# BiocSet: Representing Element Sets in the Tidyverse

Kayla Morrell1 and Martin Morgan1

1Roswell Park Comprehensive Cancer Center, Buffalo, NY

#### 2020-04-27

#### Package

BiocSet 1.1.4

# 1 Introduction

`BiocSet` is a package that represents element sets in a tibble format with the `BiocSet` class. Element sets are read in and converted into a tibble format. From here, typical `dplyr` operations can be performed on the element set. `BiocSet` also provides functionality for mapping different ID types and providing reference urls for elements and sets.

# 2 Installation

Install the most recent version from Bioconductor:

    if(!requireNamespace("BiocManager", quietly = TRUE))
        install.packages("BiocManager")
    BiocManager::install("BiocSet")

The development version is also available for install from GitHub:

    BiocManager::install("Kayla-Morrell/BiocSet")

Then load `BiocSet`:

    library(BiocSet)

# 3 BiocSet

## 3.1 Input and Output

`BiocSet` can create a `BiocSet` object using two different input methods. The first is to input named character vectors of element sets. `BiocSet` returns three tibbles, `es_element` which contains the elements, `es_set` which contains the sets and `es_elementset` which contains elements and sets together.

    tbl <- BiocSet(set1 = letters, set2 = LETTERS)
    tbl
    #> class: BiocSet
    #>
    #> es_element():
    #> # A tibble: 52 x 1
    #>   element
    #>   <chr>
    #> 1 a
    #> 2 b
    #> 3 c
    #> # … with 49 more rows
    #>
    #> es_set():
    #> # A tibble: 2 x 1
    #>   set
    #>   <chr>
    #> 1 set1
    #> 2 set2
    #>
    #> es_elementset() <active>:
    #> # A tibble: 52 x 2
    #>   element set
    #>   <chr>   <chr>
    #> 1 a       set1
    #> 2 b       set1
    #> 3 c       set1
    #> # … with 49 more rows

The second method of creating a `BiocSet` object would be to read in a `.gmt` file. Using `import()`, a path to a downloaded `.gmt` file is read in and a `BiocSet` object is returned. The example below uses a hallmark element set downloaded from GSEA, which is also included with this package. This `BiocSet` includes a `source` column within the `es_elementset` tibble for reference as to where the element set came from.

    gmtFile <- system.file(package = "BiocSet",
                            "extdata",
                            "hallmark.gene.symbol.gmt")
    tbl2 <- import(gmtFile)
    tbl2
    #> class: BiocSet
    #>
    #> es_element():
    #> # A tibble: 4,386 x 1
    #>   element
    #>   <chr>
    #> 1 JUNB
    #> 2 CXCL2
    #> 3 ATF3
    #> # … with 4,383 more rows
    #>
    #> es_set():
    #> # A tibble: 50 x 2
    #>   set                     source
    #>   <chr>                   <chr>
    #> 1 HALLMARK_TNFA_SIGNALIN… http://www.broadinstitute.org/gsea/msigdb/cards/HALLM…
    #> 2 HALLMARK_HYPOXIA        http://www.broadinstitute.org/gsea/msigdb/cards/HALLM…
    #> 3 HALLMARK_CHOLESTEROL_H… http://www.broadinstitute.org/gsea/msigdb/cards/HALLM…
    #> # … with 47 more rows
    #>
    #> es_elementset() <active>:
    #> # A tibble: 7,324 x 2
    #>   element set
    #>   <chr>   <chr>
    #> 1 JUNB    HALLMARK_TNFA_SIGNALING_VIA_NFKB
    #> 2 CXCL2   HALLMARK_TNFA_SIGNALING_VIA_NFKB
    #> 3 ATF3    HALLMARK_TNFA_SIGNALING_VIA_NFKB
    #> # … with 7,321 more rows

`export()` allows for a `BiocSet` object to be exported into a temporary file with the extention `.gmt`.

