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%\VignetteIndexEntry{eQTL tutorial, Bioc 2014}
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<h1>eQTL tutorial, Bioc 2014</h1>
VJ Carey stvjc at channing dot harvard dot edu

```{r extracode,echo=FALSE,error=FALSE,message=FALSE}
suppressMessages({
exs2se = function (x, assayname = "exprs", 
    fngetter = function(z) rownames(exprs(z)), 
    annoDb,
    probekeytype = "PROBEID", duphandler = function(z) {
        if (any(isd <- duplicated(z[, "ENTREZID"]))) 
            return(z[!isd, , drop = FALSE])
        z
    }, signIsStrand = TRUE, ucsdChrnames = TRUE) 
{
    annopk = annoDb
    stopifnot(is(annopk, "ChipDb") | is(annopk, "OrgDb"))
    fn = fngetter(x)
    locd = duphandler(fulls <- select(annopk, keytype = probekeytype, 
        keys = fn, columns = c("CHR", "CHRLOC", "CHRLOCEND")))
    nfulls = na.omit(fulls)
    nmultiaddr = nrow(nfulls) - length(fn)
    rownames(locd) = locd[, probekeytype]
    locd = na.omit(locd)
    dropped = setdiff(fn, rownames(locd))
    if (length(dropped) > 0) 
        warning(paste("there were", length(dropped), "addresses dropped owing to missing address information in bioc annotation"))
    locd = locd[intersect(rownames(locd), fn), ]
    strand = rep("*", nrow(locd))
    if (signIsStrand) 
        strand = ifelse(locd[, "CHRLOC"] > 0, "+", "-")
    chpref = ""
    if (ucsdChrnames) 
        chpref = "chr"
    rowd = GRanges(paste0(chpref, locd[, "CHR"]), IRanges(abs(locd[, 
        "CHRLOC"]), abs(locd[, "CHRLOCEND"])), strand = strand)
    names(rowd) = rownames(locd)
    metadata(rowd)$dropped = dropped
    metadata(rowd)$nmultiaddr = nmultiaddr
    hasrowd = match(names(rowd), fn, nomatch = 0)
    ex = x[hasrowd, ]
    stopifnot(nrow(exprs(ex)) == length(rowd))
    ed = SimpleList(initExptData = experimentData(ex))
    SummarizedExperiment(assays = SimpleList(exprs = exprs(ex)), 
        rowData = rowd, colData = DataFrame(pData(ex)), exptData = ed)
}
genemodel = function (sym, genome = "hg19", force = FALSE) 
{
    stopifnot(genome == "hg19")
    if (!exists("txdb") || force) 
        txlib()
    require(org.Hs.eg.db)
    num = get(sym, revmap(org.Hs.egSYMBOL))
    exonsBy(txdb, by = "gene")[[num]]
}
txlib = function () 
{
    library(TxDb.Hsapiens.UCSC.hg19.knownGene)
    txdb <<- TxDb.Hsapiens.UCSC.hg19.knownGene
    message("txdb assigned")
    invisible(NULL)
}

rangeFromToksViaEntrez = function (toks, genome = "hg19", force = FALSE,
   takeRange=TRUE, orgDb=Homo.sapiens, typesToTry=c("SYMBOL", "GOID", "PATH",
   "TERM"), routeSink=TRUE) 
{
#
# convert tokens to GRanges after translation to Entrez ids
# takeRange if true reduces answer to 'range' of exons
# open question whether we also want to cache specific models
#
    require(stashR)
    stopifnot(genome == "hg19")
#
# cache exonsBy of TxDb
#
    require(deparse(substitute(orgDb)), character.only=TRUE)
    modcon = new("localDB", dir="~/.SEresolvers", name="rowResolvers")
    if (!dbExists(modcon, "hg19.exbygene")) {
        message("one time cache creation for TxDb...")
        txlib()  # special function to create txdb in .GlobalEnv
        dbInsert( modcon, "hg19.exbygene", exonsBy(txdb, by="gene") )
