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---
title: "Introduction to _Bioconductor_"
author: "Thomas Schwarzl adapted from Valerie Obenchain (valerie.obenchain@roswellpark.org)<br />
    Lori Shepherd (lori.shepherd@roswellpark.org)<br />
    Martin Morgan (martin.morgan@roswellpark.org)<br />
    Stanford University, Stanford, CA"
output:
    slidy_presentation: 
        theme: paper
---
```{r style, echo = FALSE, results = 'asis'}
BiocStyle::markdown()
options(width=100, max.print=1000)
knitr::opts_chunk$set(
    eval=as.logical(Sys.getenv("KNITR_EVAL", "TRUE")),
    cache=as.logical(Sys.getenv("KNITR_CACHE", "TRUE")))
```
```{r packages, eval=TRUE, echo=FALSE, warning=FALSE, message=FALSE}
suppressPackageStartupMessages({
    library(BioC2016Introduction)
    library(SummarizedExperiment)
    library(airway)
})
```
# _Bioconductor_

### Physically

- Collection of 1211 software, 916 annotation and 293 experimental
  data R packages.
- Web site (http://bioconductor.org) for package distribution and
  other resources.
- Support site (https://support.bioconductor.org) for user questions.

# _Bioconductor_

### Conceptually

- Analysis and comprehension of high throughput genomic data

# Core principles: Volume of data

### Type of research question

- Designed experiments
- Population samples
- ...

# Core principles: Volume of data

### Technological artifacts

- Differences in sequencing depth between samples
- Bias in the genomic regions sampled

# Reproducibility

- Cisplatin-resistant non-small-cell lung cancer gene sets

- Hsu et al. 2007 J Clin Oncol 25:
  [4350-4357](http://jco.ascopubs.org/content/25/28/4350.abstract)
  [retracted](http://jco.ascopubs.org/content/28/35/5229.long)

  ![](our_figures/HsuEtAl-F1-large-a.jpg)

# Reproducibility

- Baggerly & Coombes 2009 Ann Appl Stat
  [3: 1309-1334](http://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.aoas/1267453942)

  ![](our_figures/BaggerlyCoombes2009-fig2a.jpg)

# Lessons

- Record each step of the analysis
- Coordinated manipulation of feature, sample, and assay data
- Informative labels on visualizations

# How to be reproducible?

- Use software 'objects' that take care of some of the tedious
  book-keeping
- Document our analysis in scripts and 'markdown' documents

# Example: `SummarizedExperiment`

![](our_figures/SE_Description.png)

# Example: `SummarizedExperiment`

Underlying data is a matrix

- Regions of interest (e.g., genes) x samples
- `assay()` -- e.g., matrix of counts of reads overlapping genes

# Example: `SummarizedExperiment`

Include information about rows

- `rowRanges()` -- gene identifiers, or _genomic ranges_ describing
  the coordinates of each gene

# Example: `SummarizedExperiment`

Include information about columns

- `colData()` -- describing samples, experimental design, ...

# Example: `SummarizedExperiment`

```{r airway-SummarizedExperiment, eval=F}
library(airway)         # An 'ExperimentData' package...
data(airway)            # ...with a sample data set...
airway                  # ...that is a SummarizedExperiment
head(assay(airway))     # contains a matrix of counts
head(rowRanges(airway)) # information about the genes...
colData(airway)[, 1:3]  # ...and samples
## coordinated subsetting
untrt <- airway[, airway$dex == 'untrt']
head(assay(untrt))
colData(untrt)[, 1:3]
```

# Packages!

- Discover and navigate via [biocViews][]
- Package 'landing page'
  - Title, author / maintainer, short description, citation,
    installation instructions, ..., download statistics
- All user-visible functions have help pages, most with runnable
  examples
- 'Vignettes' an important feature in Bioconductor -- narrative
  documents illustrating how to use the package, with integrated code
- 'Workflows' make use of multiple packages for complete end-to-end
  analysis
- 'Release' (every six months) and 'devel' branches
- [Support site](https://support.bioconductor.org);
  [videos](https://www.youtube.com/user/bioconductor), [recent
  courses](http://bioconductor.org/help/course-materials/)

# Visualization

Inter-operability between packages made easier by using similar data structures

Examples (details later)