    fl <- tempfile(fileext = ".gmt")
    gmt <- export(tbl2, fl)
    gmt
    #> GMTFile object
    #> resource: /tmp/RtmpPB7dk8/file8d296abe89d6.gmt

## 3.2 Implemented functions

A feature available to `BiocSet` is the ability to activate different tibbles to perform certain functions on. When a `BiocSet` is created, the tibble `es_elementset` is automatically activated and all functions will be performed on this tibble. `BiocSet` adopts the use of many common `dplyr` functions such as `filter()`, `select()`, `mutate()`, `summarise()`, and `arrange()`. With each of the functions the user is able to pick a different tibble to activate and work on by using ‘verb_tibble’. After the function is executed than the ‘active’ tibble is returned back to the tibble that was active before the function call. Some examples are shown below of how these functions work.

    tbl <- BiocSet(set1 = letters, set2 = LETTERS)
    tbl
    #> class: BiocSet
    #>
    #> es_element():
    #> # A tibble: 52 x 1
    #>   element
    #>   <chr>
    #> 1 a
    #> 2 b
    #> 3 c
    #> # … with 49 more rows
    #>
    #> es_set():
    #> # A tibble: 2 x 1
    #>   set
    #>   <chr>
    #> 1 set1
    #> 2 set2
    #>
    #> es_elementset() <active>:
    #> # A tibble: 52 x 2
    #>   element set
    #>   <chr>   <chr>
    #> 1 a       set1
    #> 2 b       set1
    #> 3 c       set1
    #> # … with 49 more rows
    tbl %>% filter_element(element == "a" | element == "A")
    #> class: BiocSet
    #>
    #> es_element():
    #> # A tibble: 2 x 1
    #>   element
    #>   <chr>
    #> 1 a
    #> 2 A
    #>
    #> es_set():
    #> # A tibble: 2 x 1
    #>   set
    #>   <chr>
    #> 1 set1
    #> 2 set2
    #>
    #> es_elementset() <active>:
    #> # A tibble: 2 x 2
    #>   element set
    #>   <chr>   <chr>
    #> 1 a       set1
    #> 2 A       set2
    tbl %>% mutate_set(pval = rnorm(1:2))
    #> class: BiocSet
    #>
    #> es_element():
    #> # A tibble: 52 x 1
    #>   element
    #>   <chr>
    #> 1 a
    #> 2 b
    #> 3 c
    #> # … with 49 more rows
    #>
    #> es_set():
    #> # A tibble: 2 x 2
    #>   set    pval
    #>   <chr> <dbl>
    #> 1 set1  -1.90
    #> 2 set2   1.13
    #>
    #> es_elementset() <active>:
    #> # A tibble: 52 x 2
    #>   element set
    #>   <chr>   <chr>
    #> 1 a       set1
    #> 2 b       set1
    #> 3 c       set1
    #> # … with 49 more rows
    tbl %>% arrange_elementset(desc(element))
    #> class: BiocSet
    #>
    #> es_element():
    #> # A tibble: 52 x 1
    #>   element
    #>   <chr>
    #> 1 a
    #> 2 b
    #> 3 c
    #> # … with 49 more rows
    #>
    #> es_set():
    #> # A tibble: 2 x 1
    #>   set
    #>   <chr>
    #> 1 set1
    #> 2 set2
    #>
    #> es_elementset() <active>:
    #> # A tibble: 52 x 2
    #>   element set
    #>   <chr>   <chr>
    #> 1 z       set1
    #> 2 y       set1
    #> 3 x       set1
    #> # … with 49 more rows

## 3.3 Set operations

`BiocSet` also allows for common set operations to be performed on the `BiocSet` object. `union()` and `intersection()` are the two set operations available in `BiocSet`. We demonstate how a user can find the union between two `BiocSet` objects or within a single `BiocSet` object. Intersection is used in the same way.

