| Title: | fastseg - a fast segmentation algorithm |
|---|---|
| Description: | fastseg implements a very fast and efficient segmentation algorithm. It has similar functionality as DNACopy (Olshen and Venkatraman 2004), but is considerably faster and more flexible. fastseg can segment data from DNA microarrays and data from next generation sequencing for example to detect copy number segments. Further it can segment data 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 2001). The speed-up arises 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. |
| Authors: | Guenter Klambauer [aut], Sonali Kumari [ctb], Alexander Blume [cre] |
| Maintainer: | Alexander Blume <[email protected]> |
| License: | LGPL (>= 2.0) |
| Version: | 1.53.2 |
| Built: | 2024-11-23 02:59:58 UTC |
| Source: | https://github.com/bioc/fastseg |
These are two data array CGH studies sets of Corriel cell lines taken from the reference below.
A data frame containing five variables: first is clone name, second is clone chromosome, third is clone position, fourth and fifth are log2ratio for two cell lines.
http://www.nature.com/ng/journal/v29/n3/suppinfo/ng754\_S1.html
Snijders et al., Assembly of microarrays for genome-wide measurement of DNA copy number, Nature Genetics, 2001
Detection of breakpoints using a fast segmentation algorithm based on the cyber t-test.
fastseg(x, type = 1, alpha = 0.05, segMedianT, minSeg = 4, eps = 0, delta = 5, maxInt = 40, squashing = 0, cyberWeight = 10)fastseg(x, type = 1, alpha = 0.05, segMedianT, minSeg = 4, eps = 0, delta = 5, maxInt = 40, squashing = 0, cyberWeight = 10)
x |
Values to be segmented either in the format of a sorted GRanges object, ExpressionSet object, matrix or vector. |
type |
Parameter that sets the type of test. If set to 1 a test of the left against the right window is performend. If set to 2 the segment is also tested against the global mean. (Default = 1). |
alpha |
A value between 0 and 1 is interpreted as the ratio of initial breakpoints. An integer greater than one is interpreted as number of desired breakpoints. Increasing this parameter leads to more segments. (Default = 0.1) |
segMedianT |
A numeric vector of length two with the thresholds of segments' median values that are considered as significant. Only segments with a median above the first or below the second value are kept in a final merging step. If missing the algorithm will try to find a reasonable value by using z-scores. (Default "missing".) |
minSeg |
The minimal segment length. (Default = 4). |
eps |
Minimal distance between consecutive values. Only consecutive values with a minimium distance of "eps" are tested. This makes the segmentation algorithm even faster. If all values should be tested "eps" can be set to zero. If missing the algorithm will try to find a reasonable value by using quantiles. (Default = 0.) |
delta |
Segment extension parameter. If delta consecutive extensions of the left and the right segment do not lead to a better p-value the testing is stopped. (Default = 5). |
maxInt |
Maximal length of the left and the right segment. (Default = 40). |
squashing |
The degree of squashing of the input values. If set to zero no squashing is performed. (Default = 0). |
cyberWeight |
The nu parameter of the cyber t-test. Can be interpreted as the weight of the global variance. The higher the value the more small segments with high variance will be significant. (Default = 10). |
A data frame containing the segments.
Guenter Klambauer [email protected]
library(fastseg) ##################################################################### ### the data ##################################################################### data(coriell) head(coriell) samplenames <- colnames(coriell)[4:5] data <- as.matrix(coriell[4:5]) data[is.na(data)] <- median(data, na.rm=TRUE) chrom <- coriell$Chromosome maploc <- coriell$Position ########################################################### ## GRanges ########################################################### library("GenomicRanges") ## with both individuals gr <- GRanges(seqnames=chrom, ranges=IRanges(maploc, end=maploc)) mcols(gr) <- data colnames(mcols(gr)) <- samplenames res <- fastseg(gr) ## with one individual gr2 <- gr data2 <- as.matrix(data[, 1]) colnames(data2) <- "sample1" mcols(gr2) <- data2 res <- fastseg(gr2) ########################################################### ## vector ########################################################### data2 <- data[, 1] res <- fastseg(data2) ########################################################### ## matrix ########################################################### data2 <- data[1:400, ] res <- fastseg(data2)library(fastseg) ##################################################################### ### the data ##################################################################### data(coriell) head(coriell) samplenames <- colnames(coriell)[4:5] data <- as.matrix(coriell[4:5]) data[is.na(data)] <- median(data, na.rm=TRUE) chrom <- coriell$Chromosome maploc <- coriell$Position ########################################################### ## GRanges ########################################################### library("GenomicRanges") ## with both individuals gr <- GRanges(seqnames=chrom, ranges=IRanges(maploc, end=maploc)) mcols(gr) <- data colnames(mcols(gr)) <- samplenames res <- fastseg(gr) ## with one individual gr2 <- gr data2 <- as.matrix(data[, 1]) colnames(data2) <- "sample1" mcols(gr2) <- data2 res <- fastseg(gr2) ########################################################### ## vector ########################################################### data2 <- data[, 1] res <- fastseg(data2) ########################################################### ## matrix ########################################################### data2 <- data[1:400, ] res <- fastseg(data2)
The data is a small subset of copy number calls which were produced by the cn.farms algorithm from an Affymetrix SNP microarray experiment of a HapMap sample.
