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E-raamat: Omic Association Studies with R and Bioconductor

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  • Formaat: 390 pages
  • Ilmumisaeg: 14-Jun-2019
  • Kirjastus: CRC Press
  • Keel: eng
  • ISBN-13: 9780429803369
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Omic Association Studies with R and BioconductorSuurem pilt
  • Formaat: 390 pages
  • Ilmumisaeg: 14-Jun-2019
  • Kirjastus: CRC Press
  • Keel: eng
  • ISBN-13: 9780429803369
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After the great expansion of genome-wide association studies, their scientific methodology and, notably, their data analysis has matured in recent years, and they are a keystone in large epidemiological studies. Newcomers to the field are confronted with a wealth of data, resources and methods. This book presents current methods to perform informative analyses using real and illustrative data with established bioinformatics tools and guides the reader through the use of publicly available data. Includes clear, readable programming codes for readers to reproduce and adapt to their own data.











Emphasises extracting biologically meaningful associations between traits of interest and genomic, transcriptomic and epigenomic data





Uses up-to-date methods to exploit omic data





Presents methods through specific examples and computing sessions





Supplemented by a website, including code, datasets, and solutions

Arvustused

"This book is a good tool for self-learning analytical strategies for omics data. It requires previous knowledge of R and focuses on getting things done...I think the book would be a good reference for masters or PhD students that have to perform their analysis and need a starting point. Also, for the practicing statistician working with omics data." - Victor Moreno, ISCB News, July 2020