    # union of two BiocSet objects
    es1 <- BiocSet(set1 = letters[c(1:3)], set2 = LETTERS[c(1:3)])
    es2 <- BiocSet(set1 = letters[c(2:4)], set2 = LETTERS[c(2:4)])
    union(es1, es2)
    #> class: BiocSet
    #>
    #> es_element():
    #> # A tibble: 8 x 1
    #>   element
    #>   <chr>
    #> 1 a
    #> 2 b
    #> 3 c
    #> # … with 5 more rows
    #>
    #> es_set():
    #> # A tibble: 2 x 1
    #>   set
    #>   <chr>
    #> 1 set1
    #> 2 set2
    #>
    #> es_elementset() <active>:
    #> # A tibble: 8 x 2
    #>   element set
    #>   <chr>   <chr>
    #> 1 a       set1
    #> 2 b       set1
    #> 3 c       set1
    #> # … with 5 more rows

    # union within a single BiocSet object
    es3 <- BiocSet(set1 = letters[c(1:10)], set2 = letters[c(4:20)])
    union_single(es3)
    #> class: BiocSet
    #>
    #> es_element():
    #> # A tibble: 20 x 1
    #>   element
    #>   <chr>
    #> 1 a
    #> 2 b
    #> 3 c
    #> # … with 17 more rows
    #>
    #> es_set():
    #> # A tibble: 1 x 1
    #>   set
    #>   <chr>
    #> 1 union
    #>
    #> es_elementset() <active>:
    #> # A tibble: 20 x 2
    #>   element set
    #>   <chr>   <chr>
    #> 1 a       union
    #> 2 b       union
    #> 3 c       union
    #> # … with 17 more rows

# 4 Case study I

Next, we demonstrate the a couple uses of `BiocSet` with an experiment dataset `airway` from the package `airway`. This data is from an RNA-Seq experiment on airway smooth muscle (ASM) cell lines.

The first step is to load the library and the necessary data.

    library(airway)
    data("airway")
    se <- airway

The function `go_sets()` discovers the keys from the org object and uses `AnnotationDbi::select` to create a mapping to GO ids. `go_sets()` also allows the user to indicate which evidence type or ontology type they would like when selecting the GO ids. The default is using all evidence types and all ontology types. We represent these identifieres as a `BiocSet` object. Using the `go_sets` function we are able to map the Ensembl ids and GO ids from the genome wide annotation for Human data in the `org.Hs.eg.db` package. The Ensembl ids are treated as elements while the GO ids are treated as sets.

    library(org.Hs.eg.db)
    go <- go_sets(org.Hs.eg.db, "ENSEMBL")
    go
    #> class: BiocSet
    #>
    #> es_element():
    #> # A tibble: 22,512 x 1
    #>   element
    #>   <chr>
    #> 1 ENSG00000151729
    #> 2 ENSG00000025708
    #> 3 ENSG00000068305
    #> # … with 22,509 more rows
    #>
    #> es_set():
    #> # A tibble: 18,175 x 1
    #>   set
    #>   <chr>
    #> 1 GO:0000002
    #> 2 GO:0000003
    #> 3 GO:0000010
    #> # … with 18,172 more rows
    #>
    #> es_elementset() <active>:
    #> # A tibble: 318,556 x 2
    #>   element         set
    #>   <chr>           <chr>
    #> 1 ENSG00000151729 GO:0000002
    #> 2 ENSG00000025708 GO:0000002
    #> 3 ENSG00000068305 GO:0000002
    #> # … with 318,553 more rows

