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PLoS Comput Biol. 2008 November; 4(11): e1000227. Published online 2008 November 28. doi: 10.1371/journal.pcbi.1000227. | PMCID: PMC2582686 |
Copyright Toedling, Huber. This is an open-access article distributed under
the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and
source are credited. Analyzing ChIP-chip Data Using Bioconductor Joern Toedling* and Wolfgang Huber EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus,
Hinxton, United Kingdom Fran Lewitter, Editor Whitehead Institute, United States of America ChIP-chip, chromatin immunoprecipitation combined with DNA microarrays, is a widely
used assay for DNA–protein binding and chromatin plasticity, which are of
fundamental interest for the understanding of gene regulation. The interpretation of ChIP-chip data poses two computational challenges: first, what
can be termed primary statistical analysis, which includes quality assessment, data
normalization and transformation, and the calling of regions of interest; second,
integrative bioinformatic analysis, which interprets the data in the context of
existing genome annotation and of related experimental results obtained, for
example, from other ChIP-chip or (m)RNA abundance microarray experiments. Both tasks rely heavily on visualization, which helps to explore the data as well as
to present the analysis results. For the primary statistical analysis, some
standardization is possible and desirable: commonly used experimental designs and
microarray platforms allow the development of relatively standard workflows and
statistical procedures. Most software available for ChIP-chip data analysis can be
employed in such standardized approaches [1]– [6]. Yet even
for primary analysis steps, it may be beneficial to adapt them to specific
experiments, and hence it is desirable that software offers flexibility in the
choice of algorithms for normalization, visualization, and identification of
enriched regions. For the second task, integrative bioinformatic analysis, the datasets, questions, and
applicable methods are diverse, and a degree of flexibility is needed that often can
only be achieved in a programmable environment. In such an environment, users are
not limited to predefined functions, such as the ones made available as
“buttons” in a GUI, but can supply custom functions that are
designed toward the analysis at hand. Bioconductor [7] is an open source and open development software
project for the analysis and comprehension of genomic data, and it offers tools that
cover a broad range of computational methods, visualizations, and experimental data
types, and is designed to allow the construction of scalable, reproducible, and
interoperable workflows. A consequence of the wide range of functionality of
Bioconductor and its concurrency with research progress in biology and computational
statistics is that using its tools can be daunting for a new user. Various books
provide a good general introduction to R and Bioconductor (e.g., [8]– [10]), and most Bioconductor
packages are accompanied by extensive documentation. This tutorial covers basic
ChIP-chip data analysis with Bioconductor. Among the packages used are Ringo
[5],
biomaRt
[11], and
topGO
[12]. We wrote this document in the Sweave [13] format, which
combines explanatory text and the actual R source code used in this analysis [14]. Thus,
the analysis can be reproduced by the reader. An R package
ccTutorial that contains the data, the text, and code presented
here, and supplementary text and code, is available from the Bioconductor Web site.
>
library(“Ringo”)
>
library(“biomaRt”)
>
library(“topGO”)
>
library(“ccTutorial”)
Terminology. Reporters are the DNA sequences
fixed to the microarray; they are designed to specifically hybridize with
corresponding genomic fragments from the immunoprecipitate. A reporter has a unique
identifier and a unique sequence, and it can appear in one or multiple
features on the array surface [15]. The
sample is the aliquot of immunoprecipitated or
input DNA that is hybridized to the microarray. We shall call a
genomic region apparently enriched by ChIP a ChIP-enriched region. The data. We consider a ChIP-chip dataset on a post-translational
modification of histone protein H3, namely tri-methylation of its Lysine residue 4,
in short H3K4me3. H3K4me3 has been associated with active transcription (e.g., [16], [17]). Here, enrichment for H3K4me3 was investigated
in Mus musculus brain and heart cells. The microarray platform is a
set of four arrays manufactured by NimbleGen containing 390 k reporters each. The
reporters were designed to tile 32,482 selected regions of the Mus
musculus genome (assembly mm5) with one base every 100 bp, with a different
set of promoters represented on each of the four arrays ( [18], Methods: Condensed
array ChIP-chip). We obtained the data from the GEO repository [19] (accession GSE7688). Importing the Data into R For each microarray, the scanner output consists of two files, one holding the Cy3
intensities (the untreated input sample), the other one the Cy5
intensities, coming from the immunoprecipitated sample. These files are
tab-delimited text files in NimbleGen's pair format. Since
the reporters are distributed over four arrays, we have 16 files (4
microarrays×2 dyes×2 tissues).
