Converting IntroToAnnotationDbi.Rnw to Rmarkdown

DESCRIPTION

@@ -8,8 +8,15 @@ BugReports: https://github.com/Bioconductor/AnnotationDbi/issues
 Version: 1.63.2
 License: Artistic-2.0
 Encoding: UTF-8
-Author: Hervé Pagès, Marc Carlson, Seth Falcon, Nianhua Li
-Maintainer: Bioconductor Package Maintainer <[email protected]>
+Authors@R: c(
+	person("Hervé", "Pagès", role="aut"),
+	person("Marc", "Carlson", role="aut"),
+	person("Seth", "Falcon", role="aut"),
+	person("Nianhua", "Li", role="aut"),
+	person("Bioconductor Package Maintainer", role="cre",
+    	  email="[email protected]"),
+	person("Beryl", "Kanali", role="ctb",
+	  comment="Converted 'IntoToAnnotationPackages' vignette from Sweave to RMarkdown."))
 Depends: R (>= 2.7.0), methods, stats4, BiocGenerics (>= 0.29.2),
 	Biobase (>= 1.17.0), IRanges
 Imports: DBI, RSQLite, S4Vectors (>= 0.9.25), stats, KEGGREST

vignettes/IntroToAnnotationPackages.Rmd

@@ -0,0 +1,361 @@
+---
+title: "AnnotationDbi: Introduction To Bioconductor Annotation Packages"
+author: 
+  - name: "Marc Carlson"
+  - name: "Beryl Kanali"
+    affiliation: "Converted vignette from Sweave to Rmarkdown"
+date: "Last modified: November 2022; Compiled: `r format(Sys.time(), '%B %d , %Y')`"
+
+vignette: >
+  %\VignetteIndexEntry{1. Introduction To Bioconductor Annotation Packages}
+  %\VignetteDepends{org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, EnsDb.Hsapiens.v75}
+  %\VignetteKeywords{annotation, database}
+  %\VignettePackage{AnnotationDbi}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+output:
+  BiocStyle::html_document
+---
+
+```{r dbtypes, fig.cap = "Annotation Packages: the big picture", echo = FALSE, fig.width=0.6}
+knitr::include_graphics("databaseTypes.png")
+```
+
+*Bioconductor* provides extensive annotation resources. These can be
+*gene centric*, or *genome centric*. Annotations can be provided in packages
+curated by *Bioconductor*, or obtained from web-based resources. This vignette
+is primarily concerned with describing the annotation resources that are
+available as packages. More advanced users who wish to learn about how to make
+new annotation packages should see the vignette titled "Creating select
+Interfaces for custom Annotation resources" from the 
+`r Biocpkg("AnnotationForge")` package.
+
+Gene centric `r Biocpkg("AnnotationDbi")` packages include: 
+
+- Organism level: e.g. `r Biocpkg("org.Mm.eg.db")`. 
+- Platform level: e.g. `r Biocpkg("hgu133plus2.db")`, 
+`r Biocpkg("hgu133plus2.probes")`, `r Biocpkg("hgu133plus2.cdf")` 
+- System-biology level: `r Biocpkg("GO.db")`
+
+
+Genome centric `r Biocpkg("GenomicFeatures")` packages include
+
+-   Transcriptome level: e.g. `r Biocpkg("TxDb.Hsapiens.UCSC.hg19.knownGene")`,
+`r Biocpkg("EnsDb.Hsapiens.v75")`.
+-   Generic genome features: Can generate via `r Biocpkg("GenomicFeatures")`
+
+One web-based resource accesses [biomart](http://www.biomart.org/), via the 
+`r Biocpkg("biomaRt")` package: 
+
+- Query web-based \`biomart' resource for genes,
+sequence, SNPs, and etc.
+
+The most popular annotation packages have been modified so that they can make
+use of a new set of methods to more easily access their contents. These
+four methods are named: `columns`, `keytypes`, `keys` and `select`. And they are
+described in this vignette. They can currently be used with all chip, organism,
+and `TxDb` packages along with the popular `r Biocpkg("GO.db")` package.