        }
    allex = dbFetch(modcon, "hg19.exbygene")
    tf = file(tempfile(), open="wt")
    if (routeSink) message("resolving row selection tokens... (messages diverted)")
    if (routeSink) sink(tf, type="message")
    for (curty in typesToTry) {
      num = try(select(orgDb, keytype=curty,
          keys=toks, columns="ENTREZID")[,"ENTREZID"])
      if (!inherits(num, "try-error")) break
      }
    if (routeSink) sink(NULL, type="message")
    if (routeSink) message("messages restored")
    if (inherits(num, "try-error")) stop("can't resolve toks")
    inds = match(num, names(allex))
    filt = force
    if (takeRange) filt = range
    ans = do.call(c, lapply(1:length(inds), function(x) try({
      tmp = filt(allex[[inds[x]]])
      tmp$ENTREZID=num[x]
      tmp})))
    syms = select(Homo.sapiens, keys=ans$ENTREZID, keytype="ENTREZID",
            columns="SYMBOL")
    if (length(unique(syms[,1]))==nrow(syms)) ans$SYMBOL = syms[,2]
    else { 
      ssyms = split(syms[,2], syms[,1])[ syms[,1] ] # retain order
      s1 = sapply(ssyms, "[", 1)
      ans$SYMBOL = s1
      }
    ans
}
hasRowResolver = function(x) !is.null(exptData(x)$rowResolverHook)
getRowResolverTypes = function(x) exptData(x)$rowResolverHook()
tryRowResolution = function(x,i) {
  ress = getRowResolverTypes(x)
  rngs = rangeFromToksViaEntrez(toks=i, typesToTry=ress)
  ov = findOverlaps( rowData(x), rngs )
  queryHits(ov)
}
# based on devel 1.17.26 GRanges 1.17.26
setMethod("[", c("SummarizedExperiment",
  "ANY", "ANY", "ANY"), function (x, i, j, ..., drop = TRUE) 
{
    clone = GenomicRanges:::clone
    .SummarizedExperiment.charbound = GenomicRanges:::.SummarizedExperiment.charbound
    .SummarizedExperiment.assays.subset = GenomicRanges:::.SummarizedExperiment.assays.subset
    if (1L != length(drop) || (!missing(drop) && drop)) 
        warning("'drop' ignored '[,", class(x), ",ANY,ANY-method'")
    if (missing(i) && missing(j)) 
        return(x)
    if (!missing(i) && is.character(i)) {
        if (hasRowResolver(x)) i = tryRowResolution(x, i)  # will be vector of integers
        else {
          fmt <- paste0("<", class(x), ">[i,] index out of bounds: %s")
          i <- .SummarizedExperiment.charbound(i, rownames(x), 
            fmt)
          }
    }
    if (!missing(j) && is.character(j)) {
        fmt <- paste0("<", class(x), ">[,j] index out of bounds: %s")
        j <- .SummarizedExperiment.charbound(j, colnames(x), 
            fmt)
    }
    if (!missing(i) && !missing(j)) {
        ii <- as.vector(i)
        jj <- as.vector(j)
        x <- clone(x, ..., rowData = rowData(x)[i], colData = colData(x)[j, 
            , drop = FALSE], assays = .SummarizedExperiment.assays.subset(x, 
            ii, jj))
    }
    else if (missing(i)) {
        jj <- as.vector(j)
        x <- clone(x, ..., colData = colData(x)[j, , drop = FALSE], 
            assays = .SummarizedExperiment.assays.subset(x, j = jj))
    }
    else {
        ii <- as.vector(i)
        x <- clone(x, ..., rowData = rowData(x)[i], assays = .SummarizedExperiment.assays.subset(x, 
            ii))
    }
    x
})
library(knitcitations)
library(bibtex)
allbib = read.bibtex("/data/eQTL2014/eq.bib")
})

