%\VignetteIndexEntry{GGtools overview}
%\VignetteDepends{ceu1kg, ceuhm3, GenomicRanges, illuminaHumanv1.db}
%\VignetteKeywords{Genetical genomics,SNP,expression}
%\VignettePackage{GGtools}


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\title{Overview of GGtools for expression genetics: for 3.7.x}
\author{VJ Carey stvjc at channing.harvard.edu}
\maketitle
\tableofcontents

\section{Introduction}

The \Rpackage{GGtools} package contains infrastructure and
demonstration data for joint analysis of transcriptome and
genome through combination of DNA expression microarray
and high-density SNP genotyping data.  For Bioconductor
2.2 we adopted a representation of genotypes due to Clayton
(in package \Rpackage{snpStats}) allowing reasonably
convenient storage and manipulation of 4 megaSNP phase III
HapMap genotypes on all the CEPH CEU samples.  This contrasts with
the previous version of \Rpackage{GGtools} which was limited
to 550 kiloSNP and 58 CEU founders.

Note added 2010: GGtools 3.7.x is undergoing revisions to simplify
management of the code to face the task of comprehensive eQTL
searches.  Functions like \texttt{gwSnpTests} were created with
highly flexible but very complex interface capabilities, and these
seem very little used.  More important are facilities for concurrent
computation and minimum-volume representation of results.  The
\texttt{eqtlTests} function will be the main workhorse for material
described in this document; some of the \texttt{gwSnpTests} functionalities
will be retained for consistency with published work on this package,
but the following broad changes to strategy are noted:
\begin{itemize}
\item gene symbol translations and gene set manipulation are not
addressed in the recommended tools surrounding \texttt{eqtlTests};
\item SNP and gene location metadata will not be maintained in the package
except for demonstration purposes; location structures will need to
be IRanges-based and are the responsibility of the user;
\item to cope with volume of test results generated, the \texttt{ff}
external matrix representation approaches are heavily used.
\end{itemize}


To give an immediate taste of the capabilities, we
attach the package and load some test data.
<<doini>>=
library(GGtools)
library(ceuhm3)
data(hm3ceuSMS)
hm3ceuSMS
@
Expression data are recoverable in a familiar way:
<<lkex>>=
exprs(hm3ceuSMS)[1:5,1:5]
@

Genotype data have more complex representation.
<<lksn>>=
names(smList(hm3ceuSMS))
smList(hm3ceuSMS)[1:2]
class(smList(hm3ceuSMS)[["chr20"]])
@
This shows that we use a named list to hold items
of the \Rclass{SnpMatrix} class from \Rpackage{snpStats}.

It will generally be unnecessary to probe to this level, but
it is instructive to check the underlying representation:
<<lkr>>=
schunk = smList(hm3ceuSMS)[["chr20"]]
schunk@.Data[1:4,1:4]
@
The leading zeroes show that a raw byte representation is used.
We can convert to allele codes as follows:
<<lkr2>>=
as(schunk[1:4,1:4], "character")
@
[Note mapping to nucleotide codes is not directly supported
at this time.  The extra baggage is voluminous and for most loci it
will never be used.  When necessary, manual bioinformatics can be used to check the
locus-specific allele frequency in a cohort against the
allele count in the data to determine the code unambiguously; the
SNP metadata resources in SNPlocs.Hsapiens.* packages, or in ceu1kg,
are also relevant.]

