An Introduction to the genoset Package
2020-04-27
Contents
1 Introduction
The genoset package offers an extension of the familiar
Bioconductor RangedSummarizedExperiment object for genome assays:
the GenoSet class. The GenoSet class provides additional API
features (e.g. indexing by genome position). The genoset package also
provides a number of convenient functions for working with data
associated with with genome locations.
1.1 Creating Objects
In typical Bioconductor style, GenoSet objects, and derivatives, can be created using the functions
with the same name. Let’s create some fake data to experiment
with. Don’t worry too much about how the fake data gets made. Notice
how assays can be matrices or RleDataFrames. They can also
be BigMatrix or BigMatrixFactor objects (from bigmemoryExtras).
library(genoset)## Loading required package: BiocGenerics## Loading required package: parallel##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:parallel':
##
## clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
## clusterExport, clusterMap, parApply, parCapply, parLapply,
## parLapplyLB, parRapply, parSapply, parSapplyLB## The following objects are masked from 'package:stats':
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## IQR, mad, sd, var, xtabs## The following objects are masked from 'package:base':
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## Filter, Find, Map, Position, Reduce, anyDuplicated, append,
## as.data.frame, basename, cbind, colnames, dirname, do.call,
## duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
## lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
## pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,
## tapply, union, unique, unsplit, which, which.max, which.min## Loading required package: GenomicRanges## Loading required package: stats4## Loading required package: S4Vectors##
## Attaching package: 'S4Vectors'## The following object is masked from 'package:base':
##
## expand.grid## Loading required package: IRanges##
## Attaching package: 'IRanges'## The following object is masked from 'package:grDevices':
##
## windows## Loading required package: GenomeInfoDb## Loading required package: SummarizedExperiment## Loading required package: Biobase## Welcome to Bioconductor
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## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.## Loading required package: DelayedArray## Loading required package: matrixStats##
## Attaching package: 'matrixStats'## The following objects are masked from 'package:Biobase':
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## anyMissing, rowMedians##
## Attaching package: 'DelayedArray'## The following objects are masked from 'package:matrixStats':
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## colMaxs, colMins, colRanges, rowMaxs, rowMins, rowRanges## The following objects are masked from 'package:base':
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## aperm, apply, rowsumsample.names = LETTERS[11:13]
probe.names = paste("p", 1:1000, sep="")
num.samples = length(sample.names)
num.probes = length(probe.names)
locs = GRanges(
ranges= IRanges(
start=c(seq(from=125e6,by=3e4,length=400), seq(from=1,length=400,by=3.25e4),
seq(from=30e6,length=200,by=3e4)),width=1,names=probe.names),
seqnames=factor(c(rep("chr8",400), rep("chr12",400),rep("chr17",200)),levels=c("chr8","chr12","chr17")))
fake.cn = matrix(c(
c(rnorm(200,.4,0.05),rnorm(200,.2,0.05),rnorm(200,0.23,0.05),rnorm(200,.15,0.05),rnorm(200,2.,0.05)),
c(rnorm(200,0,0.05), rnorm(200,3,0.05),rnorm(200,14,0.05),rnorm(200,.1,0.05),rnorm(200,-0.05,0.05)),
c(rnorm(200,.1,0.05),rnorm(200,1,0.05),rnorm(200,-.5,0.05),rnorm(200,3,0.05),rnorm(200,3,0.05))
),
nrow=num.probes,ncol=num.samples,dimnames=list(probe.names,sample.names))
fake.pData=data.frame(matrix(LETTERS[1:15],nrow=3,ncol=5,dimnames=list(sample.names,letters[1:5])))
gs = GenoSet( rowRanges=locs, assays=list(cn=fake.cn), colData=fake.pData )
gs## class: GenoSet
## dim: 1000 3
## metadata(0):
## assays(1): cn
## rownames(1000): p1 p2 ... p999 p1000
## rowData names(0):
## colnames(3): K L M
## colData names(5): a b c d erle.ds = GenoSet( rowRanges=locs,
assays=list(cn = fake.cn,
cn.segments=RleDataFrame(
K=Rle(c(rep(1.5,300),rep(2.3,700))),L=Rle( c(rep(3.2,700),rep(2.1,300)) ),
M=Rle(rep(1.1,1000)),row.names=rownames(fake.cn))),
colData=fake.pData
)Let’s have look at what’s inside these objects.