    # an example of subsetting by evidence type
    go_sets(org.Hs.eg.db, "ENSEMBL", evidence = c("IPI", "TAS"))
    #> class: BiocSet
    #>
    #> es_element():
    #> # A tibble: 16,011 x 1
    #>   element
    #>   <chr>
    #> 1 ENSG00000151729
    #> 2 ENSG00000168685
    #> 3 ENSG00000114030
    #> # … with 16,008 more rows
    #>
    #> es_set():
    #> # A tibble: 4,318 x 2
    #>   set        evidence
    #>   <chr>      <named list>
    #> 1 GO:0000002 <chr [1]>
    #> 2 GO:0000018 <chr [1]>
    #> 3 GO:0000019 <chr [1]>
    #> # … with 4,315 more rows
    #>
    #> es_elementset() <active>:
    #> # A tibble: 68,112 x 2
    #>   element         set
    #>   <chr>           <chr>
    #> 1 ENSG00000151729 GO:0000002
    #> 2 ENSG00000168685 GO:0000018
    #> 3 ENSG00000114030 GO:0000018
    #> # … with 68,109 more rows

Some users may not be interested in reporting the non-descriptive elements. We demonstrate subsetting the `airway` data to include non-zero assays and then filter out the non-descriptive elements.

    se1 = se[rowSums(assay(se)) != 0,]
    go %>% filter_element(element %in% rownames(se1))
    #> class: BiocSet
    #>
    #> es_element():
    #> # A tibble: 16,479 x 1
    #>   element
    #>   <chr>
    #> 1 ENSG00000151729
    #> 2 ENSG00000025708
    #> 3 ENSG00000068305
    #> # … with 16,476 more rows
    #>
    #> es_set():
    #> # A tibble: 18,175 x 1
    #>   set
    #>   <chr>
    #> 1 GO:0000002
    #> 2 GO:0000003
    #> 3 GO:0000010
    #> # … with 18,172 more rows
    #>
    #> es_elementset() <active>:
    #> # A tibble: 250,234 x 2
    #>   element         set
    #>   <chr>           <chr>
    #> 1 ENSG00000151729 GO:0000002
    #> 2 ENSG00000025708 GO:0000002
    #> 3 ENSG00000068305 GO:0000002
    #> # … with 250,231 more rows

It may also be of interest to users to know how many elements are in each set. Using the `count` function we are able to calculate the elements per set.

    go %>% group_by(set) %>% dplyr::count()
    #> # A tibble: 18,175 x 2
    #> # Groups:   set [18,175]
    #>    set            n
    #>    <chr>      <int>
    #>  1 GO:0000002    13
    #>  2 GO:0000003     4
    #>  3 GO:0000010     2
    #>  4 GO:0000012     7
    #>  5 GO:0000014     9
    #>  6 GO:0000015     4
    #>  7 GO:0000016     1
    #>  8 GO:0000018     6
    #>  9 GO:0000019     3
    #> 10 GO:0000022     2
    #> # … with 18,165 more rows

It may also be helpful to remove sets that are empty. Since we have shown how to calculate the number of elements per set, we know that this data set does not contain any empty sets. We decide to demonstrate regardless for those users that may need this functionality.

    drop <- es_activate(go, elementset) %>% group_by(set) %>%
        dplyr::count() %>% filter(n == 0) %>% pull(set)
    go %>% filter_set(!(set %in% drop))
    #> class: BiocSet
    #>
    #> es_element():
    #> # A tibble: 22,512 x 1
    #>   element
    #>   <chr>
    #> 1 ENSG00000151729
    #> 2 ENSG00000025708
    #> 3 ENSG00000068305
    #> # … with 22,509 more rows
    #>
    #> es_set():
    #> # A tibble: 18,175 x 1
    #>   set
    #>   <chr>
    #> 1 GO:0000002
    #> 2 GO:0000003
    #> 3 GO:0000010
    #> # … with 18,172 more rows
    #>
    #> es_elementset() <active>:
    #> # A tibble: 318,556 x 2
    #>   element         set
    #>   <chr>           <chr>
    #> 1 ENSG00000151729 GO:0000002
    #> 2 ENSG00000025708 GO:0000002
    #> 3 ENSG00000068305 GO:0000002
    #> # … with 318,553 more rows