- `SummarizedExperiment`
- `DNAStringSet`
- `GenomicRanges`

# Comprehension is more than statistical analysis

### Annotation

- Mapping from technical to user-friendly identifiers
- Assigning genes to pathways
- Placing our results in the context of large-scale analyses

# Comprehension is more than statistical analysis

### Objects

- Represent complicated data types
- Foster interoperability
- S4 object system
  - Introspection: `methods()`, `getClass()`, `selectMethod()`
  - 'accessors' and other documented functions / methods for
    manipulation, rather than direct access to the object structure
- Interactive help
  - `method?"substr,<tab>"` to select help on methods, `class?D<tab>`
    for help on classes

# A sequence analysis package tour

![Alt Sequencing Ecosystem](our_figures/SequencingEcosystem.png)



# BiocViews 

- Bioconductor packages are listed on the [biocViews][] page. 
- tags from a controlled vocabulary

# Landing page
  
'landing page', e.g., for
[GenomicRanges][]. 
- description, authors, and installation instructions.
- references to scientific literature
- vignettes and reference manual
- indication of cross-platform availability
- download statistics.

# Installation

 A package needs to be installed once, using the instructions on the
    landing page. Once installed, the package can be loaded into an R
    session

```{r require}
library(GenomicRanges)
```

and the help system queried interactively, as outlined above:

```{r help, eval=FALSE}
  help(package="GenomicRanges")
  vignette(package="GenomicRanges")
  vignette(package="GenomicRanges", "GenomicRangesHOWTOs")
  ?GRanges
```


# Types of Packages

Now examples for popular packages for high-throughput analyses

# Differential expression

- Important packages for analysis of differential expression include
  [edgeR][] and [DESeq2][]; both have excellent vignettes for
  exploration. Additional research methods embodied in Bioconductor
  packages can be discovered by visiting the [biocViews][] web page,
  searching for the 'DifferentialExpression' view term, and narrowing
  the selection by searching for 'RNA seq' and similar.

# ChIP-seq

- Popular ChIP-seq packages include [csaw][] an d[DiffBind][] for
  comparison of peaks across samples, [ChIPQC][] for quality
  assessment, and [ChIPseeker][] for annotating results (e.g.,
  discovering nearby genes). What other ChIP-seq packages are listed
  on the [biocViews][] page?

# Variants

- Working with called variants (VCF files) is facilitated by packages
  such as [VariantAnnotation][], [VariantFiltering][], [ensemblVEP][],
  and [SomaticSignatures][]; packages for calling variants include,
  e.g., [h5vc][] and [VariantTools][].


# Copy number variants

- Several packages identify copy number variants from sequence data,
  including [cn.mops][]; from the [biocViews][] page, what other copy
  number packages are available? The [CNTools][] package provides some
  useful facilities for comparison of segments across samples.

# Microbiome and Metagenomic analysis

- Microbiome and metagenomic analysis is facilitated by packages such
  as [phyloseq][] and [metagenomeSeq][].


# Metabolomics, Chemoinformatics, Image analysis

- Metabolomics, chemoinformatics, image analysis, and many other
  high-throughput analysis domains are also represented in
  Bioconductor; explore these via biocViews and title searches.
  
# Genomic Ranges
 
Working with sequences, alignments, common web file formats, and raw
data; these packages rely very heavily on the [IRanges][] /
[GenomicRanges][] infrastructure that we will encounter later in the
course.

# Sequences

- The [Biostrings][] package is used to represent DNA and other
  sequences, with many convenient sequence-related functions. Check
  out the functions documented on the help page `?consensusMatrix`,
  for instance. Also check out the [BSgenome][] package for working
  with whole genome sequences, e.g., `?"getSeq,BSgenome-method"`

# Alignments

- The [GenomicAlignments][] package is used to input reads aligned to
  a reference genome. See for instance the `?readGAlignments` help
  page and `vigentte(package="GenomicAlignments",
  "summarizeOverlaps")`
- [rtracklayer][]'s `import` and `export` functions can read in many
  common file types, e.g., BED, WIG, GTF, ..., in addition to querying
  and navigating the UCSC genome browser. Check out the `?import` page
  for basic usage.
- The [ShortRead][] and [Rsamtools][] packages can be used for
  lower-level access to FASTQ and BAM files, respectively. Explore the
  [ShortRead vignette](http://bioconductor.org/packages/release/bioc/vignettes/ShortRead/inst/doc/Overview.pdf)
  and Scalable Genomics labs to see approaches to effectively
  processing the large files.