names(assays(rle.ds))## [1] "cn" "cn.segments"head( rle.ds[,,"cn"] )## K L M
## p1 0.4024174 0.003482704 0.06147522
## p2 0.3755720 0.046454469 0.06323715
## p3 0.3572731 -0.038276304 0.14881797
## p4 0.3757684 0.019936165 0.18845884
## p5 0.3278210 0.029156829 0.05976260
## p6 0.4731840 0.040997813 0.02064478head( rle.ds[,,"cn.segments"] )## RleDataFrame with 6 rows and 3 columns## rownames: p1, p2, p3, p4, p5, p6, ...## $K
## numeric-Rle of length 6 with 1 run
## Lengths: 6
## Values : 1.5
##
## $L
## numeric-Rle of length 6 with 1 run
## Lengths: 6
## Values : 3.2
##
## $M
## numeric-Rle of length 6 with 1 run
## Lengths: 6
## Values : 1.11.2 Accessing Genome Information
Now lets look at some special functions for accessing genome
information from a genoset object. These functions are all defined for
GenoSet and GenomicRanges objects. We can access per-feature information
as well as summaries of chromosome boundaries in base-pair or
row-index units.
There are a number of functions for getting
portions of the locData data. chr and pos return the
chromosome and position information for each feature. genoPos is like pos, but it
returns the base positions counting from the first base in the genome, with the
chromosomes in order by number and then alphabetically for the letter
chromosomes. chrInfo returns the genoPos of the first and last feature on each chromosome in
addition to the offset of the first feature from the start of the genome. chrInfo results are
used for drawing chromosome boundaries on genome-scale plots. pos
and genoPos are defined as the floor of the average of each
features start and end positions.
head( rowRanges(gs) )## GRanges object with 6 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## p1 chr8 125000000 *
## p2 chr8 125030000 *
## p3 chr8 125060000 *
## p4 chr8 125090000 *
## p5 chr8 125120000 *
## p6 chr8 125150000 *
## -------
## seqinfo: 3 sequences from an unspecified genome; no seqlengthschrNames(gs)## [1] "chr8" "chr12" "chr17"chrOrder(c("chr12","chr12","chrX","chr8","chr7","chrY"))## [1] "chr7" "chr8" "chr12" "chr12" "chrX" "chrY"chrInfo(gs)## start stop offset
## chr8 1 136970000 0
## chr12 136970001 149937501 136970000
## chr17 149937502 185907501 149937501chrIndices(gs)## first last offset
## chr8 1 400 0
## chr12 401 800 400
## chr17 801 1000 800head(chr(gs))## [1] "chr8" "chr8" "chr8" "chr8" "chr8" "chr8"head(start(gs))## [1] 125000000 125030000 125060000 125090000 125120000 125150000head(end(gs))## [1] 125000000 125030000 125060000 125090000 125120000 125150000head(pos(gs))## [1] 125000000 125030000 125060000 125090000 125120000 125150000head(genoPos(gs))## chr8 chr8 chr8 chr8 chr8 chr8
## 125000000 125030000 125060000 125090000 125120000 1251500001.3 Genome Order
GenoSet and GenomicRanges objects can be set to, and checked for, genome
order. Weak genome order requires that features be ordered within each
chromosome. Strong genome order requires a certain order of chromosomes
as well. Features must be ordered so that features from the same
chromosome are in contiguous blocks.
Certain methods on GenoSet objects expect the rows to be in genome
order. Users are free to rearrange rows within
chromosome as they please.
The proper order of chromosomes is desirable for full-genome plots and is specified by the
chrOrder function. The object creation method Genoset creates objects in strict genome order.
chrOrder(chrNames(gs))## [1] "chr8" "chr12" "chr17"gs = toGenomeOrder(gs, strict=TRUE)
isGenomeOrder(gs, strict=TRUE)## [1] TRUE1.4 Using the Subset Features
GenoSet objects can be subset using array notation. The `features'' index can be a set of ranges or the usual logical, numeric or character indices.chrIndices` with a chromosome argument is
a convenient way to get the indices needed to subset by chromosome.