A simple vector with a copy number call as produced by the cn.farms algorithm.
http://nar.oxfordjournals.org/content/early/2011/04/12/nar.gkr197.abstract Clevert et al., cn.FARMS: a latent variable model to detect copy number variations in microarray data with a low false discovery rate, NAR, 2011
Plots the data from a copy number array experiment (aCGH, ROMA etc.) along with the results of segmenting it into regions of equal copy numbers.
segPlot(x, res, plot.type = "chrombysample", altcol = TRUE, sbyc.layout = NULL, cbys.nchrom = 1, cbys.layout = NULL, include.means = TRUE, zeroline = TRUE, pt.pch = NULL, pt.cex = NULL, pt.cols = NULL, segcol = NULL, zlcol = NULL, ylim = NULL, lwd = NULL, ...)segPlot(x, res, plot.type = "chrombysample", altcol = TRUE, sbyc.layout = NULL, cbys.nchrom = 1, cbys.layout = NULL, include.means = TRUE, zeroline = TRUE, pt.pch = NULL, pt.cex = NULL, pt.cols = NULL, segcol = NULL, zlcol = NULL, ylim = NULL, lwd = NULL, ...)
x |
The object that was segmented by fastseg. |
res |
The result of fastseg. |
plot.type |
the type of plot. (Default = "s"). |
altcol |
logical flag to indicate if chromosomes should be plotted in alternating colors in the whole genome plot. (Default = TRUE). |
sbyc.layout |
|
cbys.layout |
|
cbys.nchrom |
the number of chromosomes per page in the layout. (Default = 1). |
include.means |
logical flag to indicate whether segment means are to be drawn. (Default = TRUE). |
zeroline |
logical flag to indicate whether a horizontal line at y=0 is to be drawn. (Default = TRUE). |
pt.pch |
the plotting character used for plotting the log-ratio values. (Default = ".") |
pt.cex |
the size of plotting character used for the log-ratio values (Default = 3). |
pt.cols |
the color list for the points. The colors alternate between chromosomes. (Default = c("green","black"). |
segcol |
the color of the lines indicating the segment means. (Default = "red"). |
zlcol |
the color of the zeroline. (Default = "grey"). |
ylim |
this argument is present to override the default limits which is the range of symmetrized log-ratios. (Default = NULL). |
lwd |
line weight of lines for segment mean and zeroline. (Default = 3). |
... |
other arguments which will be passed to
|
A plot of the values and segments.
klambaue
data(coriell) head(coriell) samplenames <- colnames(coriell)[4:5] data <- as.matrix(coriell[4:5]) chrom <- coriell$Chromosome maploc <- coriell$Position library("GenomicRanges") gr <- GRanges(seqnames=chrom, ranges=IRanges(maploc, end=maploc)) mcols(gr) <- data colnames(mcols(gr)) <- samplenames res <- fastseg(gr) segPlot(gr,res)data(coriell) head(coriell) samplenames <- colnames(coriell)[4:5] data <- as.matrix(coriell[4:5]) chrom <- coriell$Chromosome maploc <- coriell$Position library("GenomicRanges") gr <- GRanges(seqnames=chrom, ranges=IRanges(maploc, end=maploc)) mcols(gr) <- data colnames(mcols(gr)) <- samplenames res <- fastseg(gr) segPlot(gr,res)
Function to create a DNAcopy object for plot functions.
toDNAcopyObj(segData, chrom, maploc, genomdat, sampleNames)toDNAcopyObj(segData, chrom, maploc, genomdat, sampleNames)
segData |
The results of the segmentation. |
chrom |
The vector of the chromosomes from the original data. |
maploc |
A vector with the physical positions of the original data. |
genomdat |
A matrix with the original data. |
sampleNames |
The sample names of the original data. |
An DNAcopy equivalent object.
