%\VignetteIndexEntry{dsQTL, data excerpt from Degner et al. 2012 Nature letter}
%\VignetteDepends{dsQTL, Biobase, SummarizedExperiment, GGBase, GGtools, rtracklayer}
%\VignetteKeywords{genetics, Expression Analysis}
%\VignettePackage{dsQTL}
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\begin{document}
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\title{dsQTL: exploring DNA-variants associated with DNaseI hypersensitivity}
\author{VJ Carey}
\maketitle
\section{Introduction}
Degner et al. (2012) publish information on associations
between DNA variants (SNP, SNV, and indels) and DNaseI hypersensitivity
measures acquired via DNase-Seq.
This package includes information from the Chicago group on
normalized DNase-seq data for chromosomes 2 and 17, and genotype data
from chromosome 2 only.
\section{The basic data structure}
<<lkd>>=
library(dsQTL)
data(DSQ_17)
metadata(DSQ_17)
metadata(DSQ_17)[[1]]
@
We use summarized experiment structure for the assay data, but the imputed
genotype data are kept separate, in the package, in the inst/parts folder.
The data structure on chr2, which will be used to reproduce some findings,
is more mature
<<lkd2>>=
data(DSQ_2)
names(assays(DSQ_2))
assays(DSQ_2)[[1]][1:5,1:5]
rowRanges(DSQ_2)
@
To implement the GGBase protocol for on-the-fly generation of smlSet instances
from getSS queries, we have an ExpressionSet instance with specific names.
<<lkd3>>=
data(eset, package="dsQTL")
ex
@
The genotype data supplied by Degner et al are imputed to 1000 genomes
haplotypes, and are reals
in [0,2]. For simplicity the current image of the data uses the
rounding of the fractional genotypes x with round(x,0).
The feature data refer to the retained 100bp segments that
were summarized for DNaseI hypersensitivity and found to lie in the
uppermost 5\% of the distribution.
<<lkf>>=
library(Biobase)
fData(ex)[1:5,,drop=FALSE]
@
We can get the integrated container as
<<domo>>=
library(GGBase)
ds2 = getSS("dsQTL", "roundGT_2")
@
the name indicates that we simply rounded the imputed fractional genotypes
to nearest integer.
A very restricted search is:
<<dose,cache=TRUE>>=
# need to get rid of SNPlocs package getSNPlocs
getSNPlocs = dsQTL::getSNPlocs # force
library(GGtools)
#library(parallel)
#options(mc.cores=12)
n1 = best.cis.eQTLs(smpack="dsQTL", radius=2000, geneannopk="dsQTL",
snpannopk="dsQTL", chrnames="2", smchrpref="roundGT_",
smFilter = function(x) GTFfilter(x, lower=0.05)[23810:23830,],
# geneApply=mclapply)
geneApply=lapply)
<<lkpr>>=
n1
@
<<lkhi,fig=TRUE>>=
plot_EvG(probeId("dhs_2_45370802"), rsid("chr2.45370846"), getSS("dsQTL", "roundGT_2",
wrapperEndo=function(x){annotation(x)="dsQTL"; x}))
@
\section{Provenance}
\subsection{Normed DNaseI hypersensitivity scores}
The dsQTL package data structures \verb+DSQ_2+ and \verb+DSQ_17+
are generated from GEO GSE31388, from which a collection of 70 compressed
BED format files were acquired Aug 9 2011. These are imported
using the rtracklayer package to obtain location and score information
for all the recorded DNaseI hypersensitivity assay results. For
example, after import for NA19257, we have
\begin{verbatim}
Browse[1]> x
[1] "NA19257"
Browse[1]> tmp
RangedData with 1465907 rows and 3 value columns across 22 spaces
space ranges | name score strand
<factor> <IRanges> | <character> <numeric> <factor>
1 chr1 [ 402, 501] | NOT -0.67088720 +
2 chr1 [ 502, 601] | NOT -1.69969288 +
3 chr1 [ 602, 701] | NOT 0.13520754 +
... ... ... ... ... ... ...
1465905 chr22 [49571602, 49571701] | NOT 0.62742318 +
1465906 chr22 [49575102, 49575201] | NOT -0.09417379 +
1465907 chr22 [49581602, 49581701] | NOT -0.29496269 +
\end{verbatim}
The scores for locations on chromosome 2 were collected using
<<showp1>>=
proc1 = function(x) {
library(rtracklayer)
tmp = import(paste(x, ".qnorm.bed.gz", sep=""))
stt = split(tmp, space(tmp))
obn = paste(x, "_dsq_chr2", sep="")
assign(obn, stt[["chr2"]])
save(list=obn, file=paste(obn, ".rda", sep=""))
NULL
}
@
The regions and scores reported are described in the GEO metadata as
\begin{quotation}
We also provide BED file format data for each individual for the top 5\% of the genome in terms of total sensitivity. This data was mapped to hg18 using a custom read-mapping algorithm which we describe in detail in the associated publication. Measures of DNase sensitivity were quantile normalized within each individual to a standard normal distribution. Each individual was corrected for GC bias and the top 4 principle (sic) components were removed from the data (See manuscript).
\end{quotation}
Score data were structured as a matrix with columns corresponding to
Yoruba HapMap subject, and rows corresponding to reported
hypersensitivity regions.
The \texttt{RangedSummarizedExperiment} container is used to unite
range and score data in the assays component, and allied metadata
are available in metadata and colData components.
\subsection{Genotype data}
Textual representation of the allelic doses is provided at
\url{http://eqtl.uchicago.edu/dsQTL_data/GENOTYPES/}.
As of Oct 2012, these were rounded to allele counts to
allow use of snpMatrix representation for chromosome 2 genotypes;
propagation of
dosage fractions will be undertaken in late 2012.
\section{Session information}
<<lks>>=
sessionInfo()
@
\end{document}