--- title: "An introduction to the GenomicScores package" author: - name: Pau Puigdevall affiliation: - &id Dept. of Medicine and Life 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_document: toc: true toc_float: true number_sections: true bibliography: bibliography.bib --- ```{r setup, echo=FALSE, cache=FALSE} library(knitr) options(width=80) knitr::opts_chunk$set( collapse=TRUE, comment="", fig.align="center", fig.wide=TRUE ) ``` # 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} install.packages("BiocManager") BiocManager::install("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.hg38")`. # 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(availability-and-retrieval-of-genomic-scores)) or as `r Biocpkg("AnnotationHub")` resources (Section \@ref(genomic-scores-as-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] or CADD [@kircher14]. 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, while it also provides rich metadata on the provenance, citation and licensing of the original data. # Availability and retrieval of genomic scores The access to genomic scores through `GScores` objects is available either through annotation packages or as `r Biocpkg("AnnotationHub")` resources. To find out what kind of genomic scores are available, through which mechanism, and in which organism, we may use the function `availableGScores()`. ```{r} avgs <- availableGScores() avgs ``` For example, if we want to use the phastCons conservation scores available through the annotation package `r Biocpkg("phastCons100way.UCSC.hg38")`, we should first install it (we only need to do this once). ```{r, eval=FALSE} BiocManager::install("phastCons100way.UCSC.hg38") ``` Second, we should load the package, and a `GScores` object will be created and named after the package name, during the loading operation. It is often handy to shorten that name. ```{r, message=FALSE, warning=FALSE, cache=FALSE} library(phastCons100way.UCSC.hg38) phast <- phastCons100way.UCSC.hg38 class(phast) ``` Typing the name of the `GScores` object shows a summary of its contents and some of its metadata. ```{r} phast ``` The bibliographic reference to cite the genomic score data stored in a `GScores` object can be accessed using the `citation()` method either on the package name (in case of annotation packages), or on the `GScores` object. ```{r} citation(phast) ``` 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(phast) providerVersion(phast) organism(phast) seqlevelsStyle(phast) ``` To retrieve genomic scores for specific consecutive positions we should use the method `gscores()`, as follows. ```{r} gscores(phast, GRanges(seqnames="chr22", IRanges(start=50528591:50528596, width=1))) ``` For a single position we may use this other `GRanges()` constructor. ```{r} gscores(phast, GRanges("chr22:50528593")) ``` We may also retrieve the score values only with the method `score()`. ```{r} score(phast, GRanges(seqnames="chr22", IRanges(start=50528591:50528596, width=1))) score(phast, GRanges("chr22:50528593")) ``` Let's illustrate how to retrieve phastCons scores using data from the GWAS catalog available through the Bioconductor package `r Biocpkg("gwascat")`. For the purpose of this vignette, we will filter the GWAS catalog data by (1) discarding entries with `NA` values in either chromosome name or position, or with multiple positions; (2) storing the data into a `GRanges` object, including the GWAS catalog columns `STRONGEST SNP-RISK ALLELE` and `MAPPED_TRAIT`, and the reference and alternate alleles, as metadata