# Visualization

- The [Gviz][] package provides great tools for visualizing local
  genomic coordinates and associated data. 
- [epivizr][] drives the [epiviz](http://epiviz.cbcb.umd.edu/) genome
  browser from within R; [rtracklayer][] provides easy ways to
  transfer data to and manipulate UCSC browser sessions.
- Additionl packages include [ggbio][], [OmicCircos][], ...

# DNA or amino acid sequences
_Biostrings_, _ShortRead_, _BSgenome_

# Classes

- XString, XStringSet, e.g., DNAString (genomes),
  DNAStringSet (reads)

# Methods 

- [Cheat sheat](http://bioconductor.org/packages/release/bioc/vignettes/Biostrings/inst/doc/BiostringsQuickOverview.pdf)
- Manipulation, e.g., `reverseComplement()`
- Summary, e.g., `letterFrequency()`
- Matching, e.g., `matchPDict()`, `matchPWM()`

# Related packages

- [BSgenome][]
    - Whole-genome representations
    - Model and custom
- [ShortRead][]
    - FASTQ files

# Example 

- Whole-genome sequences are distrubuted by ENSEMBL, NCBI, and others
  as FASTA files; model organism whole genome sequences are packaged
  into more user-friendly `BSgenome` packages. The following
  calculates GC content across chr14.

```{r BSgenome-require, message=FALSE}
  require(BSgenome.Hsapiens.UCSC.hg19)
  chr14_range = GRanges("chr14", IRanges(1, seqlengths(Hsapiens)["chr14"]))
  chr14_dna <- getSeq(Hsapiens, chr14_range)
  letterFrequency(chr14_dna, "GC", as.prob=TRUE)
```
  
# Ranges: _GenomicRanges_, _IRanges_

Ranges represent:
- Data, e.g., aligned reads, ChIP peaks, SNPs, CpG islands, ...
- Annotations, e.g., gene models, regulatory elements, methylated
  regions
- Ranges are defined by chromosome, start, end, and strand
- Often, metadata is associated with each range, e.g., quality of
  alignment, strength of ChIP peak

Many common biological questions are range-based
- What reads overlap genes?
- What genes are ChIP peaks nearest?
- ...

# GenomicRanges 

The [GenomicRanges][] package defines essential classes and methods

### `GRanges`

![Alt ](our_figures/GRanges.png)
# GenomicRanges
### `GRangesList`

![Alt ](our_figures/GRangesList.png)
# GenomicRanges
### Range operations

![Alt Ranges Algebra](our_figures/RangeOperations.png)

# Ranges
- IRanges
  - `start()` / `end()` / `width()`
  - List-like -- `length()`, subset, etc.
  - 'metadata', `mcols()`
- GRanges
  - 'seqnames' (chromosome), 'strand'
  - `Seqinfo`, including `seqlevels` and `seqlengths`

# Intra-range methods
- Independent of other ranges in the same object
- GRanges variants strand-aware
- `shift()`, `narrow()`, `flank()`, `promoters()`, `resize()`,
  `restrict()`, `trim()`
- See `?"intra-range-methods"`

# Inter-range methods
- Depends on other ranges in the same object
- `range()`, `reduce()`, `gaps()`, `disjoin()`
- `coverage()` (!)
- see `?"inter-range-methods"`

# Between-range methods
- Functions of two (or more) range objects
- `findOverlaps()`, `countOverlaps()`, ..., `%over%`, `%within%`,
  `%outside%`; `union()`, `intersect()`, `setdiff()`, `punion()`,
  `pintersect()`, `psetdiff()`

# Example

```{r ranges, message=FALSE, eval=F}
require(GenomicRanges)
gr <- GRanges("A", IRanges(c(10, 20, 22), width=5), "+")
shift(gr, 1)                            # 1-based coordinates!
range(gr)                               # intra-range
reduce(gr)                              # inter-range
coverage(gr)
setdiff(range(gr), gr)                  # 'introns'
```

