Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

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5.3 Differential Gene Expression Methods "scale" method in the package limma [466]. Replicate arrays were averaged and the genome (GRCh37) segmented into regions with different copy number using the circular binary segmentation algorithm in the Bioconductor package DNAcopy [510], with the option undo.SD set to 1. Aberrations were called using the package CGHcall [503] [37] with the option nclass set to 4. CGH data are available from ArrayExpress [28] under accession E-MTAB-972. 5.3 Differential Gene Expression We called the differentially expressed genes with the Bioconductor package DESeq that uses pseudo reference-based normalization [21]. DESeq compares sequencing-derived expression pr<strong>of</strong>iles using a method that is able to account for large variation between biological replicates, as can be expected with cancer samples. The R code that generated the differential expression calls is reported below, with ## signs indicating a comment: ## Define auxiliary functions read.gns.counts
5.4 Quantitative Real Time-PCR Validation Methods resVarB=de$resVarB, counts) rownames(x)
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Transcriptional Characterization of
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This dissertation is my own work an
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Acknowledgements I would like to th
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5.9 External Dataset Expression Cor
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Introduction 1
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1.1 Glial Cells in the Central Nerv
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1.2 Glioblastoma Multiforme Introdu
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1.3 Primary and Secondary Glioblast
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1.4 Pathways Involved in Glioblasto
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1.5 Pathway Crosstalk Introduction
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Chapter 2 Neurogenesis Contents 2.1
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2.1 Radial Glia Introduction VZ adj
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2.2 Neural Stem Cells Introduction
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Page 57 and 58: 2.2 Neural Stem Cells Introduction
Page 65 and 66: Chapter 3 Brain Cancer Stem Cells C
Page 67 and 68: 3.1 The Cancer Stem Cell Hypothesis
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Page 103 and 104: 5.1 Tag-sequencing Data Processing
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6.7 Isoform Differential Expression
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6.8 Long ncRNA Differential Express
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6.8 Long ncRNA Differential Express
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Chapter 7 Dataset Correlation Analy
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7.1 Enrichment Analysis Results Tab
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7.1 Enrichment Analysis Results Fig
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7.1 Enrichment Analysis Results Tab
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7.2 Glioblastoma Expression Signatu
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7.3 Tumour Expression Correlation R
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7.4 Survival Analysis Results Table
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7.4 Survival Analysis Results 867 g
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7.5 Glioblastoma Pathway Analysis R
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8.1 Principles Results say, this is
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8.3 Databases Results Figure 8.1: O
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8.3 Databases Results Figure 8.3: W
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8.4 Filters Results Bayesian method
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8.5 Target Prediction Ensemble Anal
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9.1. Digital Profiling of GNS Cell
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9.2. MicroRNA Target Prediction Ana
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9.3. Concluding Remarks Appendix pr
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A.1 Differential Expression Appendi
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A.2 Classified Differential Express
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A.3 Quantitative RT-PCR Appendix Ta
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A.3 Quantitative RT-PCR Appendix We
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A.3 Quantitative RT-PCR Appendix Ta
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A.3 Quantitative RT-PCR Appendix We
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A.4 Tag-seq vs qRT-PCR Correlation
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Appendix "BEX5", "DIAPH2", "GBP3",
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End Select End If End Sub Appendix
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If NameStr.ToLower().Equals("query"
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Appendix C Long ncRNAs We detected
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C. Long ncRNAs Appendix Tag Accessi
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D.1 Pathway Interactions Appendix F
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D.2 Pathway Images Appendix Figure
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D.2 Pathway Images Appendix Figure
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E. Exon Array Data Appendix Average
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F. MicroRNA Array Data Appendix Tab
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F. MicroRNA Array Data Appendix GNS
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F. MicroRNA Array Data Appendix GNS
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Ln Natural logarithm LOH Loss of He
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3.3 Schematisation of cell cycle ph
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8.3 Workflows at the core of the pr
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7.1 Selected Gene Ontology terms an
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Bibliography [1] Cell signaling tec
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