| Title: | Data and analysis scripts for David, Huber et al. yeast tiling array paper |
|---|---|
| Description: | This package contains the data for the paper by L. David et al. in PNAS 2006 (PMID 16569694): 8 CEL files of Affymetrix genechips, an ExpressionSet object with the raw feature data, a probe annotation data structure for the chip and the yeast genome annotation (GFF file) that was used. In addition, some custom-written analysis functions are provided, as well as R scripts in the scripts directory. |
| Authors: | Wolfgang Huber <[email protected]>, Joern Toedling <[email protected]> |
| Maintainer: | Wolfgang Huber <[email protected]> |
| License: | LGPL |
| Version: | 1.52.0 |
| Built: | 2026-10-10 11:03:08 UTC |
| Source: | https://github.com/bioc/davidTiling |
The data are from an experiment that used Affymetrix Scerevisiaetiling chips from 2004, which where custom-made for the Stanford Genome Center. The chips tile the complete genome of S. cerevisiae in steps of 8 bases, separately for each strand of each chromosome. The two tiles for one chromosome (Watson and Crick strands) are offset by 4 bases.
Note that the class eSet was used instead of
AffyBatch since the additional
overhead of 'CDF environments' in the latter is not needed here.
data("davidTiling")data("davidTiling")
Intensity data for 8 arrays. The
phenoData slot contains the file names
and the nucleic acid type.
W. Huber [email protected]
Lior David and Lars Steinmetz, both from the Stanford Genome Center. Lars Steinmetz is also at EMBL Heidelberg.
data("davidTiling") dim(exprs(davidTiling))data("davidTiling") dim(exprs(davidTiling))
A data frame with genomic features of Saccharomyces cerevisiae.
data("gff")data("gff")
Object of class data.frame.
GFF is a file format for annotating genomes, see <insert the URL to
the documentation page for GFF at Sanger here>.
The format is essentially a rectangular table, and here
it is represented as a data frame.
W. Huber [email protected]
Two GFF files were downloaded:
saccharomyces_cerevisiae.gff from
ftp://genome-ftp.stanford.edu/pub/yeast/data_download/chromosomal_feature
on 7 Aug 2005, 18:16 BST, and
IGR_v24.2.p001.allowoverlap.GFF from
http://jura.wi.mit.edu/fraenkel/download/release_v24/GFF
upon suggestion from the SGD curators on 30 Aug 2005. (Future versions of
SGD's GFF files are likely to include the latter as well).
They were parsed, combined and written into the
gff data.frame with the script
makeProbeAnno.R in the inst/scripts directory of this package.
data("gff") str(gff)data("gff") str(gff)
The environment contains probe mapping information for the Affymetrix Scerevisiaetiling chip from 2004, which was custom-made for the Stanford Genome Center. The chips tile the complete genome of S. cerevisiae in steps of 8 bases, separately for each strand of each chromosome. The two tiles for one chromosome (Watson and Crick strands) are offset by 4 bases.
In the following a brief description of the 138 elements of the
probeAnno environment.
probeReverse: a list of 8 factors, each of length 6553600,
corresponding to the rows of davidTiling. For
example, if the probe corresponding to the j-th row
in davidTiling maps to the
coding sequence of a gene, then the factor level of
probeRevers$CDS[j] is the name of the gene, and the empty string
"" otherwise. This applies to samples that were hybridized to
the chip after a reverse transcription step.
probeDirect: analogous to probeReverse, but for samples
that were hybridized to the chip without a reverse transcription step.
The probes map to the opposite chromosomal strand compared to
experiments with reverse transcription.
1.+.index: indices (from 1...6553600, corresponding to the
rows of davidTiling) of probes mapping to the Watson
strand of chromosome 1.
1.+.start, 1.+.end: start and end positions in genomic
coordinates of the alignments of the probes (in the same order as in
1.+.index) to the Watson strand of chromosome 1. For 25-mers,
the values in 1.+.end are those in 1.+.start plus 24, but
not all probes on the array are 25-mers.
1.-.unique: specificity of the probe:
has exactly one perfect match (PM) and no near-matches in the genome
has exactly one PM and some near-matches
has no PM but one or more near-matches
has multiple PMs in the genome
1.-.index, 1.-.start, 1.-.end, 1.-.unique:
analogous to the above, but for the Crick strand of chromosome 1.
2.+.index, 2.+.start, 2.+.end, 2.-.unique:
analogous to the above, but for the Watson strand of chromosome 2;
and so forth. "Chromosome 17" is mitochondrial DNA.
data("probeAnno")data("probeAnno")
W. Huber [email protected]
Probe sequences were obtained from Affymetrix in a file called
S.cerevisiae_tiling.1lq.
The genomic sequences of the S. cerevisiae chromosomes were
downloaded from
ftp://genome-ftp.stanford.edu/pub/yeast/data_download/sequence/genomic_sequence/chromosomes/fasta
on 7 Aug 2005, 18:16 BST in 17 files
chr01.fsa–chr16.fsa, and chrmt.fsa.
The probe sequences were matched against the chromosomal sequencences
with the program MUMmer, see the script
mapProbesToGenome.sh (in the inst/scripts directory of this
package). MUMmer results were parsed and processed into the
probeAnno environment with the script
makeProbeAnno.R (in the inst/scripts directory of this package).
data("probeAnno") ls(probeAnno) str(probeAnno$"1.+.start")data("probeAnno") ls(probeAnno) str(probeAnno$"1.+.start")
The function uses the GO categories in the data.frame gff to
obtain annotated GO categories, then the GO**ANCESTOR data in the
GO package to add all parent terms as well.
getAllGO(x, gff)getAllGO(x, gff)
x |
character vector. |
gff |
data.frame with columns |
The elements of x are matched against the column
gene in gff. All are required to match.