# IRangesList, GRangesList
- List: all elements of the same type
- Many *List-aware methods, but a common 'trick': apply a vectorized
  function to the unlisted representaion, then re-list
        grl <- GRangesList(...)
        orig_gr <- unlist(grl)
        transformed_gr <- FUN(orig)
        transformed_grl <- relist(, grl)

# Aligned reads: _GenomicAlignments_, _Rsamtools_
Classes -- GenomicRanges-like behaivor
- GAlignments, GAlignmentPairs, GAlignmentsList
- SummarizedExperiment
  - Matrix where rows are indexed by genomic ranges, columns by a
    DataFrame.
Methods
- `readGAlignments()`, `readGAlignmentsList()`
  - Easy to restrict input, iterate in chunks
- `summarizeOverlaps()`

# Example

- Find reads supporting the junction identified above, at position
  19653707 + 66M = 19653773 of chromosome 14

```{r bam-require, eval=F}
require(GenomicRanges)
require(GenomicAlignments)
require(Rsamtools)
## our 'region of interest'
roi <- GRanges("chr14", IRanges(19653773, width=1)) 
## sample data
require('RNAseqData.HNRNPC.bam.chr14')
bf <- BamFile(RNAseqData.HNRNPC.bam.chr14_BAMFILES[[1]], asMates=TRUE)
## alignments, junctions, overlapping our roi
paln <- readGAlignmentsList(bf)
j <- summarizeJunctions(paln, with.revmap=TRUE)
j_overlap <- j[j %over% roi]
## supporting reads
paln[j_overlap$revmap[[1]]]
```

  
# Called variants: _VariantAnnotation_, _VariantFiltering_

Classes -- GenomicRanges-like behavior
- VCF -- 'wide'
- VRanges -- 'tall'
Functions and methods
- I/O and filtering: `readVcf()`, `readGeno()`, `readInfo()`,
  `readGT()`, `writeVcf()`, `filterVcf()`
- Annotation: `locateVariants()` (variants overlapping ranges),
  `predictCoding()`, `summarizeVariants()`
- SNPs: `genotypeToSnpMatrix()`, `snpSummary()`

# Example
- Read variants from a VCF file, and annotate with respect to a known
  gene model
  
```{r vcf, message=FALSE}
  ## input variants
  require(VariantAnnotation)
  fl <- system.file("extdata", "chr22.vcf.gz", package="VariantAnnotation")
  vcf <- readVcf(fl, "hg19")
  seqlevels(vcf) <- "chr22"
  ## known gene model
  require(TxDb.Hsapiens.UCSC.hg19.knownGene)
  coding <- locateVariants(rowRanges(vcf),
      TxDb.Hsapiens.UCSC.hg19.knownGene,
      CodingVariants())
  head(coding)
```

# Related packages

- [ensemblVEP][] 
    - Forward variants to Ensembl Variant Effect Predictor
- [VariantTools][], [h5vc][]
    - Call variants
- [VariantFiltering][]
    - Filter variants using criteria such as coding consequence, MAF,
       ..., inheritance model

# Integrated data representations: _SummarizedExperiment_

![](our_figures/SE_Description.png)

[SummarizedExperiment][]
- 'feature' x 'sample' `assays()`
- `colData()` data frame for desciption of samples
- `rowRanges()` _GRanges_ / _GRangeList_ or data frame for description
  of features
- `exptData()` to describe the entire object
    ```{r SummarizedExperiment}
    library(SummarizedExperiment)
    library(airway)
    data(airway)
    airway
    colData(airway)
    airway[, airway$dex %in% "trt"]
    ```
    
