Concise review: New paradigms for down syndrome research using induced pluripotent stem cells: Tackling complex human genetic disease

Stem Cells Translational Medicine

Volumn 2, Issue 3, 2013, Pages 175-184

  Briggs, James A ^a   Mason, Elizabeth A ^a   Ovchinnikov, Dmitry A ^a   Wells, A Christine ^a   Wolvetang, Ernst J ^a  

^a UNIVERSITY OF QUEENSLAND   (Australia)

Author keywords

Chromosomal aberrations; Induced pluripotent stem cells; Neural differentiation; Reprogramming

Indexed keywords

LONG UNTRANSLATED RNA; MICRORNA; TRANSCRIPTOME;

ACUTE MEGAKARYOCYTIC LEUKEMIA; ARTICLE; CHROMOSOME 21; DOWN SYNDROME; DUODENUM STENOSIS; GENE EXPRESSION; GENOTYPE PHENOTYPE CORRELATION; HIRSCHSPRUNG DISEASE; HUMAN; MENTAL DEFICIENCY; MICROARRAY ANALYSIS; MYELOPROLIFERATIVE DISORDER; NONHUMAN; NUCLEAR REPROGRAMMING; PHENOTYPE; PLURIPOTENT STEM CELL; SCHIZOPHRENIA; TRISOMY;

EID: 84875090534     PISSN: 21576564     EISSN: 21576580     Source Type: Journal    
DOI: 10.5966/sctm.2012-0117     Document Type: Article

References (145)

