
    • Identifying differentially expressed genes using false discovery rate controlling procedures
    • Reiner A., et al. Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics. 19:2003;368-375.
    • (2003) Bioinformatics , vol.19 , pp. 368-375
    • Reiner, A.1
  • 26
    • 0033657261 scopus 로고    scopus 로고
    • Principal components analysis to summarize microarray experiments: Application to sporulation time series
    • Raychaudhuri S. Principal components analysis to summarize microarray experiments: application to sporulation time series. Pac. Symp. Biocomput. 2000;455-466.
    • (2000) Pac. Symp. Biocomput. , pp. 455-466
    • Raychaudhuri, S.1
  • 27
    • 0034730140 scopus 로고    scopus 로고
    • Singular value decomposition for genome-wide expression data processing and modeling
    • Alter O., et al. Singular value decomposition for genome-wide expression data processing and modeling. Proc. Natl. Acad. Sci. U. S. A. 97:2000;10101-10106.
    • (2000) Proc. Natl. Acad. Sci. U. S. A. , vol.97 , pp. 10101-10106
    • Alter, O.1
  • 28
    • 0032441150 scopus 로고    scopus 로고
    • Cluster analysis and display of genome-wide expression patterns
    • Eisen M.B., et al. Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. U. S. A. 95:1998;14863-14868.
    • (1998) Proc. Natl. Acad. Sci. U. S. A. , vol.95 , pp. 14863-14868
    • Eisen, M.B.1
  • 29
    • 0033028596 scopus 로고    scopus 로고
    • Systematic determination of genetic network architecture
    • Tavazoie S., et al. Systematic determination of genetic network architecture. Nat. Genet. 22:1999;281-285.
    • (1999) Nat. Genet. , vol.22 , pp. 281-285
    • Tavazoie, S.1
  • 30
    • 0033027794 scopus 로고    scopus 로고
    • Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation
    • Tamayo P., et al. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc. Natl. Acad. Sci. U. S. A. 96:1999;2907-2912.
    • (1999) Proc. Natl. Acad. Sci. U. S. A. , vol.96 , pp. 2907-2912
    • Tamayo, P.1
  • 31
    • 0035375137 scopus 로고    scopus 로고
    • Computational analysis of microarray data
    • Quackenbush J. Computational analysis of microarray data. Nat. Rev. Genet. 2:2001;418-427.
    • (2001) Nat. Rev. Genet. , vol.2 , pp. 418-427
    • Quackenbush, J.1
  • 32
    • 0035755549 scopus 로고    scopus 로고
    • Analysis of large-scale gene expression data
    • Sherlock G. Analysis of large-scale gene expression data. Brief. Bioinform. 2:2001;350-362.
    • (2001) Brief. Bioinform. , vol.2 , pp. 350-362
    • Sherlock, G.1
  • 33
    • 0036975148 scopus 로고    scopus 로고
    • Pattern recognition techniques in microarray data analysis: A survey
    • Valafar F. Pattern recognition techniques in microarray data analysis: a survey. Ann. N. Y. Acad. Sci. 980:2002;41-64.
    • (2002) Ann. N. Y. Acad. Sci. , vol.980 , pp. 41-64
    • Valafar, F.1
  • 34
    • 0037165140 scopus 로고    scopus 로고
    • Prediction of central nervous system embryonal tumour outcome based on gene expression
    • Pomeroy S.L., et al. Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature. 415:2002;436-442.
    • (2002) Nature , vol.415 , pp. 436-442
    • Pomeroy, S.L.1
  • 35
    • 18244409933 scopus 로고    scopus 로고
    • Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning
    • Shipp M.A., et al. Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat. Med. 8:2002;68-74.
    • (2002) Nat. Med. , vol.8 , pp. 68-74
    • Shipp, M.A.1
  • 36
    • 0034954414 scopus 로고    scopus 로고
    • Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks
    • Khan J., et al. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat. Med. 7:2001;673-679.