```

<h2>Basic data from RECOUNT</h2>
We will use transcript profiles obtained through RNA-seq applied
to HapMap cell lines (`r citet(allbib[["Montgomery2010"]])`).  The reported
gene-level count data have been rendered as an ExpressionSet  (montpick\_eset.RData) in the RECOUNT project
(`r citet(allbib[["Frazee2011"]])`) using the ENSEMBL nomenclature
for genes.  These counts were filtered to
exclude genes with uniform zero value over all samples, subjected to
rlog transformation (DESeq2), and then filtered to include only
those with MAD at the 40th percentile of the overall distribution of genewise
MADs.

Characteristic code for the transformation to SummarizedExperiment is given here.
You do not need to execute this as the transformed data are available for loading
as noted below. (exs2se() is a bit of custom code available in the source
of this vignette.)
```{r trans1,eval=FALSE}
library(Biobase)
load("/data/eQTL2014/montpick_eset.RData")
mp = montpick.eset
annotation(mp) = "org.Hs.eg.db"
tmp = select(org.Hs.eg.db, keytype="ENSEMBL", columns="ENTREZID",
  keys=featureNames(mp))
ezs = split(tmp[,2], tmp[,1])
ezs = sapply(ezs, "[", 1)
drop = which(duplicated(ezs) | is.na(ezs))
if (length(drop)>0) ezs = ezs[-drop]
mp = mp[names(ezs)]
featureNames(mp) = as.character(ezs[featureNames(mp)])
mpSE = exs2se(mp, annoDb = org.Hs.eg.db, probekeytype="ENTREZID")
```

<h2>Transformed expression data in SummarizedExperiment</h2>
We obtain the basic image of transformed count data as follows.
```{r getd}
load("/data/eQTL2014/rmpDEPF.rda") # rlogged Montgomery Pickrell DESeqDataSet, some positive, filtered
rmpDEPF
```

<h2>Genotype data in an Amazon S3 bucket</h2>
All the data generated in the 1000 genomes project is available
in a S3 (simple storage service) container.  We have a
URL that can be used to access the genotypes from
chr22 in VCF format.

```{r lkgset, warning=FALSE, message=FALSE}
load("/data/eQTL2014/s3url.rda")
s3url
library(VariantAnnotation)
```{r lkg, warning=FALSE, cache=TRUE, message=FALSE}
head = scanVcfHeader(s3url)
str(head, max.level=4)
```

<h2>A naive analysis</h2>
The GGtools package includes a function, cisAssoc(), that
can test additive genetic associations in cis between SNP genotypes
housed in tabix-indexed VCF and expression data housed in a
SummarizedExperiment.  The test is the score test for a generalized
linear model fit to independent
samples with a continuous response, with response variance approximately
independent of response mean.  These assumptions are generally not met
for count data from RNA-seq experiments, but they may hold reasonably
approximately after
the rlog transformation.  We will check on the severity 
of the violations
after performing some tests.

Here is code that computes, <emph>for 200 genes on
chr17</emph>, association test statistics
and versions thereof under permutation of expression against
genotype.  Do not perform these calculations, they will take too
long for the tutorial.  The answer is provided in the
tutorial data, see below.
```{r getli}
library(GGtools)
```{r doit,eval=FALSE}
library(foreach)
library(doParallel)
registerDoSEQ()
r17 = rmpDEPF[ which(seqnames(rmpDEPF)=="chr17"),]
set.seed(1234)
s3urlb = sub("22", "17", s3url)
c17 = cisAssoc(r17, TabixFile(s3urlb), cisradius=100000, lbmaf=.025)
c17d = addmindist(c17)
```
```{r dod,echo=FALSE}
load("/data/eQTL2014/c17d.rda")
```
Here is an extract from the output.  We have manually
added a column 'mindist' with the number of nucleotides
from SNP to nearest end of gene body.  'mindist' is zero
for SNP lying within the gene body.
```{r lkres}
c17d[1:3,1:5]
names(mcols(c17d))
```

<h2>Computing the plug-in FDR</h2>
The following algorithm is from <strong>The
Elements of Statistical Learning</strong> by
Hastie, Tibshirani and Friedman (Springer 2001).
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" />

We can obtain estimated FDR for the gene-SNP pairs
tested in the example above, as follows, using the
pifdr() function in GGtools.
```{r dofd}
fdr = pifdr( c17d$chisq, c(c17d$permScore_1, 
  c17d$permScore_2, c17d$permScore_3) )
sum(fdr == 0)
sum(fdr <= .05)
```

<h2>Sensitivity analysis, and "what to count?"</h2>

The following computations generate a sensitivity
analysis display, confronting the enumeration of
significantly associated SNP-gene pairs.

```{r dosens1}
library(data.table)
library(foreach)
registerDoSEQ()
dt1 = data.table(as.data.frame(mcols(c17d)))
```{r runsen,cache=TRUE}
s1 = eqsens_dt( dt1 )
```
```{r dopl,fig=TRUE}
plotsens(s1, ylab="Count of significant SNP-gene pairs at given FDR")
```

If instead of counting SNP-gene pairs, we wish to 
count genes that show evidence of association with
some genotype, the plug-in FDR procedure can be
used with maximal association scores per gene, for
observed and permuted tests.

```{r rnsen2,cache=TRUE}
s2 = eqsens_dt( dt1, by="probes" )
```
```{r pl2,fig=TRUE}
plotsens(s2, ylab="Count of genes with evidence of eQTL at given FDR")
```

There is an indication here that we get a larger yield if
we filter more sharply on MAF and distance than we did in the
initial run.

<h3>Exercise 1.</h3>  
How can we compute the FDR for the gene-wise hypotheses
H0g: Mean expression of gene g is independent of SNP
content for all SNP cis to g, under the restrictions
cis radius less than 50kb and MAF greater than 5 percent?

<h2>Visualization</h2>


The following code creates a visualization of genotype-expression
association for a selected
SNP-gene pair.