# Annotation: _org_, _TxDb_, _AnnotationHub_, _biomaRt_, ...

- _Bioconductor_ provides extensive access to 'annotation' resources
  (see the [AnnotationData][] biocViews hierarchy); some interesting
  examples to explore during this lab include:
- [biomaRt][], [PSICQUIC][], [KEGGREST][] and other packages for
  querying on-line resources; each of these have informative vignettes.
- [AnnotationDbi][] is a cornerstone of the
  [Annotation Data][AnnotationData] packages provided by Bioconductor.
    - **org** packages (e.g., [org.Hs.eg.db][]) contain maps between
      different gene identifiers, e.g., ENTREZ and SYMBOL. The basic
      interface to these packages is described on the help page `?select`
    - **TxDb** packages (e.g., [TxDb.Hsapiens.UCSC.hg19.knownGene][])
      contain gene models (exon coordinates, exon / transcript
      relationships, etc) derived from common sources such as the hg19
      knownGene track of the UCSC genome browser. These packages can be
      queried, e.g., as described on the `?exonsBy` page to retrieve all
      exons grouped by gene or transcript.
    - **BSgenome** packages (e.g., [BSgenome.Hsapiens.UCSC.hg19][])
      contain whole genomes of model organisms.
- [VariantAnnotation][] and [ensemblVEP][] provide access to sequence
  annotation facilities, e.g., to identify coding variants; see the
  [Introduction to VariantAnnotation](http://bioconductor.org/packages/release/bioc/vignettes/ShortRead/inst/doc/Overview.pdf)
  vignette for a brief introduction.
- Take a quick look at the [annotation work
  flow](http://bioconductor.org/help/workflows/annotation/annotation/)
  on the Bioconductor web site.


# Scalable computing

1. Efficient _R_ code
   - Vectorize!
   - Reuse others' work Know -- [DESeq2][], [GenomicRanges][],
     [Biostrings][], [dplyr][], [data.table][], [Rcpp][]
2. Iteration
   - Chunk-wise
   - `open()`, read chunk(s), `close()`.
   - e.g., `yieldSize` argument to `Rsamtools::BamFile()`
3. Restriction
   - Limit to columns and / or rows of interest
   - Exploit domain-specific formats, e.g., BAM files and
     `Rsamtools::ScanBamParam()`
   - Use a data base
4. Sampling
   - Iterate through large data, retaining a manageable sample, e.g.,
     `ShortRead::FastqSampler()`
5. Parallel evaluation
   - **After** writing efficient code
   - Typically, `lapply()`-like operations
   - Cores on a single machine ('easy'); clusters (more tedious);
     clouds

# Parallel evaluation in _Bioconductor_

- [BiocParallel][] -- `bplapply()` for `lapply()`-like functions,
  increasingly used by package developers to provide easy, standard
  way of gaining parallel evaluation.
- [GenomicFiles][] -- Framework for working on groups of files,
  ranges, or ranges x files
- Bioconductor [AMI][] (Amazon Machine Instance) including
  pre-configured StarCluster, and [docker] containers.

# Resources

_R_ / _Bioconductor_

- [Web site][Bioconductor] -- install, learn, use, develop _R_ /
  _Bioconductor_ packages
- [Support](http://support.bioconductor.org) -- seek help and
  guidance; also
  [StackOverflow](http://stackoverflow.com/questions/tagged/r) for _R_
  programming questions
- [biocViews](http://bioconductor.org/packages/release/BiocViews.html)
  -- discover packages
- Package landing pages, e.g.,
  [GenomicRanges](http://bioconductor.org/packages/release/bioc/html/GenomicRanges.html),
  including title, description, authors, installation instructions,
  vignettes (e.g., GenomicRanges '[How
  To](http://bioconductor.org/packages/release/bioc/vignettes/GenomicRanges/inst/doc/GenomicRangesHOWTOs.pdf)'),
  etc.
- [Course](http://bioconductor.org/help/course-materials/) and other
  [help](http://bioconductor.org/help/) material (e.g., videos, EdX
  course, community blogs, ...)

# Publications (General _Bioconductor_)

- Lawrence M, Huber W, Pag&egrave;s H, Aboyoun P, Carlson M, et al. (2013)
  Software for Computing and Annotating Genomic Ranges. PLoS Comput
  Biol 9(8): e1003118. doi:
  [10.1371/journal.pcbi.1003118][GRanges.bib]
- Lawrence, M, and Morgan, M. 2014. Scalable Genomics with R and
  Bioconductor. Statistical Science 2014, Vol. 29, No. 2,
  214-226. [http://arxiv.org/abs/1409.2864v1][Scalable.bib]

Other

- Lawrence, M. 2014. Software for Enabling Genomic Data
  Analysis. Bioc2014 conference [slides][Lawrence.bioc2014.bib].