    • (2001) Nat. Med. , vol.7 , pp. 673-679
    • Khan, J.1
  • 37
    • 0034602774 scopus 로고    scopus 로고
    • Knowledge-based analysis of microarray gene expression data by using support vector machines
    • Brown M.P. Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc. Natl. Acad. Sci. U. S. A. 97:2000;262-267.
    • (2000) Proc. Natl. Acad. Sci. U. S. A. , vol.97 , pp. 262-267
    • Brown, M.P.1
  • 38
    • 0035082045 scopus 로고    scopus 로고
    • Neural network model of gene expression
    • Vohradsky J. Neural network model of gene expression. FASEB J. 15:2001;846-854.
    • (2001) FASEB J. , vol.15 , pp. 846-854
    • Vohradsky, J.1
  • 39
    • 0036141951 scopus 로고    scopus 로고
    • Finding genes in the C2C12 osteogenic pathway by k-nearest-neighbor classification of expression data
    • Theilhaber J., et al. Finding genes in the C2C12 osteogenic pathway by k-nearest-neighbor classification of expression data. Genome Res. 12:2002;165-176.
    • (2002) Genome Res. , vol.12 , pp. 165-176
    • Theilhaber, J.1
  • 40
    • 0033692876 scopus 로고    scopus 로고
    • Tissue classification with gene expression profiles
    • Ben-Dor A., et al. Tissue classification with gene expression profiles. J. Comput. Biol. 7:2000;559-583.
    • (2000) J. Comput. Biol. , vol.7 , pp. 559-583
    • Ben-Dor, A.1
  • 41
    • 0034785443 scopus 로고    scopus 로고
    • Identifying regulatory networks by combinatorial analysis of promoter elements
    • Pilpel Y., et al. Identifying regulatory networks by combinatorial analysis of promoter elements. Nat. Genet. 29:2001;153-159.
    • (2001) Nat. Genet. , vol.29 , pp. 153-159
    • Pilpel, Y.1
  • 42
    • 0034616930 scopus 로고    scopus 로고
    • Functional discovery via a compendium of expression profiles
    • Hughes T.R., et al. Functional discovery via a compendium of expression profiles. Cell. 102:2000;109-126.
    • (2000) Cell , vol.102 , pp. 109-126
    • Hughes, T.R.1
  • 43
    • 0033028596 scopus 로고    scopus 로고
    • Systematic determination of genetic network architecture
    • Tavazoie S., et al. Systematic determination of genetic network architecture. Nat. Genet. 22:1999;281-285.
    • (1999) Nat. Genet. , vol.22 , pp. 281-285
    • Tavazoie, S.1
  • 44
    • 0031616241 scopus 로고    scopus 로고
    • Reveal, a general reverse engineering algorithm for inference of genetic network architectures
    • Liang S., et al. Reveal, a general reverse engineering algorithm for inference of genetic network architectures. Pac. Symp. Biocomput. 1998;18-29.
    • (1998) Pac. Symp. Biocomput. , pp. 18-29
    • Liang, S.1
  • 45
    • 0033677274 scopus 로고    scopus 로고
    • Algorithms for identifying Boolean networks and related biological networks based on matrix multiplication and fingerprint function
    • Akutsu T., et al. Algorithms for identifying Boolean networks and related biological networks based on matrix multiplication and fingerprint function. J. Comput. Biol. 7:2000;331-343.
    • (2000) J. Comput. Biol. , vol.7 , pp. 331-343
    • Akutsu, T.1
  • 46
    • 0035221090 scopus 로고    scopus 로고
    • Development of a system for the inference of large scale genetic networks
    • Maki Y., et al. Development of a system for the inference of large scale genetic networks. Pac. Symp. Biocomput. 2001;446-458.
    • (2001) Pac. Symp. Biocomput. , pp. 446-458
    • Maki, Y.1
  • 47
    • 0033707946 scopus 로고    scopus 로고
    • Using Bayesian networks to analyze expression data
    • Friedman N., et al. Using Bayesian networks to analyze expression data. J. Comput. Biol. 7:2000;601-620.
    • (2000) J. Comput. Biol. , vol.7 , pp. 601-620
    • Friedman, N.1
  • 48
    • 0035221560 scopus 로고    scopus 로고
    • Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks
    • Hartemink A.J., et al. Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks. Pac. Symp. Biocomput. 2001;422-433.