> pairDir <-
system.file(“PairData”,package =“ccTutorial”)
> list.files(pairDir,
pattern=“pair$”)
[1] “47101_532.pair”
“47101_635.pair” “48153_532.pair”
“48153_635.pair”
[5] “48158_532.pair”
“48158_635.pair” “48170_532.pair”
“48170_635.pair”
[9] “48175_532.pair”
“48175_635.pair” “48180_532.pair”
“48180_635.pair”
[13] “48182_532.pair”
“48182_635.pair” “49728_532.pair”
“49728_635.pair”
One text file per array describes the samples, including which two
pair files belong to which sample. Another file, spottypes.text,
describes the reporter categories on the arrays. We read in the raw reporter intensities and obtain four objects of class
RGList, a class defined in package limma
[20], one
object per array type.
> RGs <-
lapply(sprintf(“files_array%d.txt”,1:4),
+ readNimblegen, “spottypes.txt”,
path=pairDir)
See Text S1
for an extended description of the data import. In this step, we check the arrays for obvious artifacts and inconsistencies between
array subsets. First, we look at the spatial distribution of the intensities on each array. See
Text S1
for the figure and the source code. We do not see any artifacts such as scratches,
bright spots, or scanning-induced patterns that would render parts of the readouts
useless. On all arrays in our set, the Cy3 channel holds the intensities from the untreated
input sample, and the Cy5 channel holds the immunoprecipitate
from brain and heart, respectively. This experiment setup is reflected in the
reporter intensity correlation per channel (see Text S1). The
correlation between the intensities of the input samples is higher
than between the ChIP samples (0.877 versus 0.734). The Bioconductor package arrayQualityMetrics offers an extensive set
of visualizations and metrics for assessing microarray data quality. Applied to this
dataset, arrayQualityMetrics also indicates that the data are of
good quality. Mapping Reporters to the Genome A mapping of reporters to genomic coordinates is usually provided by the array
manufacturer. Often, however, remapping the reporter sequences to the genome may be
required. Here, the microarray had been designed on an outdated assembly of the
mouse genome (mm5, May 2004). We remapped the reporter sequences to the current
assembly (mm9, July 2007). We used Exonerate
[21] for
the remapping, requiring 97% sequence similarity for a match. See Text S1 for
details and the scripts used. In Ringo, the mapping of reporters to the genome is stored in a
probeAnno class object. Text S1 contains details on its construction.
>
data(“probeAnno”)
> allChrs <-
chromosomeNames(probeAnno)
We want to relate ChIP-enriched regions to annotated genome elements, such as
potential regulatory regions and transcripts. Using the Bioconductor package
biomaRt
[11], we
obtain an up-to-date annotation of the mouse genome from the Ensembl database [22]. The source code for creating the annotation table mm9genes is given in Text S1. The
table holds the coordinates, Ensembl gene identifiers, MGI symbols, and description
of all genes annotated for the mm9 mouse assembly.
>
data(“mm9genes”)
> mm9genes[sample(nrow(mm9genes),
4),
+ c(“name”,
“chr”, “strand”,
“start”, “end”,
“symbol”)]
| Table 1An excerpt of object ‘mm9genes’. |
Moreover, we used biomaRt to retrieve the Gene Ontology (GO) [23]
annotation for all genes in the table. Find the source code and further details in
Text S1.
>
data(“mm9.gene2GO”)
For all genes, we stored which reporters, if any, are mapped inside the gene or in
its 5 kb upstream region.