Subset by chromosome
chr12.ds = gs[ chrIndices(gs,"chr12"), ]
dim(chr12.ds)## [1] 400 3chrIndices(chr12.ds)## first last offset
## chr12 1 400 0Subset by a collection of gene locations
gene.gr = GRanges(ranges=IRanges(start=c(35e6,127e6),end=c(35.5e6,129e6),
names=c("HER2","CMYC")), seqnames=c("chr17","chr8"))
gene.ds = gs[ gene.gr, ]
dim(gene.ds)## [1] 84 3chrIndices(gene.ds)## first last offset
## chr8 1 67 0
## chr17 68 84 67GenoSet objects can also be subset by a group of samples and/or features, just like an ExpressionSet, or a matrix for that matter.
dim(gs[1:4,1:2])## [1] 4 2eSet-derived classes tend to have special functions to get and set
specific assayDataElement members (the big data matrices). For
example, ExpressionSet has the exprs function. It is common to put
other optional matrices in assayData too (genotypes, quality
scores, etc.). These can be get and set with the assayDataElement
function, but typing that out can get old. GenoSet and derived
classes use the ``k’’ argument to the matrix subsetting bracket to
subset from a specific assayDataElement. In addition to saving some
typing, you can directly use a set of ranges to subset the
assayDataElement.
all( gs[ 1:4,1:2,"cn"] == assay(gs,"cn")[1:4,1:2] )## [1] TRUE2 Processing Data
2.1 Correction of Copy Number for local GC content
Copy number data generally shows a GC content effect that appears as
slow `waves'' along the genome (Diskin et al., NAR, 2008). The functiongcCorrect` can be used to remove this effect resulting in
much clearer data and more accurate segmentation. GC content is best
measured as the gc content in windows around each feature, about 2Mb
in size.
library(BSgenome.Hsapiens.UCSC.hg19)
gc = rnorm(nrow(gs))
gs[,,"cn"] = gcCorrect(gs[,,"cn"],gc)2.2 Segmentation
Segmentation is the process of identifying blocks of the genome in each sample that have the same copy number value. It is a smoothing method that attempts to replicate the biological reality where chunks of chromosome have been deleted or amplified.
Genoset contains a convenience function for segmenting data for each sample/chr using the DNAcopy package (the CBS algorithm). Genoset adds features to split jobs among processor cores. When the library parallel is loaded, the argument n.cores can control the number of processor cores utilized.
Additionally, GenoSet stores segment values so that they can be
accessed quickly at both the feature and segment level. We use a
RleDataFrame object where each column is a
Run-Length-Encoded Rle object. This dramatically reduces the
amount of memory required to store the segments. Note how the segmented values
become just another member of the assayData slot.
Try running CBS directly
library(DNAcopy)
cbs.cna = CNA(gs[,,"cn"], chr(gs), pos(gs) )
cbs.smoothed.CNA = smooth.CNA( cbs.cna )
cbs.segs = segment( cbs.cna )## Analyzing: Sample.1
## Analyzing: Sample.2
## Analyzing: Sample.3Or use the convenience function runCBS
gs[,,"cn.segs"] = runCBS(gs[,,"cn"],rowRanges(gs))## Working on segmentation for sample number 1 : K
## Working on segmentation for sample number 2 : L
## Working on segmentation for sample number 3 : MTry it with parallel
library(parallel)
gs[,,"cn.segs"] = runCBS(gs[, , "cn"],rowRanges(gs), n.cores=3)
gs[,,"cn.segs"][1:5,1:3]Other segmenting methods can also be used of course.
This function makes use of the parallel package to run things in parallel, so plan ahead when picking ``n.cores’’. Memory usage can be a bit hard to predict.
2.3 Segments as tables or runs
Having segmented the data for each sample, you may want to explore different
representations of the segments. Genoset describes data in genome
segments two ways: 1) as a table of segments, and 2) a
Run-Length-Encoded vector. Tables of segments are useful for printing, overlap queries,
database storage, or for summarizing changes in a sample. Rle
representations can be used like regular vectors, plotted as segments (see genoPlot), and
stored efficiently. A collection of Rle objects, one for each sample, are often stored as
one RleDataFrame in a GenoSet. genoset provides functions to quickly flip back and forth
between table and Rle representations. You can use these functions on single samples, or
the whole collection of samples.
head( gs[,,"cn.segs"] )## RleDataFrame with 6 rows and 3 columns## rownames: p1, p2, p3, p4, p5, p6, ...## $K
## numeric-Rle of length 6 with 1 run
## Lengths: 6
## Values : 0.3951
##
## $L
## numeric-Rle of length 6 with 1 run
## Lengths: 6
## Values : -0.0023
##
## $M
## numeric-Rle of length 6 with 1 run
## Lengths: 6
## Values : 0.0988segs = segTable( gs[,2,"cn.segs"], rowRanges(gs) )
list.of.segs = segTable( gs[,,"cn.segs"], rowRanges(gs) )
rbind.list.of.segs = segTable( gs[,,"cn.segs"], rowRanges(gs), stack=TRUE )
two.kinds.of.segs = segPairTable( gs[,2,"cn.segs"], gs[,3,"cn.segs"], rowRanges(gs) )
rle = segs2Rle( segs, rowRanges(gs) )
rle.df = segs2RleDataFrame( list.of.segs, rowRanges(gs) )
bounds = matrix( c(1,3,4,6,7,10),ncol=2,byrow=TRUE)
cn = c(1,3,2)
rle = bounds2Rle( bounds, cn, 10 )segPairTable summarizes two Rle objects into segments that have one unique
value for each Rle. This is useful for cases where you want genome regions with one
copy number state, and one LOH state, for example.
