--- title: "B.4 -- Next Steps" author: Martin Morgan date: "16 - 17 May, 2016" output: BiocStyle::html_document: toc: true toc_depth: 2 vignette: > % \VignetteIndexEntry{B.4 -- Next Steps} % \VignetteEngine{knitr::rmarkdown} --- ```{r style, echo = FALSE, results = 'asis'} options(width=100) knitr::opts_chunk$set( eval=as.logical(Sys.getenv("KNITR_EVAL", "TRUE")), cache=as.logical(Sys.getenv("KNITR_CACHE", "TRUE"))) ``` ```{r setup, echo=FALSE} suppressPackageStartupMessages({ library(BiocParallel) library(microbenchmark) }) ``` # Common _Bioconductor_ work flows 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. 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][] and [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. - 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](https://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. - Additional packages include [ggbio][], [OmicCircos][], ... ![Alt Sequencing Ecosystem](our_figures/SequencingEcosystem.png) # Computation: Big Data ## Efficient _R_ code The goal of this section is to highlight practices for writing correct, robust and efficient R code. ## Priorities 1. Correct: consistent with hand-worked examples (`identical()`, `all.equal()`) 2. Robust: supports realistic inputs, e.g., 0-length vectors, `NA` values, ... 3. Simple: easy to understand next month; easy to describe what it does to a colleague; easy to spot logical errors; easy to enhance. 4. Fast, or at least reasonable given the speed of modern computers. ## Strategies 1. Profile - _Look_ at the script to understand in general terms what it is doing. - _Step_ through the code to see how it is executed, and to gain an understanding of the speed of each line. - _Time_ evaluation of select lines or simple chunks of code with `system.time()` or the `r CRANpkg("microbenchmark")` package. - _Profile_ the code with a tool that indicates how much time is spent in each function call or line -- the built-in `Rprof()` function, or packages such as `r CRANpkg("lineprof")` or `r CRANpkg("aprof")` 2. Vectorize -- operate on vectors, rather than explicit loops ```{r vectorize} x <- 1:10 log(x) ## NOT for (i in seq_along(x)) x[i] <- log(x[i]) ``` 3. Pre-allocate memory, then fill in the result ```{r pre-allocate} result <- numeric(10) result[1] <- runif(1) for (i in 2:length(result)) result[i] <- runif(1) * result[i - 1] result ``` 4. Hoist common sub-expressions outside of repeated calculations, so that the sub-expression is only calculated once - Simple, e.g., 'hoist' constant multiplications from a `for` loop - Higher-level, e.g., use `lm.fit()` rather than repeatedly fitting the same design matrix. 5. Re-use existing, tested code - Efficient implementations of common operations -- `tabulate()`, `rowSums()` and friends, `%in%`, ... - Efficient domain-specific implementations, e.g., `r Biocpkg("snpStats")` for GWAS linear models; `r Biocpkg("limma")` for microarray linear models; `r Biocpkg("edgeR")`, `r Biocpkg("DESeq2")` for negative binomial GLMs relevant to RNASeq. - Reuse others' work -- `r Biocpkg("DESeq2")`, `r Biocpkg("GenomicRanges")`, `r Biocpkg("Biostrings")`, ..., `r CRANpkg("dplyr")`, `r CRANpkg("data.table")`, `r CRANpkg("Rcpp")` ### Case Study: Pre-allocate and vectorize Here's an obviously inefficient function: ```{r inefficient} f0 <- function(n, a=2) { ## stopifnot(is.integer(n) && (length(n) == 1) && ## !is.na(n) && (n > 0)) result <- numeric() for (i in seq_len(n)) result[[i]] <- a * log(i) result } ``` Use `system.time()` to investigate how this algorithm scales with `n`, focusing on elapsed time. ```{r system-time} system.time(f0(10000)) n <- 1000 * seq(1, 20, 2) t <- sapply(n, function(i) system.time(f0(i))[[3]]) plot(t ~ n, type="b") ``` Remember the current 'correct' value, and an approximate time ```{r correct-init} n <- 10000 system.time(expected <- f0(n)) head(expected) ``` Revise the function to hoist the common multiplier, `a`, out of the loop. Make sure the result of the 'optimization' and the original calculation are the same. Use the `r CRANpkg("microbenchmark")` package to compare the two versions ```{r hoist} f1 <- function(n, a=2) { result <- numeric() for (i in seq_len(n)) result[[i]] <- log(i) a * result } identical(expected, f1(n)) library(microbenchmark) microbenchmark(f0(n), f1(n), times=5) ``` Adopt a 'pre-allocate and fill' strategy ```{r preallocate-and-fill} f2 <- function(n, a=2) { result <- numeric(n) for (i in seq_len(n)) result[[i]] <- log(i) a * result } identical(expected, f2(n)) microbenchmark(f0(n), f2(n), times=5) ``` Use an `*apply()` function to avoid having to explicitly pre-allocate, and make opportunities for vectorization more apparent. ```{r use-apply} f3 <- function(n, a=2) a * sapply(seq_len(n), log) identical(expected, f3(n)) microbenchmark(f0(n), f2(n), f3(n), times=10) ``` Now that the code is presented in a single line, it is apparent that it could be easily vectorized. Seize the opportunity to vectorize it: ```{r use-vectorize} f4 <- function(n, a=2) a * log(seq_len(n)) identical(expected, f4(n)) microbenchmark(f0(n), f3(n), f4(n), times=10) ``` `f4()` definitely seems to be the winner. How does it scale with `n`? (Repeat several times) ```{r vectorized-scale} n <- 10 ^ (5:8) # 100x larger than f0 t <- sapply(n, function(i) system.time(f4(i))[[3]]) plot(t ~ n, log="xy", type="b") ``` Any explanations for the different pattern of response? Lessons learned: 1. Vectorizing offers a huge improvement over iteration 2. Pre-allocate-and-fill is very helpful when explicit iteration is required. 