1. Parker SE, Mai CT, Canfield MA et al. Updated National Birth Prevalence estimates for selected birth defects in the United States, 2004-2006. Birth Defects Res A Clin Mol Teratol 2010;88:1008-1016
2. Yoon PW, Freeman SB, Sherman SL et al. Advanced maternal age and the risk of Down syndrome characterized by the meiotic stage of chromosomal error: A population-based study. Am J Hum Genet 1996;58:628-633
3. Hassold T, Hall H, Hunt P. The origin of human aneuploidy: Where we have been, where we are going. Hum Mol Genet 2007;16 Spec No. 2:R203-R208
4. Petersen MB, Mikkelsen M. Nondisjunction in trisomy 21: Origin and mechanisms. Cytogenet Cell Genet 2000;91:199-203
5. Korenberg JR, Chen XN, Schipper R et al. Down syndrome phenotypes: The consequences of chromosomal imbalance. Proc Natl Acad Sci USA 1994;91:4997-5001
6. Delabar JM, Theophile D, Rahmani Z et al. Molecular mapping of twenty-four features of Down syndrome on chromosome 21. Eur J Hum Genet 1993;1:114-124
7. Epstein CJ, Avraham KB, Lovett M et al. Transgenic mice with increased Cu/Zn-superoxide dismutase activity: Animal model of dosage effects in Down syndrome. Proc Natl Acad Sci USA 1987;84:8044-8048
8. Smith DJ, Stevens ME, Sudanagunta SP et al. Functional screening of 2 Mb of human chromosome21q22.2 in transgenic mice implicates minibrain in learning defects associated with Down syndrome. Nat Genet 1997;16:28-36
9. Niebuhr E. Down's syndrome. The possibility of a pathogenetic segment on chromosome no. 21. Humangenetik 1974;21:99-101
10. McCormick MK, Schinzel A, Petersen MB et al. Molecular genetic approach to the characterization of the "Down syndrome region" of chromosome 21. Genomics 1989;5:325-331
11. Korenberg JR. Molecular mapping of the Down syndrome phenotype. Prog Clin Biol Res 1990;360:105-115
12. Olson LE, Richtsmeier JT, Leszl J et al. A chromosome 21 critical region does not cause specific Down syndrome phenotypes. Science 2004;306:687-690
13. Korbel JO, Tirosh-Wagner T, Urban AE et al. The genetic architecture of Down syndrome phenotypes revealed by high-resolution analysis of human segmental trisomies. Proc Natl Acad Sci USA 2009;106:12031-12036
14. Lyle R, Bena F, Gagos S et al. Genotypephenotype correlations in Down syndrome identified by array CGH in 30 cases of partial trisomy and partial monosomy chromosome 21. Eur J Hum Genet 2009;17:454-466
15. Roper RJ, Reeves RH. Understanding the basis for Down syndrome phenotypes. PLoS Genet 2006;2:e50
16. Cataldo AM, Petanceska S, Peterhoff CM et al. App gene dosage modulates endosomal abnormalities of Alzheimer's disease in a segmental trisomy 16 mouse model of down syndrome. J Neurosci 2003;23:6788-6792
17. Rovelet-Lecrux A, Hannequin D, Raux G et al. APP locus duplication causes autosomal dominant early-onset Alzheimer disease with cerebral amyloid angiopathy. Nat Genet 2006; 38:24-26
18. Arron JR, Winslow MM, Polleri A et al. NFAT dysregulation by increased dosage of DSCR1 and DYRK1A on chromosome 21. Nature 2006;441:595-600
19. Baek KH, Zaslavsky A, Lynch RC et al. Down's syndrome suppression of tumour growth and the role of the calcineurin inhibitor DSCR1. Nature 2009;459:1126-1130
20. Schadt EE. Molecular networks as sensors and drivers of common human diseases. Nature 2009;461:218-223
21. Loscalzo J, Kohane I, Barabasi AL. Human disease classification in the postgenomic era: A complex systems approach to human pathobiology. Mol Syst Biol 2007;3:124
22. Schadt EE, Lum PY. Thematic review series: Systems biology approaches to metabolic and cardiovascular disorders. Reverse engineering gene networks to identify key drivers of complex disease phenotypes. J Lipid Res 2006;47:2601-2613
23. Eichler EE, Flint J, Gibson G et al. Missing heritability and strategies for finding the underlying causes of complex disease. Nat Rev Genet 2010;11:446-450
24. Mao R, Zielke CL, Zielke HR et al. Global up-regulation of chromosome 21 gene expression in the developing Down syndrome brain. Genomics 2003;81:457-467
25. FitzPatrick DR, Ramsay J, McGill NI et al. Transcriptome analysis of human autosomal trisomy. Hum Mol Genet 2002;11:3249-3256
26. Lockstone HE, Harris LW, Swatton JE et al. Gene expression profiling in the adult Down syndrome brain. Genomics 2007;90:647-660