The primary analysis of interest is the genome-wide
association study, here applied with expression as the phenotype.
Here we execute a founders-only analysis, adjusting for gender,
confining attention to SNP on chromosome 20.  We isolate
a single gene, CPNE1, for analysis.
<<dod>>=
pd = pData(hm3ceuSMS)
hmFou = hm3ceuSMS[, which(pd$mothid == 0 & pd$fathid == 0)]
library(illuminaHumanv1.db)
pc = get("CPNE1", revmap(illuminaHumanv1SYMBOL))
hmFouc = hmFou[probeId(pc),]
hmFouc
system("rm -rf foo")
f1 = eqtlTests(hmFouc[chrnum("chr20"),], ~male)
f1
@
By default, test results are stored in a compact disk-based
archive governed by facilities of the \textit{ff} package.
<<lkd>>=
dir("foo")
@

\section{Using imputation to 1000 genomes loci}

The \textit{snpStats} package defines methods for using
linear and haplotype regression to estimate conditional
expectations of allele counts for unobserved loci.  We used
the pilot 1 calls from 1000 genomes (1KG) to define rules
relating loci observed in phase III HapMap for CEU to
1KG loci.
Here are the first 10 rules for a version of the imputation that
has a liberal approach to allowing LD computations.
<<getr>>=
data(imphm3_1KG_20_mA2)
imphm3_1KG_20_mA2[1:10]
@
We can use these rules to obtain a richer
collection of tests for eQTL for CPNE1:
<<doi,eval=FALSE>>=
try(system("rm -rf icpne1"))
if1 = ieqtlTests(hmFouc[chrnum("chr20"),], ~male, targdir="icpne1",
     runname="icpne1",
     rules=imphm3_1KG_20_mA2)
@
The number of tests on the original data:
<<lkn1,eval=FALSE>>=
length(GGtools:::snpIdList(f1)[[1]])
@
and on the imputed data:
<<lki1,eval=FALSE>>=
length(GGtools:::snpIdList(if1)[[1]])
@
We need special metadata to get locations for the SNP in this run.
<<getnm,eval=FALSE>>=
library(ceu1kg)
data(ceu1kgMeta_20)
inn = start(ceu1kgMeta_20)
names(inn) = names(ceu1kgMeta_20)
igg = getGRanges( if1, 1, 1, "chr20", inn)
@

In the right environment we can use code like the following
to see the impact of the imputation.
<<lkd,eval=FALSE,keep.source=TRUE>>=
library(rtracklayer)
s1 = browserSession()
genome(rd) = "hg18"
s1[["CPNE1"]] = rd
ird = RangedData(igg)
genome(ird) = "hg18"
s1[["ICPNE1"]] = ird
v1 = browserView(s1, GenomicRanges(33.5e6,34.5e6, "chr20"), 
   track=ucscTrackModes(hide="knownGene", 
   full=c("refGene", "CPNE1", "ICPNE1")))
@

\setkeys{Gin}{width=0.95\textwidth}
\includegraphics{CPNE1plusImp}

We see the expected added density for the richer set of loci, but
no qualitative impact of the imputation.

\section{Assessing concordance of regression-based imputation with
MACH}

Blanca Himes of the Channing Laboratory, Boston, ran the MACH imputation
program to compute imputed 'dose' measures based on a
certain set of calls for CEU, perhaps HapMap phase III.  The \texttt{m20}
data object for this package includes the results of importing
the \texttt{mlprob} MACH output for chromosome 20 after imputing
to the 1000 genomes loci.
<<lkmach>>=
data(m20)
as(m20, "numeric")[1:10,1:8]
@

The observed genotypes for CEU founders from HapMap phase III are
<<getcc>>=
c20obs = smList(hmFouc)[[20]]
c20obs
@
We impute as follows:
<<geti>>=
unix.time(c20imp <- impute.snps( imphm3_1KG_20_mA2, c20obs ))
c20imp[1:5,1:5]
@
The results need to be placed in compatible structures.
We will use C20D to hold the allele dose measurements from 
MACH and R20D to hold the snpStats regression-based expected allele counts.
<<lkcom,keep.source=TRUE>>=
C20D = m20[, intersect(colnames(m20), colnames(c20imp))]
R20D = c20imp[, intersect(colnames(m20), colnames(c20imp))]
oksnp = intersect(rownames(C20D), rownames(R20D))
length(oksnp)
C20D = C20D[oksnp,]
R20D = R20D[oksnp,]
@

