---
title: "An introduction to the GenomicScores package"
author:
- name: Pau Puigdevall
  affiliation: 
  - &id Dept. of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- name: Robert Castelo
  affiliation: *id
  email: robert.castelo@upf.edu
package: "`r pkg_ver('GenomicScores')`"
abstract: >
  GenomicScores provides infrastructure to store and access genomewide position-specific scores within R and Bioconductor.
vignette: >
  %\VignetteIndexEntry{An introduction to the GenomicScores package}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
output: 
  BiocStyle::html_document2:
    toc: true
    toc_float: true
    number_sections: true
bibliography: bibliography.bib
---

```{r setup, echo=FALSE}
options(width=80)
```

# Getting started

`r Biocpkg("GenomicScores")` is an R package distributed as part of the
Bioconductor project. To install the package, start R and enter:

```{r library_install, message=FALSE, cache=FALSE, eval=FALSE}
source("http://bioconductor.org/biocLite.R")
biocLite("GenomicScores")
```

Once `r Biocpkg("GenomicScores")` is installed, it can be loaded with the following command.

```{r library_upload, message=FALSE, warning=FALSE, cache=FALSE}
library(GenomicScores)
```

Often, however, `r Biocpkg("GenomicScores")` will be automatically loaded when
working with an annotation package that uses `r Biocpkg("GenomicScores")`, such
as `r Biocpkg("phastCons100way.UCSC.hg19")`.

# Genomewide position-specific scores

Genomewide scores assign each genomic position a numeric value denoting an estimated measure of constraint or impact on variation at that position. They are commonly used to filter single nucleotide variants or assess the degree of constraint or functionality of genomic features. Genomic scores are built on the basis of different sources of information such as sequence homology, functional domains, physical-chemical changes of amino acid residues, etc.

One particular example of genomic scores are _phastCons scores_. They provide a measure of conservation obtained from genomewide alignments using the program [phast](http://compgen.cshl.edu/phast) (_Phylogenetic Analysis with Space/Time models_) from @siepel05. The `r Biocpkg("GenomicScores")` package allows one to retrieve these scores through annotation packages (Section \@ref(retrieval-of-genomic-scores-through-annotation-packages)) or as `r Biocpkg("AnnotationHub")` resources (Section \@ref(retrieval-of-genomic-scores-through-annotationhub-resources)).

Often, genomic scores such as phastCons are used within workflows running on
top of R and Bioconductor. The purpose of the `r Biocpkg("GenomicScores")` package is to enable
an easy and interactive access to genomic scores within those workflows.

# Lossy storage of genomic scores with compressed vectors 

Storing and accessing genomic scores within R is challenging when
their values cover large regions of the genome, resulting in gigabytes
of double-precision numbers. This is the case, for instance, for
phastCons [@siepel05], CADD [@kircher14] or M-CAP [@jagadeesh16] scores.

We address this problem by using _lossy compression_, also called _quantization_,
coupled with run-length encoding (Rle) vectors. Lossy compression attempts to
trade off precision for compression without compromising the scientific
integrity of the data [@zender16].

Sometimes, measurements and statistical estimates under certain models
generate false precision. False precision is essentialy noise that wastes
storage space and it is meaningless from the scientific point of view [@zender16].
In those circumstances, lossy compression not only saves storage space, but
also removes false precision.

The use of lossy compression leads to a subset of _quantized_ values much
smaller than the original set of genomic scores, resulting in long runs of
identical values along the genome. These runs of identical values can be
further compressed using the implementation of Rle vectors available in the
`r Biocpkg("S4Vectors")` Bioconductor package.

To enable a seamless access to genomic scores stored with quantized values
in compressed vectors the `r Biocpkg("GenomicScores")` defines the `GScores`
class of objects. This class manages the location, loading and dequantization
of genomic scores stored separately on each chromosome. A further class,
called `MafDb`, is derived from `GScores` to store minor allele frequency (MAF)
data accounting for specific features of this kind of values such as their
organization into populations of individuals.