+
+For the older less popular packages, there are still convenient ways to
+retrieve the data. The *How to use bimaps from the ".db" annotation packages*
+vignette in the `r Biocpkg("AnnotationDbi")` package is a key reference for
+learnign about how to use bimap objects.
+
+Finally, all of the \`.db' (and most other *Bioconductor* annotation packages)
+are updated every 6 months corresponding to each release of *Bioconductor*.
+Exceptions are made for packages where the actual resources that the packages
+are based on have not themselves been updated.
+
+# AnnotationDb objects and the select method
+
+As previously mentioned, a new set of methods have been added that allow a
+simpler way of extracting identifier based annotations. All the annotation
+packages that support these new methods expose an object named exactly the same
+as the package itself. These objects are collectively called *AnnotationDb*
+objects for the class that they all inherit from. The more specific classes (the
+ones that you will actually see in the wild) have names like *OrgDb*, *ChipDb*
+or *TxDb* objects. These names correspond to the kind of package (and underlying
+schema) being represented. The methods that can be applied to all of these
+objects are `columns`, `keys`, `keytypes` and `select`.
+
+In addition, another accessor has recently been added which allows extraction of
+one column at a time. the `mapIds` method allows users to extract data into
+either a named character vector, a list or even a SimpleCharacterList. This
+method should work with all the different kinds of *AnnotationDb* objects
+described below.
+
+# ChipDb objects and the select method
+
+An extremely common kind of `r Biocpkg("Annotation")` package is the so called
+platform based or chip based package type. This package is intended to make the
+manufacturer labels for a series of probes or probesets to a wide range of
+gene-based features. A package of this kind will load an *ChipDb* object. Below
+is a set of examples to show how you might use the standard 4 methods to
+interact with an object of this type.
+
+First we need to load the package:
+
+```{r loadChip}
+suppressPackageStartupMessages({
+    library(hgu95av2.db)
+})
+```
+
+If we list the contents of this package, we can see that one of the many things
+loaded is an object named after the package "hgu95av2.db":
+
+```{r listContents}
+ls("package:hgu95av2.db")
+```
+
+We can look at this object to learn more about it:
+
+```{r show}
+hgu95av2.db
+```
+
+If we want to know what kinds of data are retriveable via `select`, then we
+should use the `columns` method like this:
+
+```{r columns}
+columns(hgu95av2.db)
+```
+
+If we are further curious to know more about those values for columns, we can
+consult the help pages. Asking about any of these values will pull up a manual
+page describing the different fields and what they mean.
+
+```{r help, eval=FALSE}
+help("SYMBOL")
+```
+
+If we are curious about what kinds of fields we could potentially use as keys to
+query the database, we can use the `keytypes` method. In a perfect world, this
+method will return values very similar to what was returned by `columns`, but in
+reality, some kinds of values make poor keys and so this list is often shorter.
+
+```{r keytypes}
+keytypes(hgu95av2.db)
+```
+
+If we want to extract some sample keys of a particular type, we can use the
+`keys` method.
+
+```{r keys}
+head(keys(hgu95av2.db, keytype="SYMBOL"))
+```
+
+And finally, if we have some keys, we can use `select` to extract them. By
+simply using appropriate argument values with select we can specify what keys we
+want to look up values for (keys), what we want returned back (columns) and the
+type of keys that we are passing in (keytype)
+
+```{r selectChip}
+#1st get some example keys
+k <- head(keys(hgu95av2.db,keytype="PROBEID"))
+# then call select
+select(hgu95av2.db, keys=k, columns=c("SYMBOL","GENENAME"), keytype="PROBEID")
+```
+
+And as you can see, when you call the code above, select will try to return a
+data.frame with all the things you asked for matched up to each other.