```{r doonePlot, fig=TRUE}
plotOne = function (summex, vcf.tf, whicha, whichv, genome="hg19", ... ) {
    sampidsInSumm = colnames(summex)
    sampidsInVCF = sampsInVCF(vcf.tf)
    oksamp = intersect(sampidsInSumm, sampidsInVCF)
    stopifnot(length(oksamp) > 0)
    summex = summex[, oksamp]
    vp = ScanVcfParam(fixed = "ALT", info = NA, geno = "GT", 
        samples = oksamp, which = whichv)
    vdata = readVcf(vcf.tf, genome = genome, param = vp)
    rdd = rowData(vdata)
    vdata = GGtools:::snvsOnly(vdata)
    gtdata = factor(geno(vdata)[[1]])
    plot(  as.numeric(assays(summex)[[1]][whicha,]) ~ gtdata , ...)
}
r17 = rmpDEPF[ which(as.character(seqnames(rmpDEPF))=="chr17"),]
s3urlb = sub("22", "17", s3url)
plotOne(r17, TabixFile(s3urlb), 1, c17d[1] )
```

<h3>Exercise 2.</h3>  What are the names of the SNP
and gene depicted here?  Replot, annotating with this
information (improve the x and y labels for maximal
clarity of interpretation).

<h3>Exercise 3.</h3>
What is the FDR for the association shown in the display?

We can show four relationships at once fairly simply.
```{r dofff,fig=TRUE}
opar = par(no.readonly=TRUE)
par(mfrow=c(2,2))
for (i in 2:5)
 plotOne(r17, TabixFile(s3urlb), i, c17d[i] )
par(opar)
```

<h3>Exercise 4.</h3>
Depict the associations for four gene-SNP pairs
with very small FDR.  The four genes should be different.

Hint:
```{r lktops,eval=FALSE}
order(fdr)[1:10]
c17d[7659]
args(plotOne)
plotOne(r17, TabixFile(s3urlb), "114881", c17d[7659] )
```

<h3>Exercise 5: Master regulators.</h3>
Find SNP whose FDR for association with multiple distinct genes is
found to be less than one percent.  Visualize the relationships.

<h2>Connection with "GWAS hits"</h2>

We can acquire an image of the current NHGRI GWAS catalog
as follows.
```{r lkgw, message=FALSE, warning=FALSE, eval=FALSE}
library(gwascat)
curgw = makeCurrentGwascat()
curgw
```
HOWEVER -- as of 28 July 2014, the coordinates used for the
current catalog are GRCh38.  We used rtracklayer's liftOver
with the appropriate chain file to obtain an hg19 addressing scheme,
losing a small number of nonmappable loci.
```{r getgw}
load("/data/eQTL2014/curgwHG19.rda")
curgwHG19[1:3,1:5]
```

<h3>Exercise 6</h3>
How frequent are exact coincidences between SNP exhibiting
eQTL association at FDR five percent or 
less, and GWAS hits?  Compare this frequency as computed 
for SNP exhibiting association at FDR 95 percent or greater.

<h2>Further exercises</h2>


<h3>Exercise 7: Population of origin.</h3>
We have disregarded the population of origin in
this analysis.  Use visualization to assess whether the
association between gene OSBPL7 and SNP rs17774008
has similar forms in subjects from CEU and YRI populations.

You can use s3url to get access to genotype data for chr22.
It takes 5 minutes or so to run cisAssoc on chr22 genes and SNP.
Obtain analyses for CEU and YRI groups separately.  What
is the concordance on significance of SNP-gene pairs at FDR five percent or so?

<h3>Exercise 8: Efficient analysis with counted expression values.</h3>

/data/eQTL2014/montpick_eset.RData has the raw counts.
(Note, probe identifiers are in ENSEMBL vocabulary.)  Use DESeq2 or edgeR to
obtain an FDR for a selected SNP-gene pair.  Write a function
that does this for any given selection.  In principle the mean-variance
relationship needs to be modeled separately for each gene-SNP pair,
so obtaining a statistically optimal solution for a 
general search may be laborious.  It would be nice to understand
the costs of using rlog with blind and fast set to TRUE
before doing a Gaussian-like analysis of eQTL, as opposed to
pair-specific optimally modeled negative binomial testing for eQTL.


<h2>Bibliography</h2>

```{r results='asis',echo=FALSE}
bibliography() #style="markdown")
```