columns; (4) restricting variants to those located in chromosomes 20 to 22; and (3) excluding variants with multinucleotide alleles, or where reference and alternate alleles are identical. ```{r, eval=FALSE} library(BSgenome.Hsapiens.UCSC.hg38) library(gwascat) gwc <- get_cached_gwascat() mask <- !is.na(gwc$CHR_ID) & !is.na(gwc$CHR_POS) & !is.na(as.integer(gwc$CHR_POS)) gwc <- gwc[mask, ] grstr <- sprintf("%s:%s-%s", gwc$CHR_ID, gwc$CHR_POS, gwc$CHR_POS) gwcgr <- GRanges(grstr, RISK_ALLELE=gwc[["STRONGEST SNP-RISK ALLELE"]], MAPPED_TRAIT=gwc$MAPPED_TRAIT) seqlevelsStyle(gwcgr) <- "UCSC" mask <- seqnames(gwcgr) %in% c("chr20", "chr21", "chr22") gwcgr <- gwcgr[mask] ref <- as.character(getSeq(Hsapiens, gwcgr)) alt <- gsub("rs[0-9]+-", "", gwcgr$RISK_ALLELE) mask <- (ref %in% c("A", "C", "G", "T")) & (alt %in% c("A", "C", "G", "T")) & nchar(alt) == 1 & ref != alt gwcgr <- gwcgr[mask] mcols(gwcgr)$REF <- ref[mask] mcols(gwcgr)$ALT <- alt[mask] ``` ```{r, message=FALSE, echo=FALSE} library(BSgenome.Hsapiens.UCSC.hg38) library(gwascat) gwcgr <- readRDS(system.file("extdata", "gwcgr_chr20-22_151123.rds", package="GenomicScores")) ``` ```{r} gwcgr ``` Finally, let's obtain the phastCons scores for this GWAS catalog variant set, and examine their summary and cumulative distribution. ```{r} pcsco <- score(phast, gwcgr) summary(pcsco) round(cumsum(table(na.omit(pcsco))) / sum(!is.na(pcsco)), digits=2) ``` We can observe that only 10% of the variants in chromosomes 20 to 22 have a conservation phastCons score above 0.5. Let's examine which traits have more fully conserved variants. ```{r} xtab <- table(gwcgr$MAPPED_TRAIT[pcsco == 1]) head(xtab[order(xtab, decreasing=TRUE)]) ``` # Genomic scores as AnnotationHub resources The `r Biocpkg("AnnotationHub")` (AH), is a Bioconductor 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. An AH web resource creates and manages a local cache of files retrieved by the user, helping with quick and reproducible access. We can quickly check for the available AH resources by subsetting as follows the resources names from the previous table obtained with `availableGScores()`. ```{r} rownames(avgs)[avgs$AnnotationHub] ``` The selected resource can be downloaded with the function getGScores(). After the resource is downloaded the first time, the cached copy will enable a quicker retrieval later. Let's download other conservation scores, the phyloP scores [@pollard2010detection], for human genome version hg38. ```{r, echo=FALSE} obj20 <- readRDS(system.file("extdata", "phyloP100way.UCSC.hg38.chr20.rds", package="GenomicScores")) obj21 <- readRDS(system.file("extdata", "phyloP100way.UCSC.hg38.chr21.rds", package="GenomicScores")) obj22 <- readRDS(system.file("extdata", "phyloP100way.UCSC.hg38.chr22.rds", package="GenomicScores")) mdobj <- metadata(obj20) phylop <- GScores(provider=mdobj$provider, provider_version=mdobj$provider_version, download_url=mdobj$download_url, download_date=mdobj$download_date, reference_genome=mdobj$reference_genome, data_pkgname=mdobj$data_pkgname, data_dirpath="../inst/extdata", data_serialized_objnames=c(phyloP100way.UCSC.hg38.chr20.rds="phyloP100way.UCSC.hg38.chr20.rds", phyloP100way.UCSC.hg38.chr21.rds="phyloP100way.UCSC.hg38.chr21.rds", phyloP100way.UCSC.hg38.chr22.rds="phyloP100way.UCSC.hg38.chr22.rds"), data_tag="phyloP", license="", license_url="", license_reqconsent=FALSE) gscopops <- get(mdobj$data_pkgname, envir=phylop@.data_cache) gscopops[["default"]] <- RleList(compress=FALSE) gscopops[["default"]][[mdobj$seqname]] <- obj20 