> data(“mm9.g2p”)
For later use, we determine which genes have a sufficient
number—arbitrarily we say five—of reporters mapped to their
upstream region or inside and which genes also have one or more GO terms annotated
to them.
> arrayGenes<- names(mm9.g2p)
[listLen(mm9.g2p)>=5]
> arrayGenesWithGO <-
intersect(arrayGenes, names(mm9.gene2GO))
For each sample, we compute the log ratios log2(Cy5/Cy3) for all
reporters. To adjust for systematic dye and labeling biases, we compute
Tukey's biweight mean across each sample's log2 ratios
and subtract it from the individual log2 ratios. Each of the four
microarray types contains a unique set of reporters. Thus, we preprocess the arrays
separately by type and combine the results into one object holding the preprocessed
readouts for all reporters.
> MAs <- lapply(RGs,
function(thisRG)
+preprocess(thisRG[thisRG$genes$Status==“Probe”,],
+method=“nimblegen”,
returnMAList=TRUE))
> MA <- do.call(rbind,
MAs)
> X <-
asExprSet(MA)
> sampleNames(X) <-
paste(X$Cy5, X$Tissue,
sep=“.”)
The result is an object of class ExpressionSet, the Bioconductor
class for storing preprocessed microarray data. Note that first creating a
MAList for each array type, combining them with rbind, and then
converting the result into an ExpressionSet is only necessary if
the reporters are distributed over more than one microarray type. For data of one
microarray type only, you can call preprocess with argument
‘returnMAList = FALSE’ and
directly obtain the result as an ExpressionSet. The above procedure is the standard method suggested by NimbleGen for ChIP-chip. The
appropriate choice of normalization method generally depends on the data at hand,
and the need for normalization is inversely related to the quality of the data.
Ringo and Bioconductor offer many alternative and more
sophisticated normalization methods, e.g., using the genomic DNA hybridization as
reference [24]. However, due to the smaller dynamic range of the
data in the input channel, such additional effort seems less
worthwhile than, say, for transcription microarrays. Visualizing Intensities along the Chromosome We visualize the preprocessed H3K4me3 ChIP-chip reporter levels around the start of
the Actc1 gene, which encodes the cardiac actin protein.
> chipAlongChrom(X,
chrom=“2”,
xlim=c(113.8725e6,113.8835e6),
ylim=c(−3,5),
+probeAnno=probeAnno,
gff=mm9genes,
paletteName=‘Set2’)
The degree of H3K4me3 enrichment over the reporters mapped to this region seems
stronger in heart cells than in brain cells (see ). However, the signal is highly
variable, and individual reporters give different readouts from reporters matching
genomic positions only 100 bp away, even though the DNA fragments after sonication
are hundreds of base pairs long. See Text S1
for the corresponding intensities around the start of the brain-specific gene
Crpm1
[25]. When multiple replicates are available, it is instructive to compare these
visualizations to assess the agreement between replicates. Smoothing of Reporter Intensities The signal variance arises from systematic and stochastic noise. Individual reporters
measure the same amount of DNA with different efficiency due to reporter sequence
characteristics [26], such as GC content, secondary structure, and
cross-hybridization. To ameliorate these reporter effects as well as the stochastic
noise, we perform a smoothing over of individual reporter intensities before looking
for ChIP-enriched regions. We slide a window of 900 bp width along the chromosome
and replace the intensity at genomic position x0 by the
median over the intensities of those reporters mapped inside the window centered at
x0. Factors to take into account when choosing the
width of the sliding window are the size distribution of DNA fragments after
sonication and the spacing between reporter matches on the genome.
> smoothX <-
computeRunningMedians(X,
probeAnno=probeAnno,
+modColumn=“Tissue”,
allChr=allChrs,
winHalfSize=450,
min.probes=5)
> sampleNames(smoothX) <-
paste(sampleNames(X),
“smoothed”,sep=“.”)
Compare the smoothed reporter intensities with the original ones around the start of
the gene Actc1.