    • (2001) Pac. Symp. Biocomput. , pp. 422-433
    • Hartemink, A.J.1
  • 49
    • 0036207347 scopus 로고    scopus 로고
    • Modeling and simulation of genetic regulatory systems: A literature review
    • de Jong H. Modeling and simulation of genetic regulatory systems: a literature review. J. Comput. Biol. 9:2002;67-103.
    • (2002) J. Comput. Biol. , vol.9 , pp. 67-103
    • De Jong, H.1
  • 50
    • 0033736476 scopus 로고    scopus 로고
    • Genetic network inference: From co-expression clustering to reverse engineering
    • D'haeseleer P. Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics. 16:2000;707-726.
    • (2000) Bioinformatics , vol.16 , pp. 707-726
    • D'haeseleer, P.1
  • 51
    • 0036061833 scopus 로고    scopus 로고
    • Pathway Processor: A tool for integrating whole-genome expression results into metabolic networks
    • Grosu P., et al. Pathway Processor: a tool for integrating whole-genome expression results into metabolic networks. Genome Res. 12:2002;1121-1126.
    • (2002) Genome Res. , vol.12 , pp. 1121-1126
    • Grosu, P.1
  • 52
    • 0028806048 scopus 로고
    • Quantitative monitoring of gene expression patterns with a complementary DNA microarray
    • Schena M., et al. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science. 270:1995;467-470.
    • (1995) Science , vol.270 , pp. 467-470
    • Schena, M.1
  • 53
    • 84984933234 scopus 로고    scopus 로고
    • High density synthetic oligonucleotide arrays
    • Lipshutz R.J., et al. High density synthetic oligonucleotide arrays. Nat. Genet. 21:(Suppl. 1):1999;20-24.
    • (1999) Nat. Genet. , vol.21 , Issue.SUPPL. 1 , pp. 20-24
    • Lipshutz, R.J.1
  • 54
    • 0038119656 scopus 로고    scopus 로고
    • Algorithms for high-density oligonucleotide array
    • Zhou Y., Abagyan R. Algorithms for high-density oligonucleotide array. Curr. Opin. Drug Discov. Devel. 6:2003;339-345.
    • (2003) Curr. Opin. Drug Discov. Devel. , vol.6 , pp. 339-345
    • Zhou, Y.1    Abagyan, R.2
  • 55
    • 0035752668 scopus 로고    scopus 로고
    • Feature extraction and normalization algorithms for high-density oligonucleotide gene expression array data
    • Schadt E.E., et al. Feature extraction and normalization algorithms for high-density oligonucleotide gene expression array data. J. Cell. Biochem. 37:(Suppl.):2001;120-125.
    • (2001) J. Cell. Biochem. , vol.37 , Issue.SUPPL. , pp. 120-125
    • Schadt, E.E.1
  • 56
    • 0034536614 scopus 로고    scopus 로고
    • Analyzing high-density oligonucleotide gene expression array data
    • Schadt E.E., et al. Analyzing high-density oligonucleotide gene expression array data. J. Cell. Biochem. 80:2000;192-202.
    • (2000) J. Cell. Biochem. , vol.80 , pp. 192-202
    • Schadt, E.E.1
  • 57
    • 12244298154 scopus 로고    scopus 로고
    • Statistical analysis of high-density oligonucleotide arrays: A multiplicative noise model
    • Sasik R., et al. Statistical analysis of high-density oligonucleotide arrays: a multiplicative noise model. Bioinformatics. 18:2002;1633-1640.
    • (2002) Bioinformatics , vol.18 , pp. 1633-1640
    • Sasik, R.1
  • 58
    • 0035793042 scopus 로고    scopus 로고
    • Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection
    • Li C., Wong W.H. Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proc. Natl. Acad. Sci. U. S. A. 98:2001;31-36.
    • (2001) Proc. Natl. Acad. Sci. U. S. A. , vol.98 , pp. 31-36
    • Li, C.1    Wong, W.H.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.