+
+Finally if you wanted to extract only one column of data you could instead use
+the mapIds method like this:
+
+```{r mapIdsChip}
+#1st get some example keys
+k <- head(keys(hgu95av2.db,keytype="PROBEID"))
+# then call mapIds
+mapIds(hgu95av2.db, keys=k, column=c("GENENAME"), keytype="PROBEID")
+```
+
+# OrgDb objects and the select method
+
+An organism level package (an 'org' package) uses a central gene identifier
+(e.g. Entrez Gene id) and contains mappings between this identifier and other
+kinds of identifiers (e.g. GenBank or Uniprot accession number, RefSeq id,
+etc.). The name of an org package is always of the form org.\<Ab>\.\<id>\.db
+(e.g.`r Biocpkg("org.Sc.sgd.db")`where \<Ab>\ is a 2-letter abbreviation of the
+organism (e.g.`r Biocpkg("Sc")` for *Saccharomyces cerevisiae*) and \<id>\ is an
+abbreviation (in lower-case) describing the type of central identifier (e.g.
+`r Biocpkg("sgd")` for gene identifiers assigned by the Saccharomyces Genome
+Database, or `r Biocpkg("eg")` for Entrez Gene ids).
+
+Just as the chip packages load a *ChipDb* object, the org packages will load a
+*OrgDb* object. The following exercise should acquaint you with the use of these
+methods in the context of an organism package.
+
+**Exercise 1**
+
+*Display the OrgDb object for the `r Biocpkg("org.Hs.eg.db")`
+package.*
+
+*Use the `columns` method to discover which sorts of annotations can be extracted
+from it. Is this the same as the result from the `keytypes` method?  Use the
+`keytypes` method to find out.*
+
+*Finally, use the `keys` method to extract UNIPROT identifiers and then pass
+those keys in to the `select` method in such a way that you extract the gene
+symbol and KEGG pathway information for each.  Use the help system as needed to
+learn which values to pass in to columns in order to achieve this.*
+
+**Solution:**
+
+```{r}
+library(org.Hs.eg.db)
+columns(org.Hs.eg.db)
+```
+
+```{r}
+help("SYMBOL") ## for explanation of these columns and keytypes values
+keytypes(org.Hs.eg.db)
+```
+
+```{r}
+uniKeys <- head(keys(org.Hs.eg.db, keytype="UNIPROT"))
+cols <- c("SYMBOL", "PATH")
+select(org.Hs.eg.db, keys=uniKeys, columns=cols, keytype="UNIPROT")
+```
+
+So how could you use select to annotate your results? This next exercise should
+hlep you to understand how that should generally work.
+
+**Exercise 2**
+
+*Please run the following code snippet (which will load a fake data result that 
+I have provided for the purposes of illustration):*
+
+```{r selectData}
+load(system.file("extdata", "resultTable.Rda", package="AnnotationDbi"))
+head(resultTable)
+```
+
+*The rownames of this table happen to provide entrez gene identifiers for each
+row (for human). Find the gene symbol and gene name for each of the rows in
+resultTable and then use the merge method to attach those annotations to it.*
+
+
+**Solution:**
+
+```{r}
+annots <- select(org.Hs.eg.db, keys=rownames(resultTable),
+columns=c("SYMBOL","GENENAME"), keytype="ENTREZID")
+
+resultTable <- merge(resultTable, annots, by.x=0, by.y="ENTREZID")
+head(resultTable)
+```
+
+# Using select with GO.db
+
+When you load the ` r Biocpkg("GO.db")` package, a *GODb* object is also loaded.
+This allows you to use the `columns`, `keys`, `keytypes` and `select` methods on
+the contents of the GO ontology. So if for example, you had a few GO IDs and
+wanted to know more about it, you could do it like this:
+
+```{r selectGO}
+library(GO.db)
+GOIDs <- c("GO:0042254","GO:0044183")
+select(GO.db, keys=GOIDs, columns="DEFINITION", keytype="GOID")
+```
+
+# Using select with TxDb packages
+
+A `r Biocpkg("TxDb")` package (a 'TxDb' package) connects a set of genomic
+coordinates to various transcript oriented features. The package can also
+contain Identifiers to features such as genes and transcripts, and the internal
+schema describes the relationships between these different elements. All TxDb
+containing packages follow a specific naming scheme that tells where the data
+came from as well as which build of the genome it comes from.