27. Aït Yahya-Graison E, Aubert J, Dauphinot L et al. Classification of human chromosome 21 gene-expression variations in Down syndrome: Impact on disease phenotypes. Am J Hum Genet 2007;81:475-491
28. Kahlem P, Sultan M, Herwig R et al. Transcript level alterations reflect gene dosage effects across multiple tissues in a mouse model of down syndrome. Genome Res 2004;14: 1258-1267
29. Mao R, Wang X, Spitznagel EL, Jr., et al. Primary and secondary transcriptional effects in the developing human Down syndrome brain and heart. Genome Biol 2005;6:R107
30. Antonarakis SE, Lyle R, Dermitzakis ET et al. Chromosome 21 and down syndrome: From genomics to pathophysiology. Nat Rev Genet 2004;5:725-738
31. Saran NG, Pletcher MT, Natale JE et al. Global disruption of the cerebellar transcriptome in a Down syndrome mouse model. Hum Mol Genet 2003;12:2013-2019
32. Haydar TF, Reeves RH. Trisomy 21 and early brain development. Trends Neurosci 2012;35:81-91
33. Reeves RH. Down syndrome mouse models are looking up. Trends Mol Med 2006; 12:237-240
34. Devoy A, Bunton-Stasyshyn RK, Tybulewicz VL et al. Genomically humanized mice: Technologies and promises. Nat Rev Genet 2011;13:14-20
35. Watase K, Zoghbi HY. Modelling brain diseases in mice: The challenges of design and analysis. Nat Rev Genet 2003;4:296-307
36. Yu T, Li Z, Jia Z et al. A mouse model of Down syndrome trisomic for all human chromosome 21 syntenic regions. Hum Mol Genet 2010;19:2780-2791
37. O'Doherty A, Ruf S, Mulligan C et al. An aneuploid mouse strain carrying human chromosome 21 with Down syndrome phenotypes. Science 2005;309:2033-2037
38. Gardiner K, Fortna A, Bechtel L et al. Mouse models of Down syndrome: How useful can they be? Comparison of the gene content of human chromosome 21 with orthologous mouse genomic regions. Gene 2003;318:137-147
39. Waterston RH, Lindblad-Toh K, Birney E et al. Initial sequencing and comparative analysis of the mouse genome. Nature 2002;420: 520-562
40. Zhu H, Lensch MW, Cahan P et al. Investigating monogenic and complex diseases with pluripotent stem cells. Nat Rev Genet 2011;12: 266-275
41. Grskovic M, Javaherian A, Strulovici B et al. Induced pluripotent stem cells: Opportunities for disease modelling and drug discovery. Nat Rev Drug Discov 2011;10:915-929
42. Irion S, Nostro MC, Kattman SJ et al. Directed differentiation of pluripotent stem cells: From developmental biology to therapeutic applications. Cold Spring Harb Symp Quant Biol 2008;73:101-110
43. Laslett AL, Grimmond S, Gardiner B et al. Transcriptional analysis of early lineage commitment in human embryonic stem cells. BMC Dev Biol 2007;7:12
44. Park IH, Arora N, Huo H et al. Diseasespecific induced pluripotent stem cells. Cell 2008;134:877-886
45. Marchetto MC, Carromeu C, Acab A et al. A model for neural development and treatment of Rett syndrome using human induced pluripotent stem cells. Cell 2010;143:527-539
46. Kim KY, Hysolli E, Park IH. Neuronal maturation defect in induced pluripotent stem cells from patients with Rett syndrome. Proc Natl Acad Sci USA 2011;108:14169-14174
47. Chailangkarn T, Acab A, Muotri AR. Modeling neurodevelopmental disorders using human neurons. Curr Opin Neurobiol 2012;22: 785-790
48. Muotri AR, Marchetto MC, Coufal NG et al. L1 retrotransposition in neurons is modulated by MeCP2. Nature 2010;468:443-446
49. Briggs JA, Sun J, Shepherd J et al. Integration- free induced pluripotent stem cells model genetic and neural developmental features of Down syndrome etiology. STEM CELLS 2013. (in press).
50. Shi Y, Kirwan P, Smith J et al. A human stem cell model of early Alzheimer's disease pathology in Down syndrome. Sci Transl Med 2012;4:124-129
51. Brennand KJ, Simone A, Jou J et al. Modelling schizophrenia using human induced pluripotent stem cells. Nature 2011;473:221-225
52. Osafune K, Caron L, Borowiak M et al. Marked differences in differentiation propensity among human embryonic stem cell lines. Nat Biotechnol 2008;26:313-315
53. Lister R, Pelizzola M, Kida YS et al. Hotspots of aberrant epigenomic reprogramming in human induced pluripotent stem cells. Nature 2011;471:68-73
54. Ohm JE, Mali P, Van Neste L et al. Cancerrelated epigenome changes associated with reprogramming to induced pluripotent stem cells. Cancer Res 2010;70:7662-7673