<!-- Bibliography -->

[R]: http://r-project.org
[Bioconductor]: http://bioconductor.org
[GRanges.bib]: https://doi.org/10.1371/journal.pcbi.1003118
[Scalable.bib]: http://arxiv.org/abs/1409.2864
[Lawrence.bioc2014.bib]:
    http://bioconductor.org/help/course-materials/2014/BioC2014/Lawrence_Talk.pdf


[AnnotationData]: http://bioconductor.org/packages/release/BiocViews.html#___AnnotationData
[biocViews]: http://bioconductor.org/packages/release/BiocViews.html#___Software

[AnnotationDbi]: http://bioconductor.org/packages/AnnotationDbi
[AnnotationHub]: http://bioconductor.org/packages/AnnotationHub
[BSgenome.Hsapiens.UCSC.hg19]: http://bioconductor.org/packages/BSgenome.Hsapiens.UCSC.hg19
[BSgenome]: http://bioconductor.org/packages/BSgenome
[BiocParallel]: http://bioconductor.org/packages/BiocParallel
[Biostrings]: http://bioconductor.org/packages/Biostrings
[CNTools]: http://bioconductor.org/packages/CNTools
[ChIPQC]: http://bioconductor.org/packages/ChIPQC
[ChIPseeker]: http://bioconductor.org/packages/ChIPseeker
[DESeq2]: http://bioconductor.org/packages/DESeq2
[DiffBind]: http://bioconductor.org/packages/DiffBind
[GenomicAlignments]: http://bioconductor.org/packages/GenomicAlignments
[GenomicFiles]: http://bioconductor.org/packages/GenomicFiles
[GenomicRanges]: http://bioconductor.org/packages/GenomicRanges
[Homo.sapiens]: http://bioconductor.org/packages/Homo.sapiens
[IRanges]: http://bioconductor.org/packages/IRanges
[KEGGREST]: http://bioconductor.org/packages/KEGGREST
[PSICQUIC]: http://bioconductor.org/packages/PSICQUIC
[Rsamtools]: http://bioconductor.org/packages/Rsamtools
[Rsubread]: http://bioconductor.org/packages/Rsubread
[ShortRead]: http://bioconductor.org/packages/ShortRead
[SomaticSignatures]: http://bioconductor.org/packages/SomaticSignatures
[SummarizedExperiment]: http://bioconductor.org/packages/SummarizedExperiment
[TxDb.Hsapiens.UCSC.hg19.knownGene]: http://bioconductor.org/packages/TxDb.Hsapiens.UCSC.hg19.knownGene
[VariantAnnotation]: http://bioconductor.org/packages/VariantAnnotation
[VariantFiltering]: http://bioconductor.org/packages/VariantFiltering
[VariantTools]: http://bioconductor.org/packages/VariantTools
[biomaRt]: http://bioconductor.org/packages/biomaRt
[cn.mops]: http://bioconductor.org/packages/cn.mops
[csaw]: http://bioconductor.org/packages/csaw
[edgeR]: http://bioconductor.org/packages/edgeR
[ensemblVEP]: http://bioconductor.org/packages/ensemblVEP 
[h5vc]: http://bioconductor.org/packages/h5vc
[limma]: http://bioconductor.org/packages/limma
[metagenomeSeq]: http://bioconductor.org/packages/metagenomeSeq
[org.Hs.eg.db]: http://bioconductor.org/packages/org.Hs.eg.db
[org.Sc.sgd.db]: http://bioconductor.org/packages/org.Sc.sgd.db
[phyloseq]: http://bioconductor.org/packages/phyloseq
[rtracklayer]: http://bioconductor.org/packages/rtracklayer
[snpStats]: http://bioconductor.org/packages/snpStats
[Gviz]: http://bioconductor.org/packages/Gviz
[epivizr]: http://bioconductor.org/packages/epivizr
[ggbio]: http://bioconductor.org/packages/ggbio
[OmicCircos]: http://bioconductor.org/packages/OmicCircos

[dplyr]: https://cran.r-project.org/package=dplyr
[data.table]: https://cran.r-project.org/package=data.table
[Rcpp]: https://cran.r-project.org/package=Rcpp

[AMI]: http://bioconductor.org/help/bioconductor-cloud-ami/
[docker]: http://bioconductor.org/help/docker/