assign(mdobj$data_pkgname, gscopops, envir=phylop@.data_cache) ``` ```{r message=FALSE, cache=FALSE, eval=FALSE} phylop <- getGScores("phyloP100way.UCSC.hg38") ``` ```{r} phylop ``` Let's retrieve the phyloP conservation scores for the previous set of GWAS catalog variants and compare them in Figure \@(fig:phastvsphylop). ```{r phastvsphylop, fig.cap = "Comparison between phastCons and phyloP conservation scores. On the y-axis, phyloP scores as function of phastCons scores on the x-axis, for a set of GWAS catalog variant in the human chromosome 22.", fig.height=5, fig.wide=TRUE, echo=TRUE} ppsco <- score(phylop, gwcgr) plot(pcsco, ppsco, xlab="phastCons", ylab="phyloP", cex.axis=1.2, cex.lab=1.5, las=1) ``` We may observe that the values match in a rather discrete manner due to the quantization of the scores. In the case of the phastCons annotation package `r Biocpkg("phastCons100way.UCSC.hg38")`, the `GScore` object gives access in fact to two score populations, the default one in which conservation scores are rounded to 1 decimal place, and an alternative one, named `DP2`, in which they are rounded to 2 decimal places. To figure out what are the available score populations in a `GScores` object, we should use the method `populations()`. ```{r} populations(phast) ``` Whenever one of these populations is called `default`, this is the one used by default. In other cases we can find out which is the default population as follows: ```{r} defaultPopulation(phast) ``` To use one of the available score populations we should use the argument `pop` in the corresponding method, as follows. ```{r} pcsco2 <- score(phast, gwcgr, pop="DP2") head(pcsco2) ``` Figure \@ref(fig:phastvsphylop2) below shows again the comparison of phastCons and phyloP conservation scores, this time at the higher resolution provided by the phastCons scores rounded at two decimal places. ```{r phastvsphylop2, fig.cap = "Comparison between phastCons and phyloP conservation scores at a higher resolution. On the y-axis, phyloP scores as function of phastCons scores on the x-axis, for a set of GWAS catalog variant in the human chromosome 22.", fig.height=5, fig.wide=TRUE, echo=TRUE} plot(pcsco2, ppsco, xlab="phastCons", ylab="phyloP", cex.axis=1.2, cex.lab=1.5, las=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 instance in high-performance computing (HPC) environments. For that purpose, we can create ourselves an annotation package, such as [phastCons100way.UCSC.hg38](https://bioconductor.org/packages/phastCons100way.UCSC.hg38), from a `GScores` object corresponding to a downloaded `AnnotationHub` resource. To do that we use the function `makeGScoresPackage()` as follows: ```{r eval=FALSE} makeGScoresPackage(phast, maintainer="Me ", author="Me", version="1.0.0") ``` ```{r echo=FALSE} cat("Creating package in ./phastCons100way.UCSC.hg38\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 minor allele frequency data One particular type of genomic scores that are accessible through the `GScores` class is minor allele frequency (MAF) data. There are currently 15 annotation packages that store MAF values using the `r Biocpkg("GenomicScores")` package, named using the prefix `MafDb` or `MafH5`; 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.phase1.GRCh38")` | MAF data from the 1000 Genomes Project Phase 1 for the human genome version GRCh38. `r Biocpkg("MafDb.1Kgenomes.phase3.hs37d5")` | MAF data from the 1000 Genomes Project Phase 3 for the human genome version GRCh37. `r Biocpkg("MafDb.1Kgenomes.phase3.GRCh38")` | MAF data from the 1000 