+
+**Exercise 3**
+
+*Display the TxDb object for the `r Biocpkg("TxDb.Hsapiens.UCSC.hg19.knownGene")`
+package.*
+
+*As before, use the `columns` and `keytypes` methods to discover which sorts of
+annotations can be extracted from it.*
+
+*Use the `keys` method to extract just a few gene identifiers and then pass those
+keys in to the `select` method in such a way that you extract the transcript ids
+and transcript starts for each.*
+
+**Solution:**
+
+```{r selectTxDb}
+library(TxDb.Hsapiens.UCSC.hg19.knownGene)
+txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
+txdb
+columns(txdb)
+keytypes(txdb)
+keys <- head(keys(txdb, keytype="GENEID"))
+cols <- c("TXID", "TXSTART")
+select(txdb, keys=keys, columns=cols, keytype="GENEID")
+```
+
+As is widely known, in addition to providing access via the `select` method,
+*TxDb* objects also provide access via the more familiar `transcripts`, `exons`,
+`cds`, `transcriptsBy`, `exonsBy` and `cdsBy` methods. For those who do not yet
+know about these other methods, more can be learned by seeing the vignette
+called: *Making and Utilizing TxDb Objects* in the 
+`r Biocpkg("GenomicFeatures")` package.
+
+# Using select with EnsDb packages
+
+Similar to the *TxDb* objects/packages discussed in the previous section,
+*EnsDb* objects/packages provide genomic coordinates of gene models along with
+additional annotations (e.g. gene names, biotypes etc) but are tailored to
+annotations provided by Ensembl. The central methods `columns`, `keys`,
+`keytypes` and `select` are all implemented for *EnsD* objects. In addition,
+these methods allow also the use of the *EnsDb* specific filtering framework to
+retrieve only selected information from the database (see vignette of the 
+`r Biocpkg("ensembldb")` package for more information).
+
+In the example below we first evaluate which columns, keys and keytypes are
+available for *EnsDb* objects and fetch then the transcript ids, genomic start
+coordinate and transcript biotype for some genes.
+
+```{r selectEnsDb}
+library(EnsDb.Hsapiens.v75)
+edb <- EnsDb.Hsapiens.v75
+edb
+
+## List all columns
+columns(edb)
+
+## List all keytypes
+keytypes(edb)
+
+## Get the first
+keys <- head(keys(edb, keytype="GENEID"))
+
+## Get the data
+select(edb, keys=keys, columns=c("TXID", "TXSEQSTART", "TXBIOTYPE"), 
+       keytype="GENEID")
+```
+
+We can modify the queries above to retrieve only genes encoded on chromosome Y.
+To this end we use filter objects available for *EnsDb* objects and its methods.
+
+```{r selectEnsDb.Y}
+## Retrieve all gene IDs of all lincRNAs encoded on chromosome Y
+linkY <- keys(edb,
+              filter=list(GeneBiotypeFilter("lincRNA"), SeqNameFilter("Y")))
+length(linkY)
+
+## We get now all transcripts for these genes.
+txs <- select(edb, keys=linkY, columns=c("TXID", "TXSEQSTART", "TXBIOTYPE"),
+              keytype="GENEID")
+nrow(txs)
+
+## Alternatively, we could specify/pass the filters with the keys argument.
+txs <- select(edb, keys=list(GeneBiotypeFilter("lincRNA"), SeqNameFilter("Y")),
+              columns=c("TXID", "TXSEQSTART", "TXBIOTYPE"))
+nrow(txs)
+```
+
+The version number of R and packages loaded for generating the vignette were:
+
+```{r SessionInfo, echo=FALSE}
+sessionInfo()
+```