Working with Large Data

Martin Morgan
February 3, 2015

Scalable computing

1. Efficient R code
   • Vectorize!
   • Reuse others' work – DESeq2 , GenomicRanges , Biostrings , …, dplyr , data.table , Rcpp
   • Useful tools: system.time(), Rprof(), microbenchmark
   • More detail in deadly sins of a previous course.
2. Iteration
   • Chunk-wise
   • open(), read chunk(s), close().
   • e.g., yieldSize argument to Rsamtools::BamFile()
   • Framework: GenomicFiles::reduceByYield()
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.

Lab

Efficient code

Write the following as a function. Use system.time() to explore how long this takes to execute as n increases from 100 to 10000. Use identical() and microbenchmark to compare alternatives f1(), f2(), and f3() for both correctness and performance of these three different functions. What strategies are these functions using?

f0 <- function(n) {
 ## inefficient!
 ans <- numeric()
 for (i in seq_len(n))
 ans <- c(ans, exp(i))
 ans
}
f1 <- function(n) {
 ans <- numeric(n)
 for (i in seq_len(n))
 ans[[i]] <- exp(i)
 ans
}
f2 <- function(n)
 sapply(seq_len(n), exp)
f3 <- function(n)
 exp(seq_len(n))

Sleeping serially and in parallel

Go to sleep for 1 second, then return i. This takes 8 seconds.

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)

Counting overlaps – our own version

Iterate through files: GenomicFiles::reduceByYield()

• (1) yield a chunk; (2) map from the input chunk to a possibly transformed representation; (3) reduce mapped chunks

suppressPackageStartupMessages({
 library(GenomicFiles)
 library(GenomicAlignments)
 library(Rsamtools)
 library(TxDb.Hsapiens.UCSC.hg19.knownGene)
})
yield <- # how to input the next chunk of data
 function(X, ...)
{
 readGAlignments(X)
}
map <- # what to do to each chunk
 function(VALUE, ..., roi)
{
 olaps <- findOverlaps(VALUE, roi, type="within", ignore.strand=TRUE)
 count <- tabulate(subjectHits(olaps), subjectLength(olaps))
 setNames(count, names(roi))
}
reduce <- # how to combine mapped chunks
 `+`

Improvement: “yield factory” to keep track of how many records input

yieldFactory <- # return a function with local state 
 function() 
{
 n_records <- 0L
 function(X, ...) {
 aln <- readGAlignments(X)
 n_records <<- n_records + length(aln)
 message(n_records)
 aln
 }
}

Regions of interest, named like the chromosomes in the bam file.

exByTx <- exonsBy(TxDb.Hsapiens.UCSC.hg19.knownGene, "tx")
map0 <- read.delim("~/igv/genomes/hg19_alias.tab", header=FALSE, 
 stringsAsFactors=FALSE)
seqlevels(exByTx, force=TRUE) <- setNames(map0$V1, map0$V2)

A function to iterate through a bam file

count1 <- function(filename, roi) {
 message(filename)
 ## Create and open BAM file
 bf <- BamFile(filename, yieldSize=1000000)
 reduceByYield(bf, yieldFactory(), map, reduce, roi=roi)
}

In action

filename <- "~/bam/SRR1039508_sorted.bam"
count <- count1(filename, exByTx)

Parallelize

library(BiocParallel)
## all bam files
filenames <- dir("~/bam", pattern="bam$", full=TRUE)
names(filenames) <- sub("_sorted.bam", "", basename(filenames))
## iterate
counts <- bplapply(filenames, count1, exByTx)
counts <- simplify2array(counts)
head(counts)

Resources

• 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