bounds2Rle is convenient if you already know the genome feature indices corresponding
to the bounds of each segment.
Currently we use data.frames for tables of segments. In the near future these will have colnames
that will make it easy to coerce these to GRanges. Coercion to GRanges
takes a while, so we don’t do that by default.
2.4 Gene Level Summaries
Analyses usually start with SNP or probeset level data. Often it is desirable to get
summaries of assayData matrices over an arbitrary set of ranges, like
exons, genes or cytobands. The function rangeSampleMeans serves this
purpose. Given a GenomicRanges of arbitrary genome ranges and a
GenoSet object, rangeSampleMeans will return a matrix of values
with a row for each range.
rangeSampleMeans uses boundingIndicesByChr to select the features
bounding each range. The bounding features are the features with locations equal to or within the start
and end of the range. If no feature exactly matches an end of the range, the nearest features outside
the range will be used. This bounding ensures that the full extent of the
range is accounted for, and more importantly, at least two features
are included for each gene, even if the range falls between two
features.
rangeColMeans is used to do a fast average of each of a
set of such bounding indices for each sample. These functions are
optimized for speed. For example, with 2.5M features and ~750
samples, it takes 0.12 seconds to find the features bounded by all
Entrez Genes (one RefSeq each). Calculating the mean value for each
gene and sample takes ~9 seconds for a matrix of array data and ~30
seconds for a RleDataFrame of compressed Rle objects.
Generally, you will want to summarize segmented data and will be working with a
RleDataFrame, like that returned by runCBS.
As an example, let’s say you want to get the copynumber of your two favorite genes from the subsetting example:
Get the gene-level summary: <<genelevel, eval=TRUE>>= boundingIndicesByChr( gene.gr, rowRanges(gs) ) rangeSampleMeans(gene.gr, gs, “cn.segs”)
# Plots
Genoset has some handy functions for plotting data along the genome.
Segmented data ``knows'' it should be plotted as lines, rather than
points. One often wants to plot just one chromosome, so a convenience
argument for chromosome subsetting is provided. Like `plot`, `genoPlot` plots x against y.
'x' can be some form of location data, like a `GenoSet` or `GenomicRanges`. 'y' is some
form of data along those coordinates, like a numeric vector or `Rle`. `genoPlot` marks
chromosome boundaries and labels positions in ``bp'', ``kb'', ``Mb'',
or ``Gb'' units as appropriate.
```r
genoPlot(gs, gs[ , 1, "cn"])
genoPlot(gs, gs[ , 1, "cn.segs"], add=TRUE, col="red")
Segmented copy number across the genome for 1st sample}
genoPlot(gs,gs[,1,"cn"],chr="chr12")
genoPlot(gs,gs[,1,"cn.segs"],chr="chr12",add=TRUE, col="red")
Segmented copy number across chromosome 12 for 1st sample
Plot data without a GenoSet object using numeric or Rle data:
chr12.ds = gs[chr(gs) == "chr12",]
genoPlot(pos(chr12.ds),chr12.ds[,1,"cn"],locs=rowRanges(chr12.ds)) # Numeric data and location
genoPlot(pos(chr12.ds),chr12.ds[,1,"cn.segs"],add=TRUE, col="red") # Rle data and numeric position2.5 Processing B-Allele Frequency Data
B-Allele Frequency (BAF) data can be converted into the ``Modified BAF’’ or mBAF metric, introduced by Staaf, et al., 2008. mBAF folds the values around the 0.5 axis and makes the HOM positions NA. The preferred way to identify HOMs is to use genotype calls from a matched normal (AA, AC, AG, etc.), but NA’ing greater than a certain value works OK. A hom.cutoff of 0.90 is suggested for Affymetrix arrays and 0.95 for Illumina arrays, following Staaf, et al.
