An R Package for fast segmentation
April 30 , 2024
Abstract
Scope and Purpose of this Document
This document is a user manual for the R package fastseg. It is only meant as a gentle introduction into how to use the basic functions implemented in this package. Not all features of the R package are described in full detail. Such details can be obtained from the documentation enclosed in the R package. Further note the following: (1) this is neither an introduction to segmentation algorithms; (2) this is not an introduction to R. If you lack the background for understanding this manual, you first have to read introductory literature on these subjects.
Package
fastseg 1.50.0
Contents
1 Introduction
fastseg implements a very fast and efficient segmentation algorithm. It has similar functionality as DNAcopy (Olshen et al., 2004) but is considerably faster and more flexible. fastseg can segment data stemming from DNA microarrays and data stemming from next generation sequencing for example to detect copy number segments. Further it can segment data stemming from RNA microarrays like tiling arrays to identify transcripts. Most generally, it can segment data given as a matrix or as a vector. Various data formats can be used as input to fastseg like expression set objects for microarrays or GRanges for sequencing data.
The segmentation criterion of fastseg is based on a statistical test in a Bayesian framework, namely the cyber t-test (Baldi and Long, 2001). The speed-up stems from the facts, that sampling is not necessary in for fastseg and that a dynamic programming approach is used for calculation of the segments’ first and higher order moments.
For further information regarding the algorithm and its assessment see the fastseg homepage at http://www.bioinf.jku.at/software/fastseg/index.html
2 Getting started
To load the package, enter the following in your R session:
library(fastseg)2.1 Data
According to the DNAcopy package from bioconductor we selected a
subset of the data set presented in (Snijders et al., 2001). This data set will be called
coriell. The data correspond to two array CGH studies of fibroblast cell
strains.1 http://www.nature.com/ng/journal/v29/n3/suppinfo/ng754_S1.html In particular, the studies GM05296 and GM13330 were
chosen. After selecting only the mapped data from chromosomes 1-22 and X, there
are 2271 data points.
To prepare the data for our examples we execute the following code:
data(coriell)
head(coriell)## Clone Chromosome Position Coriell.05296 Coriell.13330
## 1 GS1-232B23 1 1 0.000359 0.207470
## 2 RP11-82d16 1 469 0.008824 0.063076
## 3 RP11-62m23 1 2242 -0.000890 0.123881
## 4 RP11-60j11 1 4505 0.075875 0.154343
## 5 RP11-111O05 1 5441 0.017303 -0.043890
## 6 RP11-51b04 1 7001 -0.006770 0.094144samplenames <- colnames(coriell)[4:5]
data <- as.matrix(coriell[4:5])
#data[is.na(data)] <- median(data, na.rm=TRUE)
chrom <- coriell$Chromosome
maploc <- coriell$PositionThe main functions of the package are fastseg and toDNAcopyObj. The first on
runs the segmentation algorithm and the latter converts the segmentation results
the a DNAcopy object which will be quite helpful for plot
functions.
2.2 File formats
The package can handle different file formats: GRanges, ExpressionSet objects, matrix or a vector.
2.2.1 GRanges objects
library("GenomicRanges")
## with both individuals
gr <- GRanges(seqnames=chrom,
ranges=IRanges(maploc, end=maploc))
mcols(gr) <- data
colnames(mcols(gr)) <- samplenames
res <- fastseg(gr)
head(res)## GRanges object with 6 ranges and 5 metadata columns:
## seqnames ranges strand | ID num.mark seg.mean
## <Rle> <IRanges> <Rle> | <character> <integer> <numeric>
## [1] 1 1-240001 * | Coriell.05296 141 0.0197312
## [2] 10 1-65001 * | Coriell.05296 57 -0.0106129
## [3] 10 66906-108904 * | Coriell.05296 43 0.4516093
## [4] 10 110001-142001 * | Coriell.05296 34 0.0040314
## [5] 11 1-34421 * | Coriell.05296 52 0.0116384
## [6] 11 35417-39624 * | Coriell.05296 14 -0.6510813
## startRow endRow
## <integer> <integer>
## [1] 1 142
## [2] 1 58
## [3] 59 102
## [4] 103 137
## [5] 1 53
## [6] 54 68
## -------
## seqinfo: 23 sequences from an unspecified genome; no seqlengths## with one individual
gr2 <- gr
data2 <- as.matrix(data[, 1])
colnames(data2) <- "sample1"
mcols(gr2) <- data2
res <- fastseg(gr2)
head(res)## GRanges object with 6 ranges and 5 metadata columns:
## seqnames ranges strand | ID num.mark seg.mean
## <Rle> <IRanges> <Rle> | <character> <integer> <numeric>
## [1] 1 1-240001 * | sample1 141 0.0197312
## [2] 10 1-65001 * | sample1 57 -0.0106129
## [3] 10 66906-108904 * | sample1 43 0.4516093
## [4] 10 110001-142001 * | sample1 34 0.0040314
## [5] 11 1-34421 * | sample1 52 0.0116384
## [6] 11 35417-39624 * | sample1 14 -0.6510813
## startRow endRow
## <integer> <integer>
## [1] 1 142
## [2] 1 58
## [3] 59 102
## [4] 103 137
## [5] 1 53
## [6] 54 68
## -------
## seqinfo: 23 sequences from an unspecified genome; no seqlengths2.2.2 ExpressionSet objects
library(oligo)