55. Miura K, Okada Y, Aoi T et al. Variation in the safety of induced pluripotent stem cell lines. Nat Biotechnol 2009;27:743-745
56. Hussein SM, Batada NN, Vuoristo S et al. Copy number variation and selection during reprogramming to pluripotency. Nature 2011; 471:58-62
57. Laurent LC, Ulitsky I, Slavin I et al. Dynamic changes in the copy number of pluripotency and cell proliferation genes in human ESCs and iPSCs during reprogramming and time in culture. Cell Stem Cell 2011;8:106-118
58. Polo JM, Liu S, Figueroa ME et al. Cell type of origin influences the molecular and functional properties of mouse induced pluripotent stem cells. Nat Biotechnol 2010;28: 848-855
59. Bar-Nur O, Russ HA, Efrat S et al. Epigenetic memory and preferential lineage-specific differentiation in induced pluripotent stem cells derived from human pancreatic islet beta cells. Cell Stem Cell 2011;9:17-23
60. Ohi Y, Qin H, Hong C et al. Incomplete DNA methylation underlies a transcriptional memory of somatic cells in human iPS cells. Nat Cell Biol 2011;13:541-549
61. Kim K, Doi A, Wen B et al. Epigenetic memory in induced pluripotent stem cells. Nature 2010;467:285-290
62. Cheng L, Hansen NF, Zhao L et al. Low incidence of DNA sequence variation in human induced pluripotent stem cells generated by nonintegrating plasmid expression. Cell Stem Cell 2012;10:337-344
63. Schena M, Shalon D, Davis RW et al. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 1995;270:467-470
64. Eisen MB, Spellman PT, Brown PO et al. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 1998;95:14863-14868
65. Wang Z, Gerstein M, Snyder M. RNA-Seq: A revolutionary tool for transcriptomics. Nat Rev Genet 2009;10:57-63
66. Mortazavi A, Williams BA, McCue K et al. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 2008;5: 621-628
67. Kuhn DE, Nuovo GJ, Terry AV, Jr., et al. Chromosome 21-derived microRNAs provide an etiological basis for aberrant protein expression in human Down syndrome brains. J Biol Chem 2010;285:1529-1543
68. Dauphinot L, Lyle R, Rivals I et al. The cerebellar transcriptome during postnatal development of the Ts1Cje mouse, a segmental trisomy model for Down syndrome. Hum Mol Genet 2005;14:373-384
69. Ramanan VK, Shen L, Moore JH et al. Pathway analysis of genomic data: Concepts, methods, and prospects for future development. Trends Genet 2012;28:323-332
70. Subramanian A, Tamayo P, Mootha VK et al. Gene set enrichment analysis: A knowledgebased approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 2005;102:15545-15550
71. Duke DC, Moran LB, Kalaitzakis ME et al. Transcriptome analysis reveals link between proteasomal and mitochondrial pathways in Parkinson's disease. Neurogenetics 2006;7: 139-148
72. Sutherland GT, Matigian NA, Chalk AM. et al. A cross-study transcriptional analysis of Parkinson's disease. PLoS One 2009;4:e4955
73. Mar JC, Matigian NA, Quackenbush J et al. attract: A method for identifying core pathways that define cellular phenotypes. PLoS One 2011;6:e25445
74. Mar JC, Matigian NA, Mackay-Sim A et al. Variance of gene expression identifies altered network constraints in neurological disease. PLoS Genet 2011;7:e1002207
75. Khatri P, Draghici S. Ontological analysis of gene expression data: Current tools, limitations, and open problems. Bioinformatics 2005;21:3587-3595
76. Venet D, Dumont JE, Detours V. Most random gene expression signatures are significantly associated with breast cancer outcome. PLoS Comput Biol 2011;7:e1002240
77. Mercer TR, Dinger ME, Mattick JS. Long non-coding RNAs: Insights into functions. Nat Rev Genet 2009;10:155-159
78. Mattick JS, Makunin IV. Non-coding RNA. Hum Mol Genet 2006;15 Spec No 1:R17-R29
79. Tompa M, Li N, Bailey TL et al. Assessing computational tools for the discovery of transcription factor binding sites. Nat Biotechnol 2005;23:137-144
80. Laird PW. Principles and challenges of genomewide DNA methylation analysis. Nat Rev Genet 2010;11:191-203
81. Schones DE, Zhao K. Genome-wide approaches to studying chromatin modifications. Nat Rev Genet 2008;9:179-191
82. Meissner A, Mikkelsen TS, Gu H et al. Genome-scale DNA methylation maps of pluripotent and differentiated cells. Nature 2008; 454:766-770