Return data as a matrix:
fake.baf = sample(c(0,0.5,1), length(probe.names), replace=TRUE) + rnorm(length(probe.names),0,0.01)
fake.baf[ fake.baf > 1 ] = fake.baf[ fake.baf > 1 ] - 1
fake.baf[ fake.baf < 0 ] = fake.baf[ fake.baf < 0 ] + 1
hets = fake.baf < 0.75 & fake.baf > 0.25
fake.baf[ 101:200 ][ hets[101:200] ] = fake.baf[ 101:200 ][ hets[101:200] ] + rep(c(-0.2,0.2),50)[hets[101:200]]
fake.baf = matrix(fake.baf,nrow=num.probes,ncol=num.samples,dimnames=list(probe.names,sample.names))
baf.ds = GenoSet( rowRanges=locs, assays=list(lrr=fake.cn, baf=fake.baf), colData=fake.pData )
baf.ds[, , "mbaf"] = baf2mbaf(baf.ds[, , "baf"], hom.cutoff = 0.90)… or use compress it to an RleDataFrame. This uses ~1/3 the space on our random test data.
mbaf.data = RleDataFrame( sapply(colnames( baf.ds),
function(x) { Rle( baf.ds[,x, "mbaf"] ) },
USE.NAMES=TRUE, simplify=FALSE ) )
as.numeric(object.size( baf.ds[, ,"mbaf"])) / as.numeric( object.size(mbaf.data))## [1] 3.643115Using the HOM SNP calls from the matched normal works much better. A matrix of genotypes can be used to set the HOM SNPs to NA. A list of sample names matches the columns of the genotypes to the columns of your baf matrix. The names of the list should match column names in your baf matrix and the values of the list should match the column names in your genotype matrix. If this method is used and some columns in your baf matrix do not have an entry in this list, then those baf columns are cleaned of HOMs using the hom.cutoff, as above.
Both mBAF and LRR can and should be segmented. Consider storing mBAF as an RleDataFrame as only the ~1/1000 HET positions are being used and all those NA HOM positions will compress nicely.
baf.ds[,,"baf.segs"] = runCBS( baf.ds[, ,"mbaf"], rowRanges(baf.ds) )## Working on segmentation for sample number 1 : K
## Working on segmentation for sample number 2 : L
## Working on segmentation for sample number 3 : Mbaf.ds[,,"lrr.segs"] = runCBS( baf.ds[, , "lrr"], rowRanges(baf.ds) )## Working on segmentation for sample number 1 : K
## Working on segmentation for sample number 2 : L
## Working on segmentation for sample number 3 : M2.6 Plots
genoPlot(baf.ds,baf.ds[,1,"lrr"],chr="chr12",main="LRR of chr12")
genoPlot(baf.ds,baf.ds[,1,"lrr.segs"],chr="chr12",add=TRUE,col="red")
Segmented copy number across the genome for 1st sample`
par(mfrow=c(2,1))
genoPlot(baf.ds,baf.ds[,1,"baf"],chr="chr12", main="BAF of chr12")
genoPlot(baf.ds,baf.ds[,1,"mbaf"],chr="chr12", main="mBAF of chr12")
genoPlot(baf.ds,baf.ds[,1,"baf.segs"],chr="chr12", add=TRUE,col="red")
3 Cross-sample summaries
You can quickly calculate summaries across samples to identify regions
with frequent alterations. A bit more care is necessary to work one
sample at a time if your data “matrix” is an RleDataFrame.
gain.list = lapply(colnames(baf.ds),
function(sample.name) {
as.logical( baf.ds[, sample.name, "lrr.segs"] > 0.3 )
})
gain.mat = do.call(cbind, gain.list)
gain.freq = rowMeans(gain.mat,na.rm=TRUE)GISTIC (by Behroukhim and Getz of the Broad Institute) is the standard
method for assessing significance of such summaries. You’ll find
segTable convenient for getting your data formatted for input. I
find it convenient to load GISTIC output as a GRanges for
intersection with gene locations.
4 Big Data and bigmemoryExtras
Genome-scale data can be huge and keeping everything in memory can get
you into trouble quickly, especially if you like using parallel’s mclapply.
It is often convenient to use BigMatrix objects from the bigmemoryExtras
package as assayDataElements, rather than base matrices. BigMatrix is
based on the bigmemory package, which provides a matrix API to
memory-mapped files of numeric data. This allows for data matrices
larger than R’s maximum size with just the tiniest footprint in
RAM. The bigmemoryExtras vignette has more details about using eSet-derived
classes and BigMatrix objects.