Analysis of gene expression data using a linear mixed model/finite mixture model approach: Application to regional differences in the human brain

Bioinformatics

Volumn 30, Issue 11, 2014, Pages 1555-1561

  Trabzuni, Daniah ^a,b   Thomson, Peter C ^c  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b KING FAISAL SPECIALIST HOSPITAL AND RESEARCH CENTER   (Saudi Arabia)

^c UNIVERSITY OF SYDNEY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BRAIN; FEMALE; GENE EXPRESSION PROFILING; HUMAN; HUMAN GENOME; MALE; METABOLISM; METHODOLOGY; STATISTICAL MODEL;

BRAIN; FEMALE; GENE EXPRESSION PROFILING; GENOME, HUMAN; HUMANS; LINEAR MODELS; MALE;

EID: 84901323082     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btu088     Document Type: Article

References (19)

1. Auer, P.L. et al. (2012) Differential expression - The next generation and beyond. Brief. Funct. Genomics, 11, 57-62
2. Butler, D. et al. (2007) ASReml-R Reference Manual. Queensland Department of Primary Industries and Fisheries, Brisbane
3. Geschwind, D.H. and Konopka, G. (2009) Neuroscience in the era of functional genomics and systems biology. Nature, 461, 908-915
4. Ho Sui, S.J. et al. (2005) oPOSSUM: Identification of over-represented transcription factor binding sites in co-expressed genes. Nucleic Acids Res, 33, 3154-3164. (Pubitemid 41194459
5. Huang da, W., Sherman, B.T. and Lempicki, R.A. (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc., 4, 44-57
6. Irizarry, R.A. et al. (2003) Exploration, normalization and summaries of high density oligonucleotide array probe level data. Biostatistics, 2, 249-264
7. Ji, H. and Liu, X.S. (2010) Analyzing 'omics data using hierarchical models. Nat. Biotechnol., 28, 337-340
8. Johnson, M.B. et al. (2009) Functional and evolutionary insights into human brain development through global transcriptome analysis. Neuron, 62, 494-509
9. Kerr, M.K. et al. (2000) Analysis of variance for gene expression microarray data. J. Comp. Biol., 7, 819-837
10. Kumar, A. et al. (2013) Age-Associated changes in gene expression in human brain and isolated neurons. Neurobiol. Aging, 34, 1199-1209
11. McLachlan, G.J. et al. (2002) A mixture model-based approach to the clustering of gene expression data. Bioinformatics, 18, 413-422. (Pubitemid 34284942
12. McLachlan, G.J. and Krishnan, T. (1997) The EM Algorithm and Extensions. Wiley, New York
13. Millar, T. et al. (2007) Tissue and organ donation for research in forensic pathology: The MRC sudden death brain and tissue bank. J. Pathol., 213, 369-375
14. Pinheiro, J.C. and Bates, D.M. (2000) Mixed Effects Models in S and S-PLUS (Statistics and Computing). Springer, New York
15. Singh, M. et al. (2013) Comparative transcriptome analyses reveal conserved and distinct mechanisms in ovine and bovine lactation. Funct. Integr. Genomics, 13, 1-17
16. Smyth, G.K. (2004) Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Molec. Biol., 3, Article 3
17. Trabzuni, D. et al. (2011) Quality control parameters on a large dataset of regionally dissected human control brains for whole genome expression studies. J. Neurochem., 119, 275-282
18. Trabzuni, D. et al. (2013)Widespread sex differences in gene expression and splicing in the adult human brain. Nat. Commun., 4, Article 2771
19. Zhang, B. and Horvath, S. (2005) A general framework for weighted gene co-expression network analysis. Statistical Applications in Genetics and Molecular Biology, 4, Article 17