> chipAlongChrom(X,
chrom=“2”,
xlim=c(113.8725e6,113.8835e6),
ylim=c(−3,5),
+probeAnno=probeAnno,
gff=mm9genes,
paletteName=‘Set2’)
> chipAlongChrom(smoothX,
chrom=“2”,
xlim=c(113.8725e6,113.8835e6),
ilwd=4,
+probeAnno=probeAnno,
paletteName=‘Dark2’,
add=TRUE)
See the result in . After
smoothing, the reporters give a more concise picture that there is H3K4me3
enrichment inside and upstream of Actc1 in heart but not in brain
cells. Finding ChIP-Enriched Regions We would like to determine a discrete set of regions that appear antibody-enriched,
together with a quantitative score of our confidence in that and a measure of their
enrichment strength. Which approach is best for this purpose depends on the
microarray design, on the biological question, and on the subsequent use of the
regions, e.g., in a follow-up experiment or computational analysis. Below, we
describe one possible approach, but, before that, we discuss two more conceptual
aspects. In the literature, a computed confidence score is often mixed up with the term
“p-value”. Speaking of a
p-value is meaningful only if there is a defined null hypothesis
and a probability interpretation; these complications are not necessary if the goal
is simply to find and rank regions in some way that can be reasonably calibrated. Furthermore, it is helpful to distinguish between our confidence in an enrichment
being present, and the strength of the enrichment. Although stronger enrichments
tend to result in stronger signals and hence less ambiguous calls, our certainty
about an enrichment should also be affected by reporter coverage, sequence,
cross-hybridization, etc. Let us now consider the following simple approach: for an enriched region, require
that the smoothed reporter levels all exceed a certain threshold
y0, that the region contains at least
nmin reporter match positions, and that each of
these is less than dmax basepairs apart from the nearest
other affected position in the region. The minimum number of reporters rule (nmin) might seem
redundant with the smoothing median computation (since a smoothed reporter intensity
is already the median of all the reporter intensities in the window), but it plays
its role in reporter sparse regions, where a window may only contain one or a few
reporters. One wants to avoid making calls supported by only few reporters. The dmax rule prevents us from calling disconnected
regions. Setting the enrichment threshold The optimal approach for setting the enrichment threshold
y0 would be to tune it by considering sets
of positive and negative control regions. As such control regions are often
not available, as with the current data, we choose a mixture modeling
approach. The distribution of the smoothed reporter levels y can be
modeled as a mixture of two underlying distributions. One is the null
distribution  of reporter levels in non-enriched regions; the other is
the alternative distribution  of the levels in enriched regions. The challenge is to estimate the null distribution  . In Ringo, an estimate  is derived based on the empirical distribution of smoothed
reporter levels, as visualized in .
> myPanelHistogram <-
function(x,…){
+panel.histogram(x,
col=brewer.pal(8,“Dark2”)[panel.number()],…)
+panel.abline(v=y0[panel.number()],
col=“red”)
+ }
> h =
histogram(~y | z,
+y
=
as.vector(exprs(smoothX)),
+z
=
rep(X$Tissue,each=nrow(smoothX))),
+layout
= c(1,2),nint
= 50,
+xlab
= “smoothed reporter
level [log2]”,
+panel
=
myPanelHistogram)
> print(h)
The histograms motivate the following assumptions on the two mixture
components  and  : the null distribution  has most of its mass close to its mode
m0, which is close to
y= 0, and it is symmetric
about m0; the alternative distribution  is more spread out and has almost all of its mass to the
right of m0. Based on these assumptions, we can estimate  as follows. The mode m0 can be
found by the midpoint of the shorth of those y that fall
into the interval [−1,1] (on a
log 2scale). The distribution  is then estimated from the empirical distribution of
m0−| y− m0|,
i.e., by reflecting y< m0
onto y> m0. From the
estimated null distribution, an enrichment threshold
y0 can be determined, for example the
99.9% quantile. > y0 < - apply(exprs(smoothX), 2,
upperBoundNull, prob=0.99)The values y0 estimated in this way are indicated
by red vertical lines in the histograms in . Antibodies vary in their
efficiency to bind to their target epitope, and the noise level in the data
depends on the sample DNA. Thus, y0 should be
computed separately for each antibody and cell type, as the null and
alternative distributions,  and  , may vary. This algorithm has been used in previous studies [27]. A critical
parameter in algorithms for the detection of ChIP-enriched regions is the
fraction of reporters on the array that are expected to show enrichment. For
the detection of in-vivo TF binding sites, it is reasonable to assume that
this fraction is small, and most published algorithms rely on this
assumption. However, the assumption does not necessarily hold for ChIP
against histone modifications. The algorithm presented works as long as
there is a discernible population of non-enriched reporter levels, even if
the fraction of enriched levels is quite large. Another aspect of ChIP-chip data is the serial correlation between reporters,
and there are approaches that aim to model such correlations [28], [29]. ChIP-enriched regions We are now ready to identify H3K4me3 ChIP-enriched regions in the data. We
set nmin= 5
and dmax= 450.