83. Mikkelsen TS, Ku M, Jaffe DB et al. Genome- wide maps of chromatin state in pluripotent and lineage-committed cells. Nature 2007;448:553-560
84. Luo D, Saltzman WM. Synthetic DNA delivery systems. Nat Biotechnol 2000;18:33-37
85. Rubinson DA, Dillon CP, Kwiatkowski AV et al. A lentivirus-based system to functionally silence genes in primary mammalian cells, stem cells and transgenic mice by RNA interference. Nat Genet 2003;33:401-406
86. Mäe M, Langel U. Cell-penetrating peptides as vectors for peptide, protein and oligonucleotide delivery. Curr Opin Pharmacol 2006;6:509-514
87. Chu TC, Twu KY, Ellington AD et al. Aptamer mediated siRNA delivery. Nucleic Acids Res 2006;34:e73
88. Shen C, Buck AK, Liu X et al. Gene silencing by adenovirus-delivered siRNA. FEBS Lett 2003;539:111-114
89. Saez E, No D, West A et al. Inducible gene expression in mammalian cells and transgenic mice. Curr Opin Biotechnol 1997;8:608-616
90. Sanjana NE, Cong L, Zhou Y et al. A transcription activator-like effector toolbox for genome engineering. Nat Protoc 2012;7:171-192
91. Bogdanove AJ, Voytas DF. TAL effectors: Customizable proteins for DNA targeting. Science 2011;333:1843-1846
92. Khalil AS, Collins JJ. Synthetic biology: Applications come of age. Nat Rev Genet 2010; 11:367-379
93. Culler SJ, Hoff KG, Smolke CD. Reprogramming cellular behavior with RNA controllers responsive to endogenous proteins. Science 2010;330:1251-1255
94. Lamb J, Crawford ED, Peck D et al. The Connectivity Map: Using gene-expression signatures to connect small molecules, genes, and disease. Science 2006;313:1929-1935
95. Park PJ. ChIP-seq: Advantages and challenges of a maturing technology. Nat Rev Genet 2009;10:669-680
96. Valouev A, Johnson DS, Sundquist A et al. Genome-wide analysis of transcription factor binding sites based on ChIP-Seq data. Nat Methods 2008;5:829-834
97. Chu C, Qu K, Zhong FL et al. Genomic maps of long noncoding RNA occupancy reveal principles of RNA-chromatin interactions. Mol Cell 2011;44:667-678
98. Simon MD, Wang CI, Kharchenko PV et al. The genomic binding sites of a noncoding RNA. Proc Natl Acad Sci USA 2011;108:20497-20502
99. Hockemeyer D, Wang H, Kiani S et al. Genetic engineering of human pluripotent cells using TALE nucleases. Nat Biotechnol 2011;29:731-734
100. Ramírez-Solis R, Liu P, Bradley A. Chromosome engineering in mice. Nature 1995; 378:720-724
101. van der Weyden L, Shaw-Smith C, Bradley A. Chromosome engineering in ES cells. Methods Mol Biol 2009;530:49-77
102. Nuzhdin SV, Friesen ML, McIntyre LM. Genotype-phenotype mapping in a post-GWAS world. Trends Genet 2012;28:421-426
103. Lee D, Redfern O, Orengo C. Predicting protein function from sequence and structure. Nat Rev Mol Cell Biol 2007;8:995-1005
104. Ghaemmaghami S, Huh WK, Bower K et al. Global analysis of protein expression in yeast. Nature 2003;425:737-741
105. Huh WK, Falvo JV, Gerke LC et al. Global analysis of protein localization in budding yeast. Nature 2003;425:686-691
106. Kislinger T, Cox B, Kannan A et al. Global survey of organ and organelle protein expression in mouse: Combined proteomic and transcriptomic profiling. Cell 2006;125:173-186
107. Herrgård MJ, Swainston N, Dobson P et al. A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. Nat Biotechnol 2008;26:1155-1160
108. Burgard AP, Nikolaev EV, Schilling CH et al. Flux coupling analysis of genome-scale metabolic network reconstructions. Genome Res 2004;14:301-312
109. Sacco F, Gherardini PF, Paoluzi S et al. Mapping the human phosphatome on growth pathways. Mol Syst Biol 2012;8:603
110. Minguez P, Parca L, Diella F et al. Deciphering a global network of functionally associated post-translational modifications. Mol Syst Biol 2012;8:599
111. Yang B, Wu YJ, Zhu M et al. Identification of cross-linked peptides from complex samples. Nat Methods 2012; 9: 904-906.
112. Walzthoeni T, Claassen M, Leitner A et al. False discovery rate estimation for crosslinked peptides identified by mass spectrometry. Nat Methods 2012;9:901-903
113. Kristensen AR, Gsponer J, Foster LJ. A high-throughput approach for measuring temporal changes in the interactome. Nat Methods 2012;9:907-909