3. `*apply()` functions help avoid need for explicit pre-allocation and make opportunities for vectorization more apparent. This may come at a small performance cost, but is generally worth it 4. Hoisting common sub-expressions can be helpful for improving performance when explicit iteration is required. ## Parallel evaluation When data are too large to fit in memory, we can iterate through files in chunks or subset the data by fields or genomic positions. Iteration - Chunk-wise - `open()`, read chunk(s), `close()`. - e.g., `yieldSize` argument to `Rsamtools::BamFile()` - Framework: `GenomicFiles::reduceByYield()` Restriction - Limit to columns and / or rows of interest - Exploit domain-specific formats - BAM files and `Rsamtools::ScanBamParam()` - BAM files and `Rsamtools::PileupParam()` - VCF files and `VariantAnnotation::ScanVcfParam()` - Use a data base Parallel evalutation - _After_ writing efficient _R_ code, adopting iteration and restriction - [BiocParallel][] for common, cross-platform operation [BiocParallel][] provides a standardized interface for simple parallel evaluation. The package builds provides access to the `snow` and `multicore` functionality in the `parallel` package as well as `BatchJobs` for running cluster jobs. General ideas: - Use `bplapply()` instead of `lapply()` - Argument `BPPARAM` influences how parallel evaluation occurs - `MulticoreParam()`: threads on a single (non-Windows) machine - `SnowParam()`: processes on the same or different machines - `BatchJobsParam()`: resource scheduler on a cluster - `DoparParam()`: parallel execution with `foreach()` ### Exercise: Sleeping serially and in parallel This small example motivates the use of parallel execution and demonstrates how `bplapply()` can be a drop in for `lapply`. Use `system.time()` to explore how long this takes to execute as `n` increases from 1 to 10. Use `identical()` and `r CRANpkg("microbenchmark")` to compare alternatives `f0()` and `f1()` for both correctness and performance. `fun` sleeps for 1 second, then returns `i`. ```{r parallel-sleep} library(BiocParallel) fun <- function(i) { Sys.sleep(1) i } ## serial f0 <- function(n) lapply(seq_len(n), fun) ## parallel f1 <- function(n) bplapply(seq_len(n), fun) ``` [biocViews]: https://bioconductor.org/packages/ [AnnotationDbi]: https://bioconductor.org/packages/AnnotationDbi [AnnotationHub]: https://bioconductor.org/packages/AnnotationHub [BSgenome.Hsapiens.UCSC.hg19]: https://bioconductor.org/packages/BSgenome.Hsapiens.UCSC.hg19 [BSgenome]: https://bioconductor.org/packages/BSgenome [BiocParallel]: https://bioconductor.org/packages/BiocParallel [Biostrings]: https://bioconductor.org/packages/Biostrings [CNTools]: https://bioconductor.org/packages/CNTools [ChIPQC]: https://bioconductor.org/packages/ChIPQC [ChIPseeker]: https://bioconductor.org/packages/ChIPseeker [DESeq2]: https://bioconductor.org/packages/DESeq2 [DiffBind]: https://bioconductor.org/packages/DiffBind [GenomicAlignments]: https://bioconductor.org/packages/GenomicAlignments [GenomicFiles]: https://bioconductor.org/packages/GenomicFiles [GenomicRanges]: https://bioconductor.org/packages/GenomicRanges [Gviz]: https://bioconductor.org/packages/Gviz [Homo.sapiens]: https://bioconductor.org/packages/Homo.sapiens [IRanges]: https://bioconductor.org/packages/IRanges [KEGGREST]: https://bioconductor.org/packages/KEGGREST [OmicCircos]: https://bioconductor.org/packages/OmicCircos [PSICQUIC]: https://bioconductor.org/packages/PSICQUIC [Rsamtools]: https://bioconductor.org/packages/Rsamtools [Rsubread]: https://bioconductor.org/packages/Rsubread [ShortRead]: https://bioconductor.org/packages/ShortRead [SomaticSignatures]: https://bioconductor.org/packages/SomaticSignatures [SummarizedExperiment]: https://bioconductor.org/packages/SummarizedExperiment [TxDb.Hsapiens.UCSC.hg19.knownGene]: https://bioconductor.org/packages/TxDb.Hsapiens.UCSC.hg19.knownGene [VariantAnnotation]: https://bioconductor.org/packages/VariantAnnotation [VariantFiltering]: https://bioconductor.org/packages/VariantFiltering [VariantTools]: https://bioconductor.org/packages/VariantTools [airway]: https://bioconductor.org/packages/airway [biomaRt]: https://bioconductor.org/packages/biomaRt [cn.mops]: https://bioconductor.org/packages/cn.mops [csaw]: https://bioconductor.org/packages/csaw [edgeR]: https://bioconductor.org/packages/edgeR [ensemblVEP]: https://bioconductor.org/packages/ensemblVEP [epivizr]: https://bioconductor.org/packages/epivizr [ggbio]: https://bioconductor.org/packages/ggbio [h5vc]: https://bioconductor.org/packages/h5vc [limma]: https://bioconductor.org/packages/limma [metagenomeSeq]: https://bioconductor.org/packages/metagenomeSeq [org.Hs.eg.db]: https://bioconductor.org/packages/org.Hs.eg.db [org.Sc.sgd.db]: https://bioconductor.org/packages/org.Sc.sgd.db [phyloseq]: https://bioconductor.org/packages/phyloseq [rtracklayer]: https://bioconductor.org/packages/rtracklayer [snpStats]: https://bioconductor.org/packages/snpStats [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