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.
Working with sequences, alignments, common web file formats, and raw data; these packages rely very heavily on the IRanges / GenomicRanges infrastructure.
?consensusMatrix
, for instance. Also check out the BSgenome package for working with whole genome sequences, e.g., ?"getSeq,BSgenome-method"
?readGAlignments
help page and vigentte(package="GenomicAlignments", "summarizeOverlaps")
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.Visualization
The goal of this section is to highlight practices for writing correct, robust and efficient R code.
identical()
, all.equal()
)NA
values, …system.time()
or the microbenchmark package.Rprof()
function, or packages such as lineprof or aprofVectorize – operate on vectors, rather than explicit loops
x <- 1:10
log(x) ## NOT for (i in seq_along(x)) x[i] <- log(x[i])
## [1] 0.0000000 0.6931472 1.0986123 1.3862944 1.6094379 1.7917595 1.9459101
## [8] 2.0794415 2.1972246 2.3025851
Pre-allocate memory, then fill in the result
result <- numeric(10)
result[1] <- runif(1)
for (i in 2:length(result))
result[i] <- runif(1) * result[i - 1]
result
## [1] 7.744407e-01 2.837213e-01 1.332204e-01 1.315635e-01 5.785177e-03
## [6] 7.163900e-04 5.467269e-04 2.264849e-04 1.763930e-04 4.969534e-05
for
looplm.fit()
rather than repeatedly fitting the same design matrix.tabulate()
, rowSums()
and friends, %in%
, …Here’s an obviously inefficient function:
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.
system.time(f0(10000))
## user system elapsed
## 0.012 0.000 0.010
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
n <- 10000
system.time(expected <- f0(n))
## user system elapsed
## 0.004 0.000 0.004
head(expected)
## [1] 0.000000 1.386294 2.197225 2.772589 3.218876 3.583519
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 microbenchmark package to compare the two versions
f1 <- function(n, a=2) {
result <- numeric()
for (i in seq_len(n))
result[[i]] <- log(i)
a * result
}
identical(expected, f1(n))
## [1] TRUE
library(microbenchmark)
microbenchmark(f0(n), f1(n), times=5)
## Unit: milliseconds
## expr min lq mean median uq max neval
## f0(n) 3.245255 3.309522 3.392714 3.443682 3.460578 3.504531 5
## f1(n) 3.288731 3.328869 3.342055 3.353123 3.363228 3.376325 5
Adopt a ‘pre-allocate and fill’ strategy
f2 <- function(n, a=2) {
result <- numeric(n)
for (i in seq_len(n))
result[[i]] <- log(i)
a * result
}
identical(expected, f2(n))
## [1] TRUE
microbenchmark(f0(n), f2(n), times=5)
## Unit: milliseconds
## expr min lq mean median uq max neval
## f0(n) 3.206168 3.246131 3.264738 3.269302 3.290077 3.312011 5
## f2(n) 1.027886 1.031968 1.116341 1.032658 1.040285 1.448910 5
Use an *apply()
function to avoid having to explicitly pre-allocate, and make opportunities for vectorization more apparent.
f3 <- function(n, a=2)
a * sapply(seq_len(n), log)
identical(expected, f3(n))
## [1] TRUE
microbenchmark(f0(n), f2(n), f3(n), times=10)
## Unit: milliseconds
## expr min lq mean median uq max neval
## f0(n) 3.237000 3.321532 3.354189 3.358498 3.392304 3.425675 10
## f2(n) 1.027334 1.035455 1.077594 1.045682 1.066997 1.344483 10
## f3(n) 3.949579 3.977046 4.352450 4.013185 4.040701 7.465818 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:
f4 <- function(n, a=2)
a * log(seq_len(n))
identical(expected, f4(n))
## [1] TRUE
microbenchmark(f0(n), f3(n), f4(n), times=10)
## Unit: microseconds
## expr min lq mean median uq max neval
## f0(n) 3232.607 3263.341 3339.0286 3312.631 3339.555 3719.286 10
## f3(n) 3935.994 3959.041 3986.2257 3964.454 4026.461 4038.445 10
## f4(n) 235.517 236.350 539.0311 237.194 239.981 3250.685 10
f4()
definitely seems to be the winner. How does it scale with n
? (Repeat several times)
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:
*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 itWhen 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
open()
, read chunk(s), close()
.yieldSize
argument to Rsamtools::BamFile()
GenomicFiles::reduceByYield()
Restriction
Rsamtools::ScanBamParam()
Rsamtools::PileupParam()
VariantAnnotation::ScanVcfParam()
Parallel evalutation
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:
bplapply()
instead of lapply()
Argument BPPARAM
influences how parallel evaluation occurs
MulticoreParam()
: threads on a single (non-Windows) machineSnowParam()
: processes on the same or different machinesBatchJobsParam()
: resource scheduler on a clusterDoparParam()
: parallel execution with foreach()
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 microbenchmark to compare alternatives f0()
and f1()
for both correctness and performance.
fun
sleeps for 1 second, then returns i
.
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)