> chersX <-
findChersOnSmoothed(smoothX,
We relate found ChIP-enriched regions to gene coordinates retrieved from the
Ensembl database (see above). An enriched region is regarded as
related to a gene if its center position is located
less than 5 kb upstream of a gene's start coordinate or between a
gene's start and end coordinates.
> chersX <-
relateChers(chersX, mm9genes,
upstream=5000)
One characteristic of enriched regions that can be used for ranking them is
the area under the curve score, that is, the sum of the
smoothed reporter levels, each minus the threshold. Alternatively, one can
rank them by the highest smoothed reporter level in the enriched region.
> chersXD <-
as.data.frame(chersX)
> head(chersXD[
+order(chersXD$maxLevel,
decreasing=TRUE),
+c(“chr”,
“start”, “end”,
“cellType”, “features”,
“maxLevel”,
“score”)])
| Table 2The six ChIP-enriched regions with the highest smoothed reporter
levels. |
We visualize the intensities around the region with the highest smoothed
level.
>
plot(chersX[[which.max(chersXD$maxLevel)]],
smoothX,
probeAnno=probeAnno,
+gff=mm9genes,
paletteName=“Dark2”,
ylim=c(−1,6))
displays this
region, which covers the gene Tcfe3. Comparing ChIP-Enrichment between the Tissues There are several ways to compare the H3K4me3 enrichment between the two tissues.
How many ChIP-enriched regions do we find in each tissue?
>
table(chersXD$cellType)
brain heart 11852 10391 Brain cells show a higher number of H3K4me3-enriched regions than heart cells.
Which genes show tissue-specific association to H3K4me3 ChIP-enriched regions?
> brainGenes <-
getFeats(chersX[sapply(chersX,
cellType)==“brain”])
> heartGenes <-
getFeats(chersX[sapply(chersX,
cellType)==“heart”])
> brainOnlyGenes <-
setdiff(brainGenes, heartGenes)
> heartOnlyGenes <-
setdiff(heartGenes, brainGenes)
We use the Bioconductor package topGO
[12] to
investigate whether tissue-specific H3K4me3-enriched genes can be summarized by
certain biological themes. topGO employs the Fisher test to
assess whether among a list of genes, the fraction annotated with a certain GO
term is significantly higher than expected by chance, considering all genes that
are represented on the microarrays and have GO annotation. We set a
p-value cutoff of 0.001, and the evaluation starts from the
most specific GO nodes in a bottom-up approach. Genes that are used for
evaluating a node are not used for evaluating any of its ancestor nodes
[ 12] elim algorithm.
