Welcome
Abstract
Acknowledgements
Preface
1
Introduction
1.1
A grammar for genomic data analysis
1.2
Integration of genomic data structures
1.3
Representation of genomic data structures
1.4
Interactive visualisation for high-dimensional data
2
plyranges: a grammar of data transformation for genomics
2.1
Background
2.2
Results
2.2.1
Genomic Relational Algebra
2.2.2
Developing workflows with
plyranges
2.3
Discussion
2.4
Conclusion
2.5
Availability of Data and Materials
Acknowledgements
3
Fluent genomics with
plyranges
and
tximeta
3.1
Introduction
3.1.1
Experimental Data
3.2
Import Data as a
SummarizedExperiment
3.2.1
Using
tximeta
to import RNA-seq quantification data
3.2.2
Importing ATAC-seq data as a
SummarizedExperiment
object
3.3
Model assays
3.3.1
RNA-seq differential gene expression analysis
3.3.2
ATAC-seq peak differential abundance analysis
3.4
Integrate ranges
3.4.1
Finding overlaps with
plyranges
3.4.2
Down sampling non-differentially expressed genes
3.4.3
Expanding genomic coordinates around the transcription start site
3.4.4
Use overlap joins to find relative enrichment
3.5
Discussion
3.6
Software Availability
Acknowledgements
4
Exploratory coverage analysis with superintronic and plyranges
4.1
Introduction
4.2
Methods
4.2.1
Representation of coverage estimation
4.2.2
Integration of external annotations
4.2.3
Discovery of regions of interest via ‘data descriptors’
4.3
A workflow for uncovering intron retention in a zebrafish experiment
4.4
Discussion
Acknowledgements
5
Casting multiple shadows: high-dimensional interactive data visualisation with tours and embeddings
5.1
Introduction
5.2
Overview of Dimension Reduction
5.2.1
Tours explore the subspace of
\(d\)
-dimensional projections
5.3
Visual Design
5.3.1
Finding Gestalt: focus and context
5.3.2
Posing Queries: multiple views, many contexts
5.3.3
Making comparisons: revising embeddings
5.4
Software Infrastructure
5.4.1
Tours as a streaming data problem
5.4.2
Linking and highlighting views via interactions
5.5
Case Studies
5.5.1
Case Study 1: Exploring spherical Gaussian clusters
5.5.2
Case Study 2: Exploring spherical Gaussian clusters with hierarchical structure
5.5.3
Case Study 3: Exploring data with piecewise linear structure
5.5.4
Case Study 4: Clustering single cell RNA-seq data
5.6
Discussion
Acknowledgements
Supplementary Materials
6
Conclusion
6.1
Software Development
6.2
Further Work
Appendix
A
Getting started with the
plyranges
package
A.1
Ranges
revisited
A.2
Constructing
Ranges
A.3
Arithmetic on Ranges
A.4
Grouping
Ranges
A.5
Restricting
Ranges
A.6
Summarising
Ranges
A.7
Joins, or another way at looking at overlaps between
Ranges
A.8
Grouping by overlaps
A.9
Reading Genomic Files
A.10
Learning more
Bibliography
Proudly published with bookdown
Fluent statistical computing interfaces for biological data analysis
A
Getting started with the
plyranges
package