# Retrieval of genomic scores through annotation packages

There are currently four different annotation packages that store genomic
scores and can be accessed using the `r Biocpkg("GenomicScores")` package;
see Table \@ref(tab:tableGScores).

Annotation Package          | Description
--------------------------- | --------------------------------------------------------------------------------------------
`r Biocpkg("phastCons100way.UCSC.hg19")`   | phastCons scores derived from the alignment of the human genome (hg19) to other 99 vertebrate species.
`r Biocpkg("phastCons100way.UCSC.hg38")`   | phastCons scores derived from the alignment of the human genome (hg38) to other 99 vertebrate species.
`r Biocpkg("phastCons7way.UCSC.hg38")`     | phastCons scores derived from the alignment of the human genome (hg38) to other 6 mammal species.
`r Biocpkg("fitCons.UCSC.hg19")`           | fitCons scores: fitness consequences of functional annotation for the human genome (hg19).

: (\#tab:tableGScores) Bioconductor annotation packages storing genomic scores

This is an example of how genomic scores can be retrieved using the
`r Biocpkg("phastCons100way.UCSC.hg19")` package.
Here, a `GScores` object is created when the package is loaded.

```{r, message=FALSE, warning=FALSE, cache=FALSE}
library(phastCons100way.UCSC.hg19)
gsco <- phastCons100way.UCSC.hg19
class(gsco)
```

The help page of the `GScores` class describes the different methods to access the
information and metadata stored in a `GScores` object. To retrieve genomic scores
for specific positions we should use the function `scores()`, as follows.

```{r}
scores(gsco, GRanges(seqnames="chr22", IRanges(start=50967020:50967025, width=1)))
```

The `r Biocpkg("GenomicScores")` package only loads the scores data from one
sequence to retrieve metadata and from the sequences that are being
queried. Note that now the GScores object has loaded the scores from chr22.

```{r}
gsco
```

The bibliographic reference to cite the genomic scores stored in a `GScores`
object can be accessed using the `citation()` method either on the package
name or on the `GScores` object. The latter is implemented in the
`r Biocpkg("GenomicScores")` package and provides a `bibentry` object.

```{r}
citation(gsco)
```

Other methods tracing provenance and other metadata are `provider()`,
`providerVersion()`, `organism()` and `seqlevelsStyle()`; please consult
the help page of the `GScores` class for a comprehensive list of available
methods.

```{r}
provider(gsco)
providerVersion(gsco)
organism(gsco)
seqlevelsStyle(gsco)
```

# Retrieval of genomic scores through AnnotationHub resources

Another way to retrieve genomic scores is by using the `r Biocpkg("AnnotationHub")`,
which is a web resource that provides a central location where genomic files
(e.g., VCF, bed, wig) and other resources from standard (e.g., UCSC, Ensembl) and
distributed sites, can be found. A Bioconductor `r Biocpkg("AnnotationHub")` web
resource creates and manages a local cache of files retrieved by the user,
helping with quick and reproducible access.

The first step to retrieve genomic scores is to check the ones available to download.

```{r, echo=FALSE}
avgs <- readRDS(system.file("extdata", "avgs.rds", package="GenomicScores"))
```
```{r retrieve2, message=FALSE, cache=FALSE, eval=FALSE}
availableGScores()
```
```{r, echo=FALSE}
avgs
```

The selected resource can be downloaded with the function getGScores().
After the resource is downloaded the first time, the cached copy will
enable a quicker retrieval later.

```{r retrieve3, message=FALSE, cache=FALSE, eval=FALSE}
gsco <- getGScores("phastCons100way.UCSC.hg19")
```

Finally, the phastCons score of a particular genomic position
is retrieved exactly in the same we did with the annotation package.

```{r retrieve4, message=FALSE, cache=FALSE}
scores(gsco, GRanges(seqnames="chr22", IRanges(start=50967020:50967025, width=1)))
```