> sigGOTable <-
function(selGenes,
GOgenes=arrayGenesWithGO,
+gene2GO=mm9.gene2GO[arrayGenesWithGO],
ontology=“BP”,maxP=0.001)
+ {
+inGenes <-
factor(as.integer(GOgenes%in%selGenes))
+names(inGenes) <-
GOgenes
+GOdata <-
new(“topGOdata”,
ontology=ontology,
allGenes=inGenes,
+annot=annFUN.gene2GO,
gene2GO=gene2GO)
+myTestStat <-
new(“elimCount”,
testStatistic=GOFisherTest,
+name=“Fishertest”,
cutOff=maxP)
+mySigGroups <-
getSigGroups(GOdata, myTestStat)
+sTab <-
GenTable(GOdata, mySigGroups,
topNodes=length(usedGO(GOdata)))
+names(sTab)[length(sTab)]
<-
“p.value”
+sTab <-
subset(sTab, as.numeric(p.value) <
maxP)
+sTab$Term
<- sapply(mget(sTab$GO.ID,
env=GOTERM),
Term)
+return(sTab)
+ }
> brainRes <-
sigGOTable(brainOnlyGenes)
> print(brainRes)
See the result GO terms in Table
3. We perform the same analysis for genes showing heart-specific relation
to H3K4me3 enrichment. | Table 3GO terms that are significantly over-represented among genes showing
H3K4me3 enrichment specifically in brain cells. |
> heartRes <-
sigGOTable(heartOnlyGenes)
> print(heartRes)
See the result in Table 4.
Genes that show H3K4me3 in brain but not in heart cells are significantly often
involved in neuron-specific biological processes. Genes marked by H3K4me3
specifically in heart cells show known cardiomyocyte functions, amongst others. | Table 4GO terms that are significantly over-represented among genes showing
H3K4me3 enrichment specifically in heart cells. |
This analysis could be repeated for the cellular component and
molecular function ontologies of the GO. Besides GO, other
databases that collect gene lists can be used for this kind of gene set
enrichment analysis. For, example, the Kyoto Encyclopedia of Genes and Genomes
(KEGG) [30] is also available in Bioconductor. In Text
S1, we present an additional way for comparing H3K4me3 enrichment between
the two tissues, an enriched-region–wise comparison considering the
actual overlap of the enriched regions. ChIP Results and Expression Microarray Data We compare the H3K4me3 ChIP-chip results with the expression microarray data,
which Barrera et al. [18] provide for the same M.
musculus tissues they analyzed with ChIP-chip.
>
data(“barreraExpressionX”)
The data were generated using the Mouse_430_2 oligonucleotide microarray platform
from Affymetrix and preprocessed using Affymetrix's MAS5 method. Using
biomaRt, we created a mapping of Ensembl gene identifiers
to the probe set identifiers on that microarray platform (see Text S1 for
the source code).
>
data(“arrayGenesToProbeSets”)
We obtain the expression values for genes related to H3K4me3-enriched regions in
heart or brain cells.
> bX <-
exprs(barreraExpressionX)
> allH3K4me3Genes <-
union(brainGenes, heartGenes)
> allH3K4ProbeSets <-
unlist(arrayGenesToProbeSets[allH3K4me3Genes])
> noH3K4ProbeSets <-
setdiff(rownames(bX), allH3K4ProbeSets)
> brainH3K4ExclProbeSets <-
unlist(arrayGenesToProbeSets[brainOnlyGenes])
> heartH3K4ExclProbeSets <-
unlist(arrayGenesToProbeSets[heartOnlyGenes])
> brainIdx <-
barreraExpressionX$Tissue==“Brain”
> brainExpression <-
list(
+H3K4me3BrainNoHeartNo
= bX[noH3K4ProbeSets,
brainIdx],
+H3K4me3BrainYes
= bX[allH3K4ProbeSets,
brainIdx],
+H3K4me3BrainYesHeartNo
= bX[brainH3K4ExclProbeSets,
brainIdx],
+H3K4me3BrainNoHeartYes
= bX[heartH3K4ExclProbeSets,
brainIdx]
+ )
We use boxplots to compare the brain expression levels of genes with and without
H3K4me3-enriched regions in brain/heart cells.
