Scalable approaches for analysis of human genome-wide ...

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PrefaceThis thesis incorporates the following publications:• Chapter 4 is substantially based on: G. Abraham, A. Kowalczyk, S. Loi, I. Haviv,and J. Zobel. Prediction of breast cancer prognosis using gene set statistics providessignature stability and biological context. BMC Bioinfo., 11:277, 2010. (primaryauthor: G. Abraham).• Chapter 5 is substantially based on: G. Abraham, A. Kowalczyk, J. Zobel, and M. Inouye.SparSNP: Fast and memory-efficient analysis of all SNPs for phenotype prediction.BMC Bioinfo., 13:88, 2012 (primary author: G. Abraham).• Chapters 6 is substantially based on: G. Abraham, A. Kowalczyk, J. Zobel, and M. Inouye.Sparse linear models to explain phenotypic variance and predict complex disease.in “NIPS Personalized Medicine Workshop 2011”, December 16th, 2011, Granada,Spain. (primary author: G. Abraham).• Chapter 6 is partly based on: G. Abraham, A. Kowalczyk, J. Zobel, and M. Inouye.Sparse linear models to explain phenotypic variance and predict complex disease (expandedjournal version). 2012. Under peer review. (primary author: G. Abraham).vii
Page 1: Scalable Approaches for Analysis of
Page 5: DeclarationThis is to certify that1
Page 10 and 11: Government NHMRC IRIIS. Michael Ino
Page 13 and 14: Contents5.6. Software Features . .
Page 15 and 16: List of Figures2.1. The Central Dog
Page 17 and 18: List of Figures4.4. Variance and 95
Page 19 and 20: List of Figures7.3. R 2 for regress
Page 21 and 22: List of Figures8.12. The true non-z
Page 23 and 24: List of FiguresB.11.Summary plots o
Page 25 and 26: List of Tables4.1. Clinical and dem
Page 27: List of TablesB.1. The confusion ma
Page 31 and 32: 1IntroductionThe development of hig
Page 33 and 34: ability but is difficult to interpr
Page 35 and 36: • We show that the top predictive
Page 37: SummaryIn summary, this thesis is c
Page 40 and 41: 2. Biological BackgroundDNAmRNAprot
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2. Biological Background• Preproc
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2. Biological BackgroundType 1 Erro
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2. Biological Background• Non-ran
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2. Biological Backgroundheterogenei
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2. Biological Backgroundobesity and
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3. Review of the Analysis of Gene E
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3.4. Feature Selection — Finding
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3.4. Feature Selection — Finding
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3.5. Feature Selection and Multivar
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4Prediction of Breast Cancer Progno
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4.1. Introductionof the plot is mos
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4.1. Introductionmight be more in a
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4.2. Methodsprobesets with close to
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4.2. Methodsto the data, and by red
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4.2. Methods(2/3 and 1/3 of the dat
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4.2. Methodsstandard error of the s
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4.2. MethodsStatisticEquationSet ce
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4.3. Results0.70●●●● ●
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4.3. Results0.012Var(AUC)0.0100.008
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4.3. ResultsCorrelation0.60.40.20.0
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4.3. Results1 GNF2 MKI67 C4 −1 Ne
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4.3. ResultsFeatures in ≥ 5 datas
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4.3. Resultsfunction (1 if the argu
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4.3. ResultsClass < 5 years ≥ 5 y
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4.3. ResultsHerceptin), whereas ER+
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4.3. ResultsThese results show that
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4.3. ResultsClassifier # MSigDB set
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4.4. Summarynot considered when bui
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4.4. Summaryown average expression
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5. Fast and Memory-Efficient Sparse
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6. Sparse Linear Models Explain Phe
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7Characterising the Genetic Control
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7.2. Methods• to link the genetic
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7.2. Methods• Concentrations of 1
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7.2. Methodsreduce the degree of ov
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7.2. MethodsModelMarginalindependen
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7.3. ResultsR 20.350 0.355 0.360 0.
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7.3. Resultswith low R 2 . For both
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7.3. Resultshierarchical clustering
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7.3. Results0.15●R 20.100.05●
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7.3. ResultsR 20.70.60.50.40.30.20.
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7.3. Resultswith FG levels of 3.6-6
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7.3. Resultsalanine in the models o
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7.3. Resultsrs68527480.1ILMN_186713
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7.4. DiscussionProbe Gene λILMN 23
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7.4. Discussionattempt to infer cau
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8. Fused Multitask Penalised Regres
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●●●●●8.5. ResultsROCPRCSe
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8.5. ResultsTrue weightsFMPR−w1FM
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8.5. ResultsLassoFMPR−w2Variables
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8.6. DiscussionLinux. Overall, fmpr
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9ConclusionsA central theme of this
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weak effects which may exist. As da
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are with respect to the samples and
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ASupplementary Results for Gene Set
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A.3. External Validation0.80.80.6
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A.3. External Validationpamrpamr0.8
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A.3. External Validationvv2vv20.80.
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A.3. External Validationset.centroi
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A.3. External ValidationEstimate St
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B. Supplementary Results for Sparse
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B.2. Real Data10 10 10 10 9 8 7 7 7
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B.2. Real Data0.9AUC0.80.70.6Datase
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B.2. Real DataT1DSpecificity0.0 0.2
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B.2. Real DataCD1_stringentSpecific
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B.2. Real DataCD2_stringentSpecific
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B.2. Real DataAUC0.80.60.40.80.60.4
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B.2. Real DataFigure B.17.: Princip
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B.3. Results for each datasetB.3.1.
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Bibliography1000 Genomes Project Co
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BibliographyY. Benjamini and Y. Hoc
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BibliographyL. Chin, J. N. Andersen
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BibliographyVoight, H. M. Stringham
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BibliographyY. Freund and R. E. Sch
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BibliographyC. J. Hoggart, J. C. Wh
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BibliographyC. M. Lindgren, I. M. H
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BibliographyJ. D. Mosley and R. A.
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BibliographyE. Schneider, M. Rolli-
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BibliographyThe Wellcome Trust Case
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BibliographyS. Koduru, A. Love, F.
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BibliographyC. Yang, X. Wan, Q. Yan
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