Genomes Project Phase 3 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.GRCh38")` | MAF data from ExAC 60706 exomes for the human genome version GRCh38. `r Biocpkg("MafDb.ExAC.r1.0.nonTCGA.hs37d5")` | MAF data from ExAC 53105 nonTCGA exomes for the human genome version GRCh37. `r Biocpkg("MafDb.ExAC.r1.0.nonTCGA.GRCh38")` | MAF data from ExAC 53105 nonTCGA exomes for the human genome version GRCh38. `r Biocpkg("MafDb.gnomAD.r2.1.hs37d5")` | MAF data from gnomAD 15496 genomes for the human genome version GRCh37. `r Biocpkg("MafDb.gnomAD.r2.1.GRCh38")` | MAF data from gnomAD 15496 genomes for the human genome version GRCh38. `r Biocpkg("MafDb.gnomADex.r2.1.hs37d5")` | MAF data from gnomADex 123136 exomes for the human genome version GRCh37. `r Biocpkg("MafDb.gnomADex.r2.1.GRCh38")` | MAF data from gnomADex 123136 exomes for the human genome version GRCh38. `r Biocpkg("MafH5.gnomAD.v4.0.GRCh38")` | MAF data from gnomAD 76156 genomes for the human genome version GRCh38. `r Biocpkg("MafDb.TOPMed.freeze5.hg19")` | MAF data from NHLBI TOPMed 62784 genomes 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. In this type of package, the scores populations correspond to populations of individuals from which the MAF data were derived, and all MAF data were compressed using a precision of one significant figure for MAF < 0.1 and two significant figures for MAF >= 0.1. Let's load the MAF package for the release v4.0 of gnomAD [@chen2024genomic]. ```{r, message=FALSE, warning=FALSE, cache=FALSE} library(MafH5.gnomAD.v4.0.GRCh38) mafh5 <- MafH5.gnomAD.v4.0.GRCh38 mafh5 populations(mafh5) ``` Let's retrieve the gnomAD MAF values for the previous GWAS catalog variant set and examine its distribution, and how many variants occur in less than 1% of all gnomAD populations and what fraction do they represent among the analyzed variants. ```{r} mafs <- score(mafh5, gwcgr, pop="AF_allpopmax") summary(mafs) sum(mafs < 0.01, na.rm=TRUE) sum(mafs < 0.01, na.rm=TRUE) / sum(!is.na(mafs)) ``` Finally, let's examine which traits have more such rare variants. ```{r} xtab <- table(gwcgr$MAPPED_TRAIT[mafs < 0.01]) head(xtab[order(xtab, decreasing=TRUE)]) ``` # Retrieval of multiple scores per genomic position Among the score sets available as [AnnotationHub](https://bioconductor.org/packages/AnnotationHub) web resources shown in the previous section, some of them, such as CADD [@kircher14], M-CAP [@jagadeesh16] or AlphaMissense [@cheng2023accurate], provide multiple scores per genomic position that capture the tolerance to mutations of single nucleotides. Such type of scores, often used to establish the potential pathogenicity of variants, are sometimes released under some sort of license for a non-commercial use. In such cases, the function `getGScores()` will ask us interactively to accept the license. We can also set the argument `accept.license=TRUE` to accept it non-interactively. We will illustrate such a case using the AlphaMissense scores [@cheng2023accurate]. ```{r, echo=FALSE} obj20 <- readRDS(system.file("extdata", "AlphaMissense.v2023.hg38.chr20.rds", package="GenomicScores")) obj21 <- readRDS(system.file("extdata", "AlphaMissense.v2023.hg38.chr21.rds", package="GenomicScores")) obj22 <- readRDS(system.file("extdata", "AlphaMissense.v2023.hg38.chr22.rds", package="GenomicScores")) mdobj <- metadata(obj20) am23 <- GScores(provider=mdobj$provider, provider_version=mdobj$provider_version, download_url=mdobj$download_url, download_date=mdobj$download_date, reference_genome=mdobj$reference_genome, data_pkgname=mdobj$data_pkgname, data_dirpath="../inst/extdata", data_serialized_objnames=c(AlphaMissense.v2023.hg38.chr20.rds="AlphaMissense.v2023.hg38.chr20.rds", AlphaMissense.v2023.hg38.chr21.rds="AlphaMissense.v2023.hg38.chr21.rds", AlphaMissense.v2023.hg38.chr22.rds="AlphaMissense.v2023.hg38.chr22.rds"), data_tag="AlphaMissense", license=mdobj$license, license_url=mdobj$license_url, license_reqconsent=TRUE) gscopops <- get(mdobj$data_pkgname, envir=am23@.data_cache) gscopops[["default"]] <- RleList(compress=FALSE) gscopops[["default"]][[mdobj$seqname]] <- obj20 assign(mdobj$data_pkgname, gscopops, envir=am23@.data_cache) ``` ```{r, eval=FALSE} am23 <- getGScores("AlphaMissense.v2023.hg38") These data is shared under the license CC BY-NC-SA 4.0 (see https://creativecommons.org/licenses/by-nc-sa/4.0), do you accept it? [y/n]: y ``` Let's retrieve the AlphaMissense scores for the reference and alternate alleles in our GWAS catalog variant set. ```{r} am23 amsco <- score(am23, gwcgr, ref=gwcgr$REF, alt=gwcgr$ALT) summary(amsco) ``` Using the cutoffs for AlphaMissense scores reported in [@cheng2023accurate] to classify variants into "likely benign", "ambiguous" and "likely pathogenic", and 0.01 and 0.1 as MAF cutoffs, let's cross-tabulate the proportions of these two factors. ```{r} mask <- !is.na(amsco) & !is.na(mafs) amscofac <- cut(amsco[mask], breaks=c(0, 0.34, 0.56, 1)) amscofac <- relevel(amscofac, ref="(0.56,1]") maffac <- cut(mafs[mask], breaks=c(0, 0.01, 0.1, 1)) xtab <- table(maffac, amscofac) t(xtab) xtab <- t(xtab / rowSums(xtab)) round(xtab, digits=2) ``` Figure \@ref(fig:ChengEtAl2024Fig5b) below displays graphically these proportions in an analogous way to the one shown in Figure 5B from @cheng2023accurate. While these proportions are quite different to the original figure, due to the much lower number of variants analyzed here, we still can see, like in [@cheng2023accurate], that the proportion of variants classified as _likely pathogenic_ by AlphaMissense scores is much larger for rare variants with MAF < 0.01 than for common variants with MAF > 0.01. ```{r ChengEtAl2024Fig5b, echo=FALSE, height=5, width=7, fig.cap="AlphaMissense predictions. Proportions of three ranges of AlphaMissense pathogenicity scores for three ranges of minor allele frequencies (MAF) derived from gnomAD, on data from the GWAS catalog. This figure is analogous to Figure 5B from @cheng2023accurate, but with much fewer variants."} par(mar=c(4, 5, 3, 1)) barplot(xtab, horiz=TRUE, col=c("red", "royalblue", "gray"), xlab="Proportion", ylab="MAF", cex.axis=1.2, cex.names=1.2, cex.lab=1.5) par(xpd=TRUE) legend(0.17, 4.1, c("likely pathogenic", "likely benign", "ambiguous"), fill=c("red", "royalblue", "gray"), inset=0.01, horiz=TRUE) par(xpd=FALSE) ``` # Summarization of genomic scores The input genomic ranges to the `gscores()` method may have widths larger than one nucleotide. In those cases, and when there is only one score per position, the `gscores()` method calculates, by default, the arithmetic mean of the scores across each range. ```{r} gr1 <- GRanges(seqnames="chr22", IRanges(start=50528591:50528596, width=1)) gr1sco <- gscores(phast, gr1) gr1sco mean(gr1sco$default) gr2 <- GRanges("chr22:50528591-50528596") gscores(phast, gr2) ``` However, we may change the way in which scores from multiple-nucleotide ranges are summarized with the argument `summaryFun`, as follows. ```{r} gscores(phast, gr2, summaryFun=max) gscores(phast, gr2, summaryFun=min) gscores(phast, gr2, summaryFun=median) ``` # Annotating variants with