To simplify mapping we created a couple `map` functions. `map_unique()` is used when there is known 1:1 mapping, This takes four arguements, a `BiocSet` object, an `AnnotationDbi` object, the id to map from, and the id to map to. `map_multiple()` needs a fifth argument to indicate how the function should treat an element when there is multiple mapping. Both functions utilize `mapIds` from `AnnotationDbi` and return a `BiocSet` object. In the example below we show how to use `map_unique` to map `go`’s ids from Ensembl to gene symbols.

    go %>% map_unique(org.Hs.eg.db, "ENSEMBL", "SYMBOL")
    #> 'select()' returned 1:many mapping between keys and columns
    #> Joining, by = "element"
    #> `mutate_if()` ignored the following grouping variables:
    #> Column `element`
    #> Joining, by = "element"
    #> Joining, by = "element"
    #> Joining, by = "set"
    #> Joining, by = c("element", "set")
    #> class: BiocSet
    #>
    #> es_element():
    #> # A tibble: 19,821 x 1
    #>   element
    #>   <chr>
    #> 1 AKT3
    #> 2 LONP1
    #> 3 MEF2A
    #> # … with 19,818 more rows
    #>
    #> es_set():
    #> # A tibble: 18,175 x 1
    #>   set
    #>   <chr>
    #> 1 GO:0000002
    #> 2 GO:0000003
    #> 3 GO:0000010
    #> # … with 18,172 more rows
    #>
    #> es_elementset() <active>:
    #> # A tibble: 281,598 x 2
    #>   element set
    #>   <chr>   <chr>
    #> 1 AKT3    GO:0000002
    #> 2 LONP1   GO:0000002
    #> 3 MEF2A   GO:0000002
    #> # … with 281,595 more rows

Another functionality of `BiocSet` is the ability to add information to the tibbles. Using the `GO.db` library we are able to map definitions to the GO ids. From there we can add the mapping to the tibble using `map_add()` and the mutate function.

    library(GO.db)
    map <- map_add_set(go, GO.db, "GOID", "DEFINITION")
    go %>% mutate_set(definition = map)
    #> class: BiocSet
    #>
    #> es_element():
    #> # A tibble: 22,512 x 1
    #>   element
    #>   <chr>
    #> 1 ENSG00000151729
    #> 2 ENSG00000025708
    #> 3 ENSG00000068305
    #> # … with 22,509 more rows
    #>
    #> es_set():
    #> # A tibble: 18,175 x 2
    #>   set        definition
    #>   <chr>      <chr>
    #> 1 GO:0000002 The maintenance of the structure and integrity of the mitochondria…
    #> 2 GO:0000003 The production of new individuals that contain some portion of gen…
    #> 3 GO:0000010 Catalysis of the reaction: all-trans-hexaprenyl diphosphate + isop…
    #> # … with 18,172 more rows
    #>
    #> es_elementset() <active>:
    #> # A tibble: 318,556 x 2
    #>   element         set
    #>   <chr>           <chr>
    #> 1 ENSG00000151729 GO:0000002
    #> 2 ENSG00000025708 GO:0000002
    #> 3 ENSG00000068305 GO:0000002
    #> # … with 318,553 more rows

The library `KEGGREST` is a client interface to the KEGG REST server. KEGG contains pathway maps that represent interaction, reaction and relation networks for various biological processes and diseases. `BiocSet` has a function that utilizes `KEGGREST` to develop a `BiocSet` object that contains the elements for every pathway map in KEGG.