Andreas Mitterecker
library(fastseg) ##################################################################### ### the data ##################################################################### data(coriell) head(coriell) samplenames <- colnames(coriell)[4:5] data <- as.matrix(coriell[4:5]) data[is.na(data)] <- median(data, na.rm=TRUE) chrom <- coriell$Chromosome maploc <- coriell$Position ########################################################### ## GRanges ########################################################### library("GenomicRanges") ## with both individuals gr <- GRanges(seqnames=chrom, ranges=IRanges(maploc, end=maploc)) mcols(gr) <- data colnames(mcols(gr)) <- samplenames res <- fastseg(gr) segres <- toDNAcopyObj( segData = res, chrom = as.character(seqnames(gr)), maploc = as.numeric(start(gr)), genomdat = data, sampleNames = samplenames) ## with one individual gr2 <- gr data2 <- as.matrix(data[, 1]) colnames(data2) <- "sample1" mcols(gr2) <- data2 res <- fastseg(gr2) segres <- toDNAcopyObj( segData = res, chrom = as.character(seqnames(gr)), maploc = as.numeric(start(gr)), genomdat = as.matrix(data2), sampleNames = unique(mcols(res)$ID)) ########################################################### ## vector ########################################################### data2 <- data[, 1] res <- fastseg(data2) segres <- toDNAcopyObj( segData = res, chrom = rep(1, length(data2)), maploc = 1:length(data2), genomdat = as.matrix(data2), sampleNames = "sample1") ########################################################### ## matrix ########################################################### data2 <- data[1:400, ] res <- fastseg(data2) segres <- toDNAcopyObj( segData = res, chrom = rep(1, nrow(data2)), maploc = 1:nrow(data2), genomdat = as.matrix(data2), sampleNames = colnames(data2)) ##################################################################### ### plot the segments ##################################################################### library(DNAcopy) plot(segres)library(fastseg) ##################################################################### ### the data ##################################################################### data(coriell) head(coriell) samplenames <- colnames(coriell)[4:5] data <- as.matrix(coriell[4:5]) data[is.na(data)] <- median(data, na.rm=TRUE) chrom <- coriell$Chromosome maploc <- coriell$Position ########################################################### ## GRanges ########################################################### library("GenomicRanges") ## with both individuals gr <- GRanges(seqnames=chrom, ranges=IRanges(maploc, end=maploc)) mcols(gr) <- data colnames(mcols(gr)) <- samplenames res <- fastseg(gr) segres <- toDNAcopyObj( segData = res, chrom = as.character(seqnames(gr)), maploc = as.numeric(start(gr)), genomdat = data, sampleNames = samplenames) ## with one individual gr2 <- gr data2 <- as.matrix(data[, 1]) colnames(data2) <- "sample1" mcols(gr2) <- data2 res <- fastseg(gr2) segres <- toDNAcopyObj( segData = res, chrom = as.character(seqnames(gr)), maploc = as.numeric(start(gr)), genomdat = as.matrix(data2), sampleNames = unique(mcols(res)$ID)) ########################################################### ## vector ########################################################### data2 <- data[, 1] res <- fastseg(data2) segres <- toDNAcopyObj( segData = res, chrom = rep(1, length(data2)), maploc = 1:length(data2), genomdat = as.matrix(data2), sampleNames = "sample1") ########################################################### ## matrix ########################################################### data2 <- data[1:400, ] res <- fastseg(data2) segres <- toDNAcopyObj( segData = res, chrom = rep(1, nrow(data2)), maploc = 1:nrow(data2), genomdat = as.matrix(data2), sampleNames = colnames(data2)) ##################################################################### ### plot the segments ##################################################################### library(DNAcopy) plot(segres)