Let's check the basic characteristics of these different imputations.
First, how many loci are imputed by MACH?
<<lkim>>=
length(mimp <- setdiff(colnames(m20), colnames(c20obs)))
@
Of these, how many are monomorphic or close to monomorphic?
<<domon>>=
news = m20[, mimp ]
vn = apply(news, 2, var)
sum(vn==0)
sum(vn < 0.01)
@
The latter result shows that there are over 2000 SNP for which MACH
is willing to impute a single het against 109 homozygous individuals.
Let's look at an example:
<<ex>>=
table(as(news[ ,vn > 0 & vn < 0.01 ][,1], "character"))
@
This shows some of the subtlety of the imputations in this case.
It would be very nice to reconstruct the imputation for this SNP in detail.

Let's now consider loci imputed by MACH but not by snpStats.
<<lkn>>=
if (.Platform$OS.type != "windows" ) {
notreg = colnames(c20imp)[ which(apply(c20imp,2,function(x)all(is.na(x)))) ]
inmachNR = intersect( mimp, notreg )
length(inmachNR)
m20[1:10,inmachNR[1:5]]
table(as(m20[,inmachNR[1]],"character"))
table(as(m20[,inmachNR[2]],"character"))
library(ceu1kg)
if (!exists("ceu1kg.sml")) data(ceu1kg.sml)
rawc20 = ceu1kg.sml[[20]]
table(as(rawc20[,"chr20:35790"], "character"))
table(as(rawc20[,"chr20:36155"], "character"))
}
@
So MACH is able to call 3 hets for each of these loci.
We verify that snpStats regression does not
<<lkm>>=
if (.Platform$OS.type != "windows" ) {
MbutNR = c("chr20:35790", "chr20:36155")
Y = rawc20[, MbutNR]#, drop=FALSE]
X = rawc20[, -match(MbutNR, colnames(rawc20))] #, drop=FALSE]
data(ceu1kgMeta_20)
NL = start(ceu1kgMeta_20)
names(NL) = names(ceu1kgMeta_20)
LOCX = NL[ colnames(X) ]
length(LOCX)
LOCY = NL[ colnames(Y) ]
imm = snp.imputation( X, Y, LOCX, LOCY, minA=2 )
imm
imm1 = snp.imputation( X, Y, LOCX, LOCY, minA=1 )
imm1
}
@
How liberal are we willing to be with respect to estimation of LD in the
presence of very limited genotype variation?
The lower we set minA, the more liberal.
We can see what the regression procedure does in this case as follows:

<<lkimm1>>=
if (.Platform$OS.type != "windows" ) {
ii = impute.snps(imm1, rawc20)
apply(ii,2,table)
}
@
More uncertainty is evident with the
regression imputations, and a very liberal
criterion is used to make these calls. 
It is possible to show that two of the
individuals receiving an allele score of .66 with the
regression method are called A/B by MACH; the matching
between individuals is spotty.





%\section{Conversion to nucleotide codes}
%
%This is currently somewhat cumbersome.  Suppose we want to
%know the specific nucleotide assignments for a given genotype call.
%For example, rs4814683 for subject NA06985.
%<<lkc>>=
%schunk["NA06985", "rs4814683"]
%@
%We need to know a) that the A/B tokens map in lexical order to
%the nucleotides (A will be the alphabetically first nucleotide
%for the diallelic call).
%
%Using the SNPlocs.Hsapiens.dbSNP.* package, we can get the
%nucleotides:
%<<gets,eval=FALSE>>=
%library(SNPlocs.Hsapiens.dbSNP.20100427)
%s20 = getSNPlocs("chr20")
%s20[ s20[,1] == 4814683, ]
%@
%
%Now we need to translate the IUPAC code to the nucleotides:
%<<dotr>>=
%library(Biostrings)
%IUPAC_CODE_MAP
%@

\end{document}