## Building an annotation package from a GScores object

Retrieving genomic scores through `AnnotationHub` resources requires an internet
connection and we may want to work with such resources offline. For that purpose,
we can create ourselves an annotation package, such as
[phastCons100way.UCSC.hg19](https://bioconductor.org/packages/phastCons100way.UCSC.hg19),
from a `GScores` object corresponding to a downloaded `AnnotationHub` resource.
To do that we use the function `makeGScoresPackage()` as follows:

```{r eval=FALSE}
makeGScoresPackage(gsco, maintainer="Me <me@example.com>", author="Me", version="1.0.0")
```
```{r echo=FALSE}
cat("Creating package in ./phastCons100way.UCSC.hg19\n")
```

An argument, `destDir`, which by default points to the current working
directory, can be used to change where in the filesystem the package is created.
Afterwards, we should still build and install the package via, e.g.,
`R CMD build` and `R CMD INSTALL`, to be able to use it offline.

# Retrieval of multiple scores per genomic position

Among the score sets available as [AnnotationHub](https://bioconductor.org/packages/AnnotationHub)
web resources shown in the previous section, two of them, CADD [@kircher14] and
M-CAP [@jagadeesh16], provide multiple scores per genomic position that capture
the tolerance to mutations of single nucleotides. Using M-CAP scores, we will
illustrate how this type of scores are retrieved by default.

```{r, echo=FALSE}
obj <- readRDS(system.file("extdata", "mcap.v1.0.hg19.chr22.rds", package="GenomicScores"))
mdobj <- metadata(obj)
mcap <- GScores(provider=mdobj$provider,
                provider_version=mdobj$provider_version,
                download_url=mdobj$download_url,
                download_date=mdobj$download_date,
                reference_genome=mdobj$reference_genome,
                data_pkgname=mdobj$data_pkgname,
                data_dirpath="../inst/extdata",
                data_serialized_objnames=c(mcap.v1.0.hg19.chr22.rds="mcap.v1.0.hg19.chr22.rds"))
scorlelist <- get(mdobj$data_pkgname, envir=mcap@.data_cache)
scorlelist[[mdobj$seqname]] <- obj
assign(mdobj$data_pkgname, scorlelist, envir=mcap@.data_cache)
```

```{r, eval=FALSE}
mcap <- getGScores("mcap.v1.0.hg19")
```
```{r}
mcap
citation(mcap)
gr <- GRanges(seqnames="chr22", IRanges(50967020:50967025, width=1))
scores(mcap, gr)
```

The previous call to the `scores()` method returns three scores per position
corresponding to the M-CAP scores that estimate the tolerance of mutating the
reference nucleotide at that position to each of the possible alternative
alleles, in alphabetical order. One may directly retrieve the scores for
combinations of reference and alternative alelles using the `ref` and `alt`
arguments of the `scores()` method. See the following example with the
the previous genomic positions and some randomly selected alternative alleles.

```{r, message=FALSE}
library(BSgenome.Hsapiens.UCSC.hg19)

refAlleles <- as.character(getSeq(Hsapiens, gr))
altAlleles <- DNA_BASES[(match(refAlleles, DNA_BASES)) %% 4 + 1]
cbind(REF=refAlleles, ALT=altAlleles)
scores(mcap, gr, ref=refAlleles, alt=altAlleles)
```

# Summarization of genomic scores

The input genomic ranges to the `scores()` method may have widths larger than one
nucleotide. In those cases, and when there is only one score per position, the
`scores()` method calculates, by default, the arithmetic mean of the scores across
each range.

```{r}
gr1 <- GRanges(seqnames="chr22", IRanges(start=50967020:50967025, width=1))
gr1 <- scores(gsco, gr1)
gr1
mean(gr1$scores)
gr2 <- GRanges(seqnames="chr22", IRanges(start=50967020, width=6))
scores(gsco, gr2)
```

However, we may change the way in which scores from multiple-nucleotide ranges are
summarized with the argument `summaryFun`, as follows.

```{r}
scores(gsco, gr2, summaryFun=max)
scores(gsco, gr2, summaryFun=min)
scores(gsco, gr2, summaryFun=median)
```