Due to limiations of the KEGGREST package, `kegg_sets` can take some time to run depending on the amount of pathways for the species of interest. Therefore we demonstrate using `BiocFileCache` to make the data available to the user.

    library(BiocFileCache)
    rname <- "kegg_hsa"
    exists <- NROW(bfcquery(query=rname, field="rname")) != 0L
    if (!exists)
    {
        kegg <- kegg_sets("hsa")
        fl <- bfcnew(rname = rname, ext = ".gmt")
        export(kegg_sets("hsa"), fl)
    }
    kegg <- import(bfcrpath(rname=rname))

Within the `kegg_sets()` function we remove pathways that do not contain any elements. We then mutate the element tibble using the `map_add` function to contain both Ensembl and Entrez ids.

    map <- map_add_element(kegg, org.Hs.eg.db, "ENTREZID", "ENSEMBL")
    #> 'select()' returned 1:many mapping between keys and columns
    kegg <- kegg %>% mutate_element(ensembl = map)

Since we are working with ASM data we thought we would subset the `airway` data to contain only the elements in the asthma pathway. This filter is performed on the KEGG id, which for asthma is “hsa05310”.

    asthma <- kegg %>% filter_set(set == "hsa05310")

    se <- se[rownames(se) %in% es_element(asthma)$ensembl,]

    se
    #> class: RangedSummarizedExperiment
    #> dim: 7772 8
    #> metadata(1): ''
    #> assays(1): counts
    #> rownames(7772): ENSG00000000419 ENSG00000000938 ... ENSG00000273079
    #>   ENSG00000273085
    #> rowData names(0):
    #> colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
    #> colData names(9): SampleName cell ... Sample BioSample

The filtering can also be done for multiple pathways.

    pathways <- c("hsa05310", "hsa04110", "hsa05224", "hsa04970")
    multipaths <- kegg %>% filter_set(set %in% pathways)

    multipaths
    #> class: BiocSet
    #>
    #> es_element():
    #> # A tibble: 8,003 x 2
    #>   element ensembl
    #>   <chr>   <chr>
    #> 1 3101    ENSG00000160883
    #> 2 3098    ENSG00000156515
    #> 3 3099    ENSG00000159399
    #> # … with 8,000 more rows
    #>
    #> es_set():
    #> # A tibble: 4 x 2
    #>   set      source
    #>   <chr>    <chr>
    #> 1 hsa04110 <NA>
    #> 2 hsa04970 <NA>
    #> 3 hsa05224 <NA>
    #> # … with 1 more row
    #>
    #> es_elementset() <active>:
    #> # A tibble: 392 x 2
    #>   element set
    #>   <chr>   <chr>
    #> 1 595     hsa04110
    #> 2 894     hsa04110
    #> 3 896     hsa04110
    #> # … with 389 more rows

# 5 Case study II

This example will start out the same way that Case Study I started, by loading in the `airway` dataset, but we will also do some reformating of the data. The end goal is to be able to perform a Gene Set Enrichment Analysis on the data and return a BiocSet object of the gene sets.

    data("airway")
    airway$dex <- relevel(airway$dex, "untrt")

Similar to other analyses we perform a differential expression analysis on the `airway` data using the library `DESeq2` and then store the results into a tibble.

    library(DESeq2)
    library(tibble)
    des <- DESeqDataSet(airway, design = ~ cell + dex)
    des <- DESeq(des)
    #> estimating size factors
    #> estimating dispersions
    #> gene-wise dispersion estimates
    #> mean-dispersion relationship
    #> final dispersion estimates
    #> fitting model and testing
    res <- results(des)

    tbl <- res %>%
        as.data.frame() %>%
        as_tibble(rownames = "ENSEMBL")

Since we want to use `limma::goana()` to perform the GSEA we will need to have ENTREZ identifiers in the data, as well as filter out some `NA` information. Later on this will be considered our ‘element’ tibble.

    tbl <- tbl %>%
        mutate(
            ENTREZID = mapIds(
                org.Hs.eg.db, ENSEMBL, "ENTREZID", "ENSEMBL"
            ) %>% unname()
        )
    #> 'select()' returned 1:many mapping between keys and columns

    tbl <- tbl %>% filter(!is.na(padj), !is.na(