eSet <- new("ExpressionSet")
assayData(eSet) <- list(intensity=data)
featureData(eSet) <- new("AnnotatedDataFrame",
data=data.frame(
chrom = paste("chr",chrom,sep=""),
start = maploc,
end = maploc,stringsAsFactors=FALSE))
phenoData(eSet) <- new("AnnotatedDataFrame",
data=data.frame(samples=samplenames))
sampleNames(eSet) <- samplenames
res <- fastseg(eSet)
head(res)## GRanges object with 6 ranges and 5 metadata columns:
## seqnames ranges strand | ID num.mark seg.mean
## <Rle> <IRanges> <Rle> | <character> <integer> <numeric>
## [1] chr1 1-240001 * | Coriell.05296 141 0.0197312
## [2] chr10 1-65001 * | Coriell.05296 57 -0.0106129
## [3] chr10 66906-108904 * | Coriell.05296 43 0.4516093
## [4] chr10 110001-142001 * | Coriell.05296 34 0.0040314
## [5] chr11 1-34421 * | Coriell.05296 52 0.0116384
## [6] chr11 35417-39624 * | Coriell.05296 14 -0.6510813
## startRow endRow
## <integer> <integer>
## [1] 1 142
## [2] 1 58
## [3] 59 102
## [4] 103 137
## [5] 1 53
## [6] 54 68
## -------
## seqinfo: 23 sequences from an unspecified genome; no seqlengths2.2.3 Vector
data2 <- data[, 1]
res <- fastseg(data2)
head(res)## GRanges object with 6 ranges and 5 metadata columns:
## seqnames ranges strand | ID num.mark seg.mean startRow
## <Rle> <IRanges> <Rle> | <character> <numeric> <numeric> <integer>
## [1] 1 1-1227 * | sample1 1227 -0.00360432 1
## [2] 1 1228-1270 * | sample1 43 0.46162281 1228
## [3] 1 1271-1357 * | sample1 87 0.00431766 1271
## [4] 1 1358-1372 * | sample1 15 -0.65108133 1358
## [5] 1 1373-2214 * | sample1 842 0.01498080 1373
## [6] 1 2215-2271 * | sample1 57 0.61411642 2215
## endRow
## <integer>
## [1] 1227
## [2] 1270
## [3] 1357
## [4] 1372
## [5] 2214
## [6] 2271
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths2.2.4 Matrix
data2 <- data[1:400, ]
res <- fastseg(data2)
head(res)## GRanges object with 6 ranges and 5 metadata columns:
## seqnames ranges strand | ID num.mark seg.mean startRow
## <Rle> <IRanges> <Rle> | <character> <numeric> <numeric> <integer>
## [1] 1 1-80 * | Coriell.05296 80 0.01681567 1
## [2] 1 81-84 * | Coriell.05296 4 0.13437475 81
## [3] 1 85-400 * | Coriell.05296 316 0.00326755 85
## [4] 1 1-91 * | Coriell.13330 91 0.01615064 1
## [5] 1 92-140 * | Coriell.13330 49 0.48524367 92
## [6] 1 141-400 * | Coriell.13330 260 -0.03233950 141
## endRow
## <integer>
## [1] 80
## [2] 84
## [3] 400
## [4] 91
## [5] 140
## [6] 400
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths2.3 Plotting the segmentation results
For plotting the data we have to generate an DNAcopy object out of the segmentation results:
## with both individuals
gr <- GRanges(seqnames=chrom,
ranges=IRanges(maploc, end=maploc))
mcols(gr) <- data
colnames(mcols(gr)) <- samplenames
res <- fastseg(gr,segMedianT=0.2)The plotting is done via the plot function of DNAcopy:
segPlot(gr,res, plot.type="w")
Or alternatively:
segPlot(gr, res, plot.type="s")
2.4 Performance of the method
Here we show that fastseg outperforms DNAcopy with respect to computational time on summarized microarray data. The quality of the segmentation result of both fastseg and DNAcopy depends strongly on the methods’ parameters.
The data is a small subset of copy number calls which were produced by the
cn.farms algorithm Clevert et al. (2011) from an Affymetrix SNP microarray experiment of
a HapMap sample.
data(fastsegData)
system.time(res <- fastseg(fastsegData))## user system elapsed
## 0.199 0.000 0.198segPlot(fastsegData,res, plot.type="w")
library(DNAcopy)
cna <- DNAcopy::CNA(fastsegData,chrom="chr1",maploc=1:length(fastsegData))
system.time(res2 <- DNAcopy::segment(cna))## Analyzing: Sample.1## user system elapsed
## 3.814 0.007 4.036plot(res2, plot.type="w", xmaploc=TRUE)
3 Future Extensions
We are planning to program a parallelized version of this package. Furthermore we will enhance the plot functions by our own.
4 How to cite this package
If you use this package for research that is published later, you are kindly asked to cite it as follows: (Klambauer et al., 2011).
To obtain BibTeX entries of the two references, you can enter the following into your R session:
toBibtex(citation("fastseg"))References
Baldi,P. and Long,A.D. (2001) A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes. Bioinformatics, 17, 509–519.
Clevert,D.-A. et al. (2011) cn.FARMS: a latent variable model to detect copy number variations in microarray data with a low false discovery rate. Nucleic Acids Res., 39, e79.
Klambauer,G. et al. (2011) fastseg: a fast segmentation algorithm. Unknown, 99, 99–99.
Olshen,A.B. et al. (2004) Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics, 5, 557–72.
Snijders,A.M. et al. (2001) Assembly of microarrays for genome-wide measurement of DNA copy number. Nat. Genet., 29, 263–4.