> boxplot(brainExpression,
col=c(“#666666”,
“#999966”, “#669966”,
“#996666”),
+names=NA,
varwidth=TRUE,
log=“y”,
+ylab=‘geneexpressionlevelinbraincells’)
> mtext(side=1,
at=1:length(brainExpression),
padj=1,
font=2,
+text=rep(“H3K4me3”,4),
line=1)
> mtext(side=1,
at=c(0.2,1:length(brainExpression)),
padj=1,
font=2,
+text=c(“brain/heart”,
“−/−”,
“+/+”,
“+/−”,
“−/+”),
line=2)
See the boxplots in .
Genes related to H3K4me3 ChIP-enriched regions show higher expression levels
than those that are not, as we can assess using the Wilcoxon rank sum test. > with(brainExpression, +wilcox.test(H3K4me3BrainYesHeartNo, H3K4me3BrainNoHeartNo, Wilcoxon rank sum test with continuity correction data: H3K4me3BrainYesHeartNoandH3K4me3BrainNoHeartNo W = 88159233, p-value < 2.2e-16 alternative hypothesis: true location shift is greater than 0 Discussion The analysis of the ChIP-chip and transcription data of Barrera et al. [18]
showed that genes that are expressed in specific tissues are marked by
tissue-specific H3K4me3 modification. This finding agrees with previous reports
that H3K4me3 is a marker of active gene transcription [16]. We have shown how to use the freely available tools R and Bioconductor for the
analysis of ChIP-chip data. We demonstrated ways to assess data quality, to
visualize the data, and to find ChIP-enriched regions. As with any high-throughput technology, there are aspects of ChIP-chip
experiments that need close attention, such as specificity and sensitivity of
the antibodies, and potential cross-hybridization of the microarray reporters.
Good experiments will contain appropriate controls, in the presence of which the
software can be used to monitor and assess these issues. In addition to the ones introduced here, there are other Bioconductor packages
that provide further functionality, e.g., ACME
[31], oligo, and tilingArray
[24].
For analyses that go beyond pairwise comparisons of samples and use more complex
(multi-)factorial experimental designs or retrospective studies of collections
of tissues from patients, the package limma
[20]
offers a powerful statistical modeling interface and facilitates computation of
appropriate reporter-wise statistics. We also demonstrated a few conceivable follow-up investigations. Bioconductor
allows for easy integration of ChIP-chip results with other resources, such as
annotated genome elements, gene expression data, or DNA–protein
interaction networks. Software Versions This tutorial was generated using the following package versions: - R version 2.8.0 Under development (unstable) (2008-09-13 r46541),
x86_64-unknown-linux-gnu
- Locale:
LC_CTYPE=en_US.ISO-8859-1;LC_NUMERIC=C;LC_TIME=en_US.ISO-8859-1;LC_COLLATE=en_US.ISO-8859-1;LC_MONETARY=C;LC_MESSAGES=en_US.ISO-8859-1;LC_PAPER=en_US.ISO-8859-1;LC_NAME=C;LC_ADDRES8859-1;LC_IDENTIFICATION=C
- Base packages: base, datasets, graphics, grDevices, methods, splines,
stats, tools, utils
- Other packages: affy 1.19.4, affyio 1.9.1, annotate 1.19.2, AnnotationDbi
1.3.9, Biobase 2.1.7, biomaRt 1.15.1, ccTutorial 0.9.5, codetools 0.2-1,
DBI 0.2-4, digest 0.3.1, fortunes 1.3-5, genefilter 1.21.3, geneplotter
1.19.5, GO.db 2.2.3, graph 1.19.5, lattice 0.17-15, limma 2.15.11,
preprocessCore 1.3.4, RColorBrewer 1.0-2, RCurl 0.9-4, Ringo 1.5.13,
RSQLite 0.7-0, SparseM 0.78, survival 2.34-1, topGO 1.9.0, vsn 3.7.6,
weaver 1.7.0, xtable 1.5-3
- Loaded via a namespace (and not attached): cluster 1.11.11, grid 2.8.0,
KernSmooth 2.22-22, XML 1.96-0
Supporting Information Text S1 Analyzing ChIP-chip data using Bioconductor. This document contains
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Enriched Regions