genomic scores A typical use case of the `r Biocpkg("GenomicScores")` package is in the context of annotating variants with genomic scores, such as phastCons conservation scores. For this purpose, we load the `r Biocpkg("VariantAnnotaiton")` and `r Biocpkg("TxDb.Hsapiens.UCSC.hg38.knownGene")` packages. The former will allow us annotate variants, and the latter contains the gene annotations from UCSC that will be used in this process. ```{r, message=FALSE, warning=FALSE, cache=FALSE} library(VariantAnnotation) library(TxDb.Hsapiens.UCSC.hg38.knownGene) txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene ``` We annotate the location of previous set of filtered GWAS variants, using the function `locateVariants()` from the `r Biocpkg("VariantAnnotation")` package. ```{r, message=FALSE, warning=FALSE} loc <- locateVariants(gwcgr, txdb, AllVariants()) loc[1:3] table(loc$LOCATION) ``` Now we 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 use the method `score()` that returns the genomic scores as a numeric vector, instead of doing it as a metadata column in the input ranges object, done by the `gscores()` function. ```{r} loc$PHASTCONS <- score(phast, loc, pop="DP2") loc[1:3] ``` Using the following code we can examine the distribution of phastCons conservation scores of variants across the different annotated regions, shown in Figure \@ref(fig:plot1). ```{r plot1, fig.cap = "Distribution of phastCons conservation scores in variants across different annotated regions. Diamonds indicate mean values.", echo = FALSE, fig.height=5, fig.wide = TRUE, echo=TRUE} x <- split(loc$PHASTCONS, loc$LOCATION) mask <- elementNROWS(x) > 0 boxplot(x[mask], ylab="phastCons score", las=1, cex.axis=1.2, cex.lab=1.5, col="gray") points(1:length(x[mask])+0.25, sapply(x[mask], mean, na.rm=TRUE), pch=23, bg="black") ``` Next, we can annotate AlphaMissense and CADD scores as follows. Note that we use the `QUERYID` column of the annotations to fetch back reference and alternative alleles from the original data container. ```{r} loc$AM <- score(am23, loc, ref=gwcgr$REF[loc$QUERYID], alt=gwcgr$ALT[loc$QUERYID]) ``` ```{r, echo=FALSE} obj20 <- readRDS(system.file("extdata", "cadd.v1.6.hg38.chr20.rds", package="GenomicScores")) obj21 <- readRDS(system.file("extdata", "cadd.v1.6.hg38.chr21.rds", package="GenomicScores")) obj22 <- readRDS(system.file("extdata", "cadd.v1.6.hg38.chr22.rds", package="GenomicScores")) mdobj <- metadata(obj20) 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.v1.6.hg38.chr20.rds="cadd.v1.6.hg38.chr20.rds", cadd.v1.6.hg38.chr21.rds="cadd.v1.6.hg38.chr21.rds", cadd.v1.6.hg38.chr22.rds="cadd.v1.6.hg38.chr22.rds"), data_tag="CADD") gscopops <- get(mdobj$data_pkgname, envir=cadd@.data_cache) gscopops[["default"]] <- RleList(compress=FALSE) gscopops[["default"]][[mdobj$seqname]] <- obj20 assign(mdobj$data_pkgname, gscopops, envir=cadd@.data_cache) ``` ```{r} cadd loc$CADD <- score(cadd, loc, ref=gwcgr$REF[loc$QUERYID], alt=gwcgr$ALT[loc$QUERYID]) ``` Using the code below we can produce the plot of Figure \@ref(fig:am23vscadd) comparing AlphaMissense and CADD scores and labeling the location of the variants from which they are derived. ```{r am23vscadd, fig.cap = "Comparison of AlphaMissense and CADD scores. Values on the 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(loc$AM, jitter(loc$CADD, factor=2), pch=19, col=hmcol, xlab="AlphaMissense 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) ``` # Session information ```{r session_info, cache=FALSE} sessionInfo() ``` # References