A list of GO terms is then extracted from the corresponding
rows in the Ontology_term column. A gene may be annotated by
several terms, separated by ",". Then the GO package is used to
augment this by all ancestor terms.
List of character vectors.
W. Huber <[email protected]>
Extract the value of a certain field out of a character vector such as in the "attributes" column of a GFF table.
getAttributeField(x, field, attrsep=";")getAttributeField(x, field, attrsep=";")
x |
character vector. |
field |
character vector of length 1, containing the field name. |
attrsep |
character vector of length 1, containing the separator name. |
See example.
Character vector.
W. Huber <[email protected]>
acol = c("ID=46891;Name=TEL01L-TR;Note=Bla", "ID=46892;Name=TEL01L;Note=Di", "ID=46893;Name=TEL01L-XR;Note=Bla") getAttributeField(acol, "Name") getAttributeField(acol, "ID")acol = c("ID=46891;Name=TEL01L-TR;Note=Bla", "ID=46892;Name=TEL01L;Note=Di", "ID=46893;Name=TEL01L-XR;Note=Bla") getAttributeField(acol, "Name") getAttributeField(acol, "ID")
This function is adapted from the function of the same name in
GOstats package. Main difference is that it draws the GO
annotations of the genes in candidates from the data.frame
gff. It also draws a plot.
GOHyperG(candidates, gff, plotmain)GOHyperG(candidates, gff, plotmain)
candidates |
character vector |
gff |
see |
plotmain |
character, plot title |
The elements of x are matched against the column
gene in gff. All are required to match.
A list of GO terms is then extracted from the corresponding
rows in the Ontology_term column. A gene may be annotated by
several terms, separated by ",". Then the GO package is used to
augment this by all ancestor terms.
List of character vectors.
W. Huber <[email protected]>
Scatterplot with histograms of marginal distributions.
scatterWithHist(x, breaks, barcols, xlab, ylab, ...)scatterWithHist(x, breaks, barcols, xlab, ylab, ...)
x |
numeric matrix with 2 columns. |
breaks |
numeric vector with histogram breaks, see |
barcols |
character vector of length 2, colors for the histogram filling. |
xlab |
characterof length 1, label for x-axis. |
ylab |
characterof length 1, label for y-axis. |
... |
further arguments that get passed on to |
The function is called for its side effect.
W. Huber <[email protected]>
x = rexp(100) x = cbind(x, x+0.6*rnorm(length(x))) scatterWithHist(x, breaks=seq(min(x),max(x),length=20), barcols=c("mistyrose", "lightblue"), xlab="Daffodil", ylab="Petunia", pch=16)x = rexp(100) x = cbind(x, x+0.6*rnorm(length(x))) scatterWithHist(x, breaks=seq(min(x),max(x),length=20), barcols=c("mistyrose", "lightblue"), xlab="Daffodil", ylab="Petunia", pch=16)
Score the segments found by a previous call to findSegments by comparing to genome annotation
scoreSegments(s, gff, nrBasePerSeg = 1500, probeLength = 25, params = c(overlapFraction = 0.5, oppositeWindow = 100, flankProbes=10), verbose = TRUE)scoreSegments(s, gff, nrBasePerSeg = 1500, probeLength = 25, params = c(overlapFraction = 0.5, oppositeWindow = 100, flankProbes=10), verbose = TRUE)
s |
environment. See details. |
gff |
GFF dataframe. |
nrBasePerSeg |
Numeric of length 1. This parameter determines the number of segments. |
probeLength |
Numeric of length 1. |
params |
vector of additional parameters, see details. |
verbose |
Logical. |
This function scores segments.
It is typically called after a segmentation.
For an example segmentation script, see the script segment.R
in the scripts directory of this package. For an example
scoring script, which loads the data and then calls this function, see
the script scoreSegments.R.
A dataframe with columns as described in the details section.
W. Huber <[email protected]>
Plot function for more than one density
showDens(z, breaks, xat, xtickLabels=paste(xat), col, ylab = "", ...)showDens(z, breaks, xat, xtickLabels=paste(xat), col, ylab = "", ...)
z |
List: numeric vectors for computing histograms for |
breaks |
Numeric vector: breaks of the histogram |
xat |
Numeric vector: where to put the x-axis ticks |
xtickLabels |
Character vector: what to write underneath them |
col |
Character vector: colours of the histograms |
ylab |
Character scalar: y-axis label |
... |
futher arguments passed on to plot |
...
returns scale factor
Wolfgang Huber [email protected]
showDens(list(x1=runif(100), x2=exp(runif(50))-1, x3=runif(20)), breaks=seq(0, 2, 0.2), xat=seq(0, 2, 0.5), col=rainbow(3), xlab="Random Numbers")showDens(list(x1=runif(100), x2=exp(runif(50))-1, x3=runif(20)), breaks=seq(0, 2, 0.2), xat=seq(0, 2, 0.5), col=rainbow(3), xlab="Random Numbers")
The rows of the data frame correspond to feature
categories, such as "gene", "CDS", "telomere". The
column isTranscribed is a logical vector that denotes whether
this feature category is considered to be transcribable.
data(yeastFeatures)data(yeastFeatures)
A data.frame
W. Huber <[email protected]>
data(yeastFeatures)data(yeastFeatures)