Preface xi
1 Introduction
1(20)
1.1 Book overview
2(1)
1.2 Overview of omic data
2(6)
1.2.1 Genomic data
3(1)
1.2.1.1 Genomic SNP data
3(1)
1.2.1.2 SNP arrays
3(1)
1.2.1.3 Sequencing methods
4(1)
1.2.2 Genomic data for other structural variants
5(1)
1.2.3 Transcriptomic data
5(1)
1.2.3.1 Microarrays
6(1)
1.2.3.2 RNA-seq
6(1)
1.2.4 Epigenomic data
7(1)
1.2.5 Exposomic data
8(1)
1.3 Association studies
8(4)
1.3.1 Genome-wide association studies
9(1)
1.3.2 Whole transcriptome profiling
10(1)
1.3.3 Epigenome-wide association studies
10(1)
1.3.4 Exposome-wide association studies
11(1)
1.4 Publicly available resources
12(4)
1.4.1 dbGaP
12(1)
1.4.2 EGA
13(1)
1.4.3 GEO
13(1)
1.4.4 1000 Genomes
14(1)
1.4.5 GTEx
14(1)
1.4.6 TCGA
15(1)
1.4.7 Others
15(1)
1.5 Bioconductor
16(2)
1.5.1 R
16(1)
1.5.2 Omic data in Bioconductor
17(1)
1.6 Book's outline
18(3)
2 Case examples
21(28)
2.1
Chapter overview
21(1)
2.2 Reproducibility: The case for public data repositories
21(1)
2.3 Case 1: dbGaP
22(8)
2.4 Case 2: GEO
30(7)
2.5 Case 3: GTEx
37(3)
2.6 Case 4: TCGA
40(5)
2.7 Case 5: NHANES
45(4)
3 Dealing with omic data in Bioconductor
49(26)
3.1
Chapter overview
49(1)
3.2 snpMatrix
50(2)
3.3 ExpressionSet
52(1)
3.4 SummarizedExperirnent
53(2)
3.5 GRanges
55(7)
3.6 RangedSummarizedExperiment
62(2)
3.7 ExposomeSet
64(3)
3.8 MultiAssayExperiment
67(2)
3.9 MultiDataSet
69(6)
4 Genetic association studies
75(58)
4.1
Chapter overview
75(1)
4.2 Genetic association studies
76(18)
4.2.1 Analysis packages
76(1)
4.2.2 Association tests
77(2)
4.2.3 Single SNP analysis
79(4)
4.2.4 Hardy-Weinberg equilibrium
83(1)
4.2.5 SNP association analysis
84(7)
4.2.6 Gene × environment and gene × gene interactions
91(3)
4.3 Haplotype analysis
94(8)
4.3.1 Linkage disequilibrium heatmap plots
95(3)
4.3.2 Haplotype estimation
98(1)
4.3.3 Haplotype association
98(1)
4.3.4 Sliding window approach
99(3)
4.4 Genetic score
102(5)
4.5 Genome-wide association studies
107(19)
4.5.1 Quality control of SNPs
109(1)
4.5.2 Quality control of individuals
110(6)
4.5.3 Population ancestry
116(1)
4.5.4 Genome-wide association analysis
117(2)
4.5.5 Adjusting for population stratification
119(7)
4.6 Post-GWAS visualization and interpretation
126(7)
4.6.1 Genome-wide associations for imputed data
129(4)
5 Genomic variant studies
133(66)
5.1
Chapter overview
133(1)
5.2 Copy number variants
134(19)
5.2.1 CNV calling
136(17)
5.3 Single CNV association
153(16)
5.3.1 Inferring copy number status from signal data
157(4)
5.3.2 Measuring uncertainty of CNV calling
161(1)
5.3.3 Assessing the association between CNVs and traits
161(1)
5.3.3.1 Modeling association
162(2)
5.3.3.2 Global test of associations
164(2)
5.3.4 Whole genome CNV analysis
166(3)
5.4 Genetic mosaicisms
169(19)
5.4.1 Calling genetic mosaicisms
169(9)
5.4.2 Calling the loss of chromosome Y
178(10)
5.5 Polymorphic inversions
188(11)
5.5.1 Inversion detection
189(2)
5.5.2 Inversion calling
191(4)
5.5.3 Inversion association
195(4)
6 Addressing batch effects
199(12)
6.1
Chapter overview
199(1)
6.2 SVA
200(6)
6.3 ComBat
206(5)
7 Transcriptomic studies
211(34)
7.1
Chapter overview
211(1)
7.2 Microarray data
212(13)
7.2.1 Normalization
212(4)
7.2.2 Filter
216(4)
7.2.3 Differential expression
220(5)
7.3 Next generation sequencing data
225(20)
7.3.1 Normalization
229(6)
7.3.2 Gene filtering
235(1)
7.3.3 Differential expression
235(10)
8 Epigenomic studies
245(18)
8.1
Chapter overview
245(1)
8.2 Epigenome-wide association studies
245(1)
8.3 Methylation arrays
246(1)
8.4 Differential methylation analysis
247(7)
8.5 Methylation analysis of a target region
254(3)
8.6 Epigenomic and transcriptomic visualization results
257(3)
8.7 Cell proportion estimation
260(3)
9 Exposomic studies
263(28)
9.1
Chapter overview
263(1)
9.2 The exposome
264(2)
9.2.1 Exposomic data
265(1)
9.3 Exposome characterization
266(9)
9.4 Exposome-wide association analyses
275(3)
9.5 Association between exposomic and other omic data
278(13)
9.5.1 Exposome-transcriptome data analysis
279(8)
9.5.2 Exposome-methylome data analysis
287(4)
10 Enrichment analysis
291(24)
10.1
Chapter overview
291(1)
10.2 Enrichment analysis and statistical power
292(1)
10.3 Gene set annotations
293(3)
10.4 Over representation analysis
296(11)
10.5 Overlap with functional genomic regions
307(3)
10.6 Chemical and environmental enrichment
310(5)
11 Multiomic data analysis
315(38)
11.1
Chapter overview
315(1)
11.2 Multiomic data
316(1)
11.3 Massive pair-wise analyses between omic datasets
316(6)
11.4 Multiple-omic data integration
322(31)
11.4.1 Multi-staged analysis
323(1)
11.4.1.1 Genomic variation analysis
323(5)
11.4.2 Domain-knowledge approach
328(4)
11.4.3 Meta-dimensional analysis
332(1)
11.4.3.1 Principal component analysis
332(4)
11.4.3.2 Sparse principal component analysis
336(3)
11.4.3.3 Canonical correlation and coinertia analyses
339(6)
11.4.3.4 Regularized generalized canonical correlation
345(8)
Bibliography 353(18)
Index 371
Juan R. González is an Associate Research Professor leading the Bioinformatics Research Group in Epidemiology at Barcelona Institute for Global Health. He has published extensively on methods and bioinformatics tools to detect structural variants from genomic data and to perform different types of omic association studies. Dr. González is the author of a large number of R and Bioconductor packages including state-of-the-art libraries such as SNPassoc or MAD that have been used to discover new susceptibility genetic factor for complex diseases.

Alejandro Caceres is a Senior Statistician in the Bioinformatics Research Group in Epidemiology at Barcelona Institute for Global Health. He has large experience in developing new statistical methods to exploit genomic, transcriptomic and epigenomic data obtained from public repositories. Dr. Cáceres is the author of several R and Bioconductor packages that have been used, for instance, to study the role of polymorphic genomic inversions in complex diseases or to investigate how the downregulation of chromosome Y may affect age-related diseases.
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