--- title: "2. Introduction to _Bioconductor_" author: "Valerie Obenchain (valerie.obenchain@roswellpark.org)
Lori Shepherd (lori.shepherd@roswellpark.org)
Martin Morgan (martin.morgan@roswellpark.org)
Stanford University, Stanford, CA
25 - 26 June, 2016" output: BiocStyle::html_document: toc: true toc_depth: 2 vignette: > % \VignetteIndexEntry{2. Introduction to _Bioconductor_} % \VignetteEngine{knitr::rmarkdown} --- ```{r style, echo = FALSE, results = 'asis'} BiocStyle::markdown() options(width=100, max.print=1000) knitr::opts_chunk$set( eval=as.logical(Sys.getenv("KNITR_EVAL", "TRUE")), cache=as.logical(Sys.getenv("KNITR_CACHE", "TRUE"))) ``` ```{r packages, eval=TRUE, echo=FALSE, warning=FALSE, message=FALSE} suppressPackageStartupMessages({ library(BioC2016Introduction) library(SummarizedExperiment) library(airway) }) ``` The material in this course requires R version 3.3 and Bioconductor version 3.4 ```{r configure-test} stopifnot( getRversion() >= '3.3' && getRversion() < '3.4', BiocInstaller::biocVersion() == "3.4" ) ``` Version: `r packageDescription("BioC2016Introduction")$Version`
Compiled: `r date()` # _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. Conceptually - Analysis and comprehension of high throughput genomic data # Core principles ## High-throughput analysis needs statistics! Volume of data Type of research question - Designed experiments - Population samples - ... Technological artifacts - Differences in sequencing depth between samples - Bias in the genomic regions sampled ## Scientific research needs to be reproducible ### A motivating case study - 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) - 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) Underlying data is a matrix - Regions of interest (e.g., genes) x samples - `assay()` -- e.g., matrix of counts of reads overlapping genes Include information about rows - `rowRanges()` -- gene identifiers, or _genomic ranges_ describing the coordinates of each gene Include information about columns - `colData()` -- describing samples, experimental design, ... ```{r airway-SummarizedExperiment} 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] ``` ## We can 'stand on the shoulders of giants' 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/) ## We should explore our data 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 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,"` to select help on methods, `class?D` for help on classes ## A sequence analysis package tour ![Alt Sequencing Ecosystem](our_figures/SequencingEcosystem.png) This very open-ended topic points to some of the most prominent Bioconductor packages for sequence analysis. Use the opportunity in this lab to explore the package vignettes and help pages highlighted below; many of the material will be covered in greater detail in subsequent labs and lectures. Basics - Bioconductor packages are listed on the [biocViews][] page. Each package has 'biocViews' (tags from a controlled vocabulary) associated with it; these can be searched to identify appropriately tagged packages, as can the package title and author. - Each package has a 'landing page', e.g., for [GenomicRanges][]. Visit this landing page, and note the description, authors, and installation instructions. Packages are often written up in the scientific literature, and if available the corresponding citation is present on the landing page. Also on the landing page are links to the vignettes and reference manual and, at the bottom, an indication of cross-platform availability and download statistics. - 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 ``` Domain-specific analysis -- explore the landing pages, vignettes, and reference manuals of two or three of the following packages. - 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. - 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? - 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][]. - 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 is facilitated by packages such as [phyloseq][] and [metagenomeSeq][]. - Metabolomics, chemoinformatics, image analysis, and many other high-throughput analysis domains are also represented in Bioconductor; explore these via biocViews and title searches. 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. - 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"` - 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? - ... The [GenomicRanges][] package defines essential classes and methods - `GRanges` ![Alt ](our_figures/GRanges.png) - `GRangesList` ![Alt ](our_figures/GRangesList.png) ### 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} 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) Reference - Lawrence M, Huber W, Pagès 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 ## 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} 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 Reference - Obenchain, V, Lawrence, M, Carey, V, Gogarten, S, Shannon, P, and Morgan, M. VariantAnnotation: a Bioconductor package for exploration and annotation of genetic variants. Bioinformatics, first published online March 28, 2014 [doi:10.1093/bioinformatics/btu168](http://bioinformatics.oxfordjournals.org/content/early/2014/04/21/bioinformatics.btu168) ## 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, Pagès 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]. Acknowledgements The research reported in this presentation was supported by the National Cancer Institute and the National Human Genome Research Institute of the National Institutes of Health under Award numbers U24CA180996 and U41HG004059, and the National Science Foundation under Award number 1247813. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the National Science Foundation. ## `sessionInfo()` ```{r sessionInfo} sessionInfo() ``` [R]: http://r-project.org [Bioconductor]: http://bioconductor.org [GRanges.bib]: http://dx.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/