# Retrieval of quantized genomic scores

The specific quantization and dequantization functions are stored as part of
the metadata of a `GScores` object and they can be examined with the methods
`qfun()` and `dqfun()`, respectively. The latter is called by the `scores()`
method to retrieve genomic scores.

```{r}
phastqfun <- qfun(gsco)
phastqfun
phastdqfun <- dqfun(gsco)
phastdqfun
```

For single-nucleotide ranges, we can retrieve the quantized genomic scores
using the argument `quantized=TRUE`.

```{r}
gr1 <- scores(gsco, gr1, quantized=TRUE)
gr1
```

Using the dequantization function we can obtain later the genomic scores.

```{r}
phastdqfun(gr1$scores)
```

# Retrieval of minor allele frequency data

The `MafDb` class derived from `GSscores` enables the storage and access
of minor allele frequency (MAF) data. There are currently 9 different annotation
packages that store MAF values using the `r Biocpkg("GenomicScores")` package;
see Table \@ref(tab:tableMafDb) below.

Annotation Package          | Description
--------------------------- | --------------------------------------------------------------------------------------------
`r Biocpkg("MafDb.1Kgenomes.phase1.hs37d5")` | MAF data from the 1000 Genomes Project Phase 1 for the human genome version GRCh37.
`r Biocpkg("MafDb.1Kgenomes.phase3.hs37d5")` | MAF data from the 1000 Genomes Project Phase 3 for the human genome version GRCh37.
`r Biocpkg("MafDb.ESP6500SI.V2.SSA137.hs37d5")` | MAF data from NHLBI ESP 6500 exomes for the human genome version GRCh37.
`r Biocpkg("MafDb.ESP6500SI.V2.SSA137.GRCh38")` | MAF data from NHLBI ESP 6500 exomes for the human genome version GRCh38.
`r Biocpkg("MafDb.ExAC.r1.0.hs37d5")` | MAF data from ExAC 60706 exomes for the human genome version GRCh37.
`r Biocpkg("MafDb.ExAC.r1.0.nonTCGA.hs37d5")` | MAF data from ExAC 53105 nonTCGA exomes for the human genome version GRCh37.
`r Biocpkg("MafDb.gnomAD.r2.0.1.hs37d5")` | MAF data from gnomAD 15496 genomes for the human genome version GRCh37.
`r Biocpkg("MafDb.gnomADex.r2.0.1.hs37d5")` | MAF data from gnomADex 123136 exomes for the human genome version GRCh37.
`r Biocpkg("MafDb.TOPMed.freeze5.hg38")` | MAF data from NHLBI TOPMed 62784 genomes for the human genome version GRCh38.

: (\#tab:tableMafDb) Bioconductor annotation packages storing MAF data.

A `MafDb` object compresses the data in the same way as a `GScores` object but it
can store multiple versions of the data corresponding to MAF values derived from
different populations of individuals.

```{r, message=FALSE}
library(MafDb.1Kgenomes.phase1.hs37d5)

mafdb <- MafDb.1Kgenomes.phase1.hs37d5
mafdb

populations(mafdb)
```

Because `MafDb` objects derive from the `GScores` class, all the `GScores` methods
also apply.

```{r}
citation(mafdb)
provider(mafdb)
providerVersion(mafdb)
organism(mafdb)
seqlevelsStyle(mafdb)
```

The interface to access MAF values is also different to be more consistent with
the one of `SNPlocs.*` packages; see, for instance, `r Biocpkg("SNPlocs.Hsapiens.dbSNP144.GRCh37")`.
More concretely, `MafDb` objects should be queried with the following two functions:

  * `mafByOverlaps(x, ranges, pop="AF", type=c("snvs", "nonsnvs"), caching=TRUE)`: returns
    MAF values from the `MafDb` object in the argument `x`, whose positions overlap with the
    genomic ranges given in the argument `ranges` for the populations given in the argument
    `pop`. Additionally, one may query for single nucleotide variants -SNVs- (default) or 
    nonSNVs by indicating it in the argument `type`. The argument `caching` flags whether
    the queried data should be cahced into main memory (default).