114. Tkach JM, Yimit A, Lee AY et al. Dissecting DNA damage response pathways by analysing protein localization and abundance changes during DNA replication stress. Nat Cell Biol 2012;14:966-976
115. Pontén F, Gry M, Fagerberg L et al. A global view of protein expression in human cells, tissues, and organs. Mol Syst Biol 2009;5: 337
116. Beck M, Schmidt A, Malmstroem J et al. The quantitative proteome of a human cell line. Mol Syst Biol 2011;7:549
117. Munoz J, Low TY, Kok YJ et al. The quantitative proteomes of human-induced pluripotent stem cells and embryonic stem cells. Mol Syst Biol 2011;7:550
118. Havugimana PC, Hart GT, Nepusz T et al. A census of human soluble protein complexes. Cell 2012;150:1068-1081
119. Rual JF, Venkatesan K, Hao T et al. Towards a proteome-scale map of the human protein-protein interaction network. Nature 2005;437:1173-1178
120. Ideker T, Krogan NJ. Differential network biology. Mol Syst Biol 2012;8:565
121. Parmigiani G, Garrett ES, Irizarry RA et al. The analysis of gene expression data: An overview of methods and software. In: Parmigiani G, Garrett ES, Irizarry RA et al., eds. The Analysis of Gene Expression Data: An Overview of Methods and Software. New York, NY: Springer, 2003:1-45
122. Churchill G. Fundamentals of experimental design for cDNA microarray. Nat Genet 2002;32:490-495
123. Slonim D. From patterns to pathways: Gene expression data analysis comes of age. Nat Genet 2002;32:502-508
124. Smyth GK. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 2004. 3:Article3
125. Tusher V, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA 2001;98:5116-5121
126. Ringner M. What is principal component analysis? Nat Biotechnol 2008;26:1-2
127. Jolliffe IT. Principal Component Analysis. New York, NY: Springer, 2002
128. Kanehisa M. The KEGG database. Novartis Found Symp 2002;247:244-252
129. Saeed AI, Bhagabati NK, Braisted JC et al. TM4 microarray software suite. Methods Enzymol 2006;411:134-193
130. Saeed AI, Sharov V, White J et al. TM4: A free, open-source system for microarray data management and analysis. Biotechniques 2003;34:374-378
131. Reich M, Liefeld T, Gould J et al. GenePattern 2.0. Nat Genet 2006;38:500-501
132. Du P, Kibbe WA, Lin SM. lumi: A pipeline for processing Illumina microarray. Bioinformatics 2008;24:1547-1548
133. Sturn A, Quackenbush J, Trajanoski Z. Genesis: Cluster analysis of microarray data. Bioinformatics 2002;18:207-208
134. Ulitsky I, Shamir R. Identification of functional modules using network topology and high-throughput data. BMC Syst Biol 2007; 1:8
135. Doniger SW, Salomonis N, Dahlquist KD et al. MAPPFinder: Using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data. Genome Biol 2003;4:R7
136. Salomonis, Hanspers K, Zambon A et al. GENMAPP 2: New features and resources for pathway analysis. BMC Bioinformatics 2007;8: 217
137. Dennis G, Jr., Sherman BT, Hosack DA et al. DAVID: Database for Annotation, Visualisation, and Integrated Discovery. Genome Biol 2003;4:P3
138. Thomas PD, Campbell MJ, Kejariwal A et al. PANTHER: A library of protein families and subfamilies indexed by function. Genome Res 2003;13:2129-2141
139. Shannon P, Markiel A, Ozier O et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res 2003;13:2498-2504
140. Yeung N, Cline MS, Kuchinsky A et al. Exploring biological networks with Cytoscape software. Curr Protoc Bioinformatics 2008. Chapter 8:Unit 8.13
141. Wu LF, Hughes TR, Davierwala AP et al. Large-scale prediction of Saccharomyces cerevisiae gene function using overlapping transcriptional clusters. Nat Genet 2002;31: 255-265
142. Allocco DJ, Kohane IS, Butte AJ. Quantifying the relationship between co-expression, co-regulation and gene function. BMC Bioinformatics 2004;5:18
143. Golub TR, Slonim DK, Tamayo P et al. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 1999;286:531-537
144. Storey JD, Tibshirani R. SAM thresholding and false discovery rates for detecting differential gene expression in DNA microarrays. In: Parmigiani G, Garrett ES, Irizarry RA et al., eds. The Analysis of Gene Expression Data: Methods and Software. New York, NY: Springer, 2003:272-290
145. MAQCConsortium; Shi L, Reid LH, Jones WD et al. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat Biotechnol 2006;24:1151-1162.