  * `mafById(x, ids, pop, caching)`: return MAF values from the `MafDb` object in the argument
    `x` annotated by the data producer to the identifiers given in the argument `ids`. Arguments
    `pop` and `caching` work as in `mafByOverlaps()`.

Consider the following example in which we are interested in MAF values for variants associated
with _eye color_. We can start by fetching the corresponding variant identifers from the GWAS
catalog using the `r Biocpkg("gwascat")` package, as follows.

```{r, message=FALSE}
library(gwascat)
data(ebicat37)
eyersids <- subsetByTraits(ebicat37, tr="Eye color")$SNPS
eyersids
```

While we could directly interrogate a `MafDb` object with these identifiers and the function
`mafById()`, that query relies on a specific dbSNP annotation used by the MAF data producer to
annotate variants, which may not be the most up date nor may assign identifiers to every
variant. From this perspective, it is safer to first query the genomic position of the variant
identifiers with a recent release of dbSNP, as follows.

```{r, message=FALSE}
library(SNPlocs.Hsapiens.dbSNP144.GRCh37)
rng <- snpsById(SNPlocs.Hsapiens.dbSNP144.GRCh37, ids=eyersids)
rng
```

Finally, query the MAF values on those positions.

```{r}
eyecolormafs <- mafByOverlaps(mafdb, rng, c("AF", "EUR_AF", "AFR_AF"))
eyecolormafs
```

The following code produces the barplot shown in Figure \@ref(fig:eyecolormafs) illustrating
graphically the differences in MAF values from these variants between the three queried populations.

```{r eyecolormafs, fig.cap = "Eye color MAFs. Minor allele frequencies (MAFs) of variants associated with eye color for global, european and african populations of the Phase 1 data from the 1000 Genomes Project.", fig.height=5, fig.wide = TRUE, echo=TRUE}
par(mar=c(3, 5, 1, 1))
bp <- barplot(t(as.matrix(mcols(eyecolormafs))), beside=TRUE,
              col=c("darkblue", "darkgreen", "darkorange"),
              ylim=c(0, 0.55), las=1, ylab="Minor allele frequency")
axis(1, bp[2, ], 1:length(eyecolormafs))
mtext("Variant", side=1, line=2)
legend("topright", colnames(mcols(eyecolormafs)), fill=c("darkblue", "darkgreen", "darkorange"))
```

# Annotating variants with genomic scores

A typical use case of the `r Biocpkg("GenomicScores")` package is in the context of annotating variants
with genomic scores, such as phastCons conservation scores.
For this purpose, we load the `r Biocpkg("VariantAnnotaiton")` and
`r Biocpkg("TxDb.Hsapiens.UCSC.hg19.knownGene")` packages. The former will allow us to
read a VCF file and annotate it, and the latter contains the gene annotations from UCSC
that will be used in this process.

```{r, message=FALSE}
library(VariantAnnotation)
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
```

Let's load one of the sample VCF files that form part of the
`r Biocpkg("VariantAnnotation")` package.

```{r}
fl <- system.file("extdata", "chr22.vcf.gz", package="VariantAnnotation")
vcf <- readVcf(fl, "hg19")
seqlevelsStyle(vcf)
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
seqlevelsStyle(txdb)
```

Because the chromosome nomenclature from the VCF file (NCBI) is different 
from the one with the gene annotations (UCSC) we use the `seqlevelsStyle()`
function to force our variants having the chromosome nomenclature of the
gene annotations.

```{r}
seqlevelsStyle(vcf) <- seqlevelsStyle(txdb)
```

We annotate the location of variants using the function `locateVariants()`
from the `r Biocpkg("VariantAnnotation")` package.

```{r, message=FALSE}
loc <- locateVariants(vcf, txdb, AllVariants())
loc[1:3]
table(loc$LOCATION)
```

Annotate phastCons conservation scores on the variants and store
those annotations as an additional metadata column of the `GRanges` object.
For this specific purpose we should the argument `scores.only=TRUE` that
makes the `scores()` method to return the genomic scores as a numeric vector
instead as a metadata column in the input ranges object.

```{r}
loc$PHASTCONS <- scores(gsco, loc, scores.only=TRUE)
loc[1:3]
```

Using the following code we can examine the distribution of phastCons
conservation scores of variants across the different annotated regions,
shown in Figure \@ref(fig:plot1).

```{r plot1, fig.cap = "Distribution of phastCons conservation scores in variants across different annotated regions. Diamonds indicate mean values.", echo = FALSE, fig.height=5, fig.wide = TRUE, echo=TRUE}
x <- split(loc$PHASTCONS, loc$LOCATION)
mask <- elementNROWS(x) > 0
boxplot(x[mask], ylab="phastCons score", las=1, cex.axis=1.2, cex.lab=1.5, col="gray")
points(1:length(x[mask])+0.25, sapply(x[mask], mean, na.rm=TRUE), pch=23, bg="black")
```

Next, we can annotate M-CAP and CADD scores as follows. Note that we need to take
care to only query positions of single nucleotide variants, and using the `QUERYID`
column of the annotations to fetch back reference and alternative alleles from the
original VCF file container.

```{r}
maskSNVs <- isSNV(vcf)[loc$QUERYID]
loc$MCAP <- rep(NA_real_, length(loc))
loc$MCAP[maskSNVs] <- scores(mcap, loc[maskSNVs],
                             ref=ref(vcf)[loc$QUERYID[maskSNVs]],
                             alt=alt(vcf)[loc$QUERYID[maskSNVs]],
                             scores.only=TRUE)
```

```{r, echo=FALSE}
obj <- readRDS(system.file("extdata", "cadd.v1.3.hg19.chr22sub.rds", package="GenomicScores"))
mdobj <- metadata(obj)
cadd <- GScores(provider=mdobj$provider,
                provider_version=mdobj$provider_version,
                download_url=mdobj$download_url,
                download_date=mdobj$download_date,
                reference_genome=mdobj$reference_genome,
                data_pkgname=mdobj$data_pkgname,
                data_dirpath="../inst/extdata",
                data_serialized_objnames=c(cadd.v1.3.hg19.chr22.rds="cadd.v1.3.hg19.chr22sub.rds"))
scorlelist <- get(mdobj$data_pkgname, envir=cadd@.data_cache)
scorlelist[[mdobj$seqname]] <- obj
assign(mdobj$data_pkgname, scorlelist, envir=cadd@.data_cache)
```

```{r}
maskSNVs <- isSNV(vcf)[loc$QUERYID]
loc$CADD <- rep(NA_real_, length(loc))
loc$CADD[maskSNVs] <- scores(cadd, loc[maskSNVs],
                             ref=ref(vcf)[loc$QUERYID[maskSNVs]],
                             alt=alt(vcf)[loc$QUERYID[maskSNVs]],
                             scores.only=TRUE)
```

Using the code below we can produce the plot of Figure \@ref(fig:mcapvscadd) comparing
CADD and M-CAP scores and labeling the location of the variants from which they are derived.

```{r mcapvscadd, fig.cap = "Comparison of M-CAP and CADD scores. Values on the x- and y-axis are jittered to facilitate visualization.", echo = FALSE, fig.height=5, fig.width=7, dpi=100, echo=TRUE}
library(RColorBrewer)
par(mar=c(4, 5, 1, 1))
hmcol <- colorRampPalette(brewer.pal(nlevels(loc$LOCATION), "Set1"))(nlevels(loc$LOCATION))
plot(jitter(loc$MCAP, factor=2), jitter(loc$CADD, factor=2), pch=19,
     col=hmcol, xlab="M-CAP scores", ylab="CADD scores",
     las=1, cex.axis=1.2, cex.lab=1.5, panel.first=grid())
legend("bottomright", levels(loc$LOCATION), pch=19, col=hmcol, inset=0.01)
```
Finally, we will show how to annotate MAF values on these variants. However,
in this particular case, we should take care of the different sequence styles
(UCSC vs NCBI) and genome version nomenclatures (hg19 vs. hs37d5) between the
annotated variants and the `MafDb` object.

```{r}
seqlevelsStyle(loc) <- seqlevelsStyle(mafdb)[1]
seqinfo(loc, new2old=match(seqlevels(mafdb), seqlevels(loc))) <- seqinfo(mafdb)
loc$MAF[width(loc) == 1] <- mafByOverlaps(mafdb, loc[width(loc) == 1])$AF
loc$MAF[width(loc) > 1] <- mafByOverlaps(mafdb, loc[width(loc) > 1], type="nonsnvs")$AF
loc[1:3]
```

# Comparison between lossy-compressed and original phastCons scores

To have a sense of the extent of the trade-off between precision and compression in a specific case,
we compare here original _phastCons_ scores with the ones obtained by rounding their precision to
one significant figure, and stored in the annotation package `phastCons100way.UCSC.hg19`.
Because _phastCons_  scores measure conservation, we sampled uniformly at random one
thousand _phastCons_ scores from differently conserved regions, concretely CDS and 3'UTR.
These sampled scores are included in this package to illustrate this comparison.
Interestingly, among the _phastCons_ scores sampled from 1000 CDS positions,
there are only 198 different values despite the apparently very high precision of some of them.

```{r showpositions, message=FALSE, cache=FALSE}
origpcscoCDS <- readRDS(system.file("extdata", "origphastCons100wayhg19CDS.rds", package="GenomicScores"))
origpcscoCDS

length(unique(origpcscoCDS$score))
```

We look more closely the number of significant figures of precision used in these original _phastCons_ scores.

```{r}
numDecimals <- function(x) {
  spl <- strsplit(as.character(x+1), "\\.")
  spl <- sapply(spl, "[", 2)
  spl[is.na(spl)] <- ""
  nchar(spl)
}

nd1 <- numDecimals(origpcscoCDS$score)
table(nd1)
```

Similarly, in 3'UTR regions, only 209 unique phastCons scores are observed.

```{r showpositions2, message=FALSE, cache=FALSE}
origpcsco3UTRs <- readRDS(system.file("extdata", "origphastCons100wayhg193UTR.rds", package="GenomicScores"))
origpcsco3UTRs

length(table(origpcsco3UTRs$score))

nd2 <- numDecimals(origpcsco3UTRs$score)
table(nd2)
```

Retrieve the corresponding _phastCons_ scores stored in the annotation package.

```{r}
pkgpcscoCDS <- scores(gsco, origpcscoCDS, scores.only=TRUE)
pkgpcsco3UTRs <- scores(gsco, origpcsco3UTRs, scores.only=TRUE)
```

In Figure \@ref(fig:plot2) we show a visual comparison between raw and rounded _phastCons_ scores.
The two panels on top compare the whole range of scores observed in CDS (left) and 3'UTR (right) regions.
However, the rounding effect can be better observed in the cumulative distributions shown in
the panels at the bottom, again for CDS (left) and 3'UTR (right) regions.

In these bottom panels, phastcons scores in CDS and 3'UTR regions display very different cumulative distributions.
In CDS regions, most of the genomic scores (>60%) are found between the values of 0.9 and 1.0, while around 25% of
the scores are found below 0.1. Indeed, these are the range of values where lossy compression loses more precison.
The cumulative distribution of 3'UTR shows the same critical points, with the difference that most of scores are
found below 0.1 (>70%).

```{r plot2, fig.height=9, fig.width=8, dpi=100, fig.cap = "Original and lossy-compressed phastCons scores. Top panels (a, b): comparison of the distribution of values. Bottom panels (c, d): comparison of the cumulative distribution", echo = FALSE}
labelPanel <- function(lab, font=2, cex=2, offsetx=0.05, offsety=0.05) {
  par(xpd=TRUE)
  w <- par("usr")[2] - par("usr")[1]
  h <- par("usr")[4] - par("usr")[3]
  text(par("usr")[1]-w*offsetx, par("usr")[4]+h*offsety, lab, font=font, cex=cex)
  par(xpd=FALSE)
}

par(mfrow=c(2, 2), mar=c(4, 5, 2, 1))
plot(origpcscoCDS$score, jitter(pkgpcscoCDS), pch=19, cex=1, cex.lab=1.2,
     xaxt="n", yaxt="n", xlab="Original phastCons scores (CDS)",
     ylab="Compressed phastCons scores (CDS)")
axis(1, at=seq(0, 1, by=0.1), labels=seq(0, 1, by=0.1), las=1)
axis(2, at=seq(0, 1, by=0.1), labels=seq(0, 1, by=0.1), las=1)
abline(h=seq(0, 1, by=0.1), v=seq(0, 1, by=0.1), lty=3, col="gray")
abline(0, 1)
labelPanel(letters[1])
plot(origpcsco3UTRs$score, jitter(pkgpcsco3UTRs), pch=19, cex=1, cex.lab=1.2,
     xaxt="n", yaxt="n", xlab="Original phastCons scores (3' UTR)",
     ylab="Compressed phastCons scores (3' UTR)")
axis(1, at=seq(0, 1, by=0.1), labels=seq(0, 1, by=0.1), las=1)
axis(2, at=seq(0, 1, by=0.1), labels=seq(0, 1, by=0.1), las=1)
abline(h=seq(0, 1, by=0.1), v=seq(0, 1, by=0.1), lty=3, col="gray")
abline(0, 1)
labelPanel(letters[2])
ForigCDS <- ecdf(origpcscoCDS$score)
FpkgCDS <- ecdf(pkgpcscoCDS)
plot(sort(origpcscoCDS$score), ForigCDS(sort(origpcscoCDS$score)), xaxt="n", yaxt="n", cex.lab=1.2,
     pch=".", cex=4, xlab="phastCons scores (CDS)", ylab="F(x)", ylim=c(0, 1))
axis(1, at=seq(0, 1, by=0.1), labels=seq(0, 1, by=0.1), las=1)
axis(2, at=seq(0, 1, by=0.1), labels=seq(0, 1, by=0.1), las=1)
abline(h=seq(0, 1, by=0.1), v=seq(0, 1, by=0.1), lty=3, col="gray")
points(sort(pkgpcscoCDS), FpkgCDS(sort(pkgpcscoCDS)), pch=19, cex=1)
legend("topleft", c("Original score", "Rounded score"), pch=c(46, 19),
       pt.cex=c(4, 1), inset=0.01, bg="white")
labelPanel(letters[3])
Forig3UTRs <- ecdf(origpcsco3UTRs$score)
Fpkg3UTRs <- ecdf(pkgpcsco3UTRs)
plot(sort(origpcsco3UTRs$score), Forig3UTRs(sort(origpcsco3UTRs$score)), xaxt="n", yaxt="n", cex.lab=1.2,
     pch=".", cex=4, xlab="phastCons scores (3'UTR)", ylab="F(x)", ylim=c(0, 1))
axis(1, at=seq(0, 1, by=0.1), labels=seq(0, 1, by=0.1), las=1)
axis(2, at=seq(0, 1, by=0.1), labels=seq(0, 1, by=0.1), las=1)
abline(h=seq(0, 1, by=0.1), v=seq(0, 1, by=0.1), lty=3, col="gray")
points(sort(pkgpcsco3UTRs), Fpkg3UTRs(sort(pkgpcsco3UTRs)), pch=19, cex=1)
legend("topleft", c("Original score", "Rounded score"), pch=c(46, 19),
       pt.cex=c(4, 1), inset=0.01, bg="white")
labelPanel(letters[4])
```

The bottom plots in Figure \@ref(fig:plot2) also reveal that when the cumulative distribution is
of interest, such as in the context of filtering genetic variants above or below certain threshold
of conservation, the quantization of _phastCons_ scores to one significant figure provides sufficient
precision for a wide range of conservation values.

# Session information

```{r session_info, cache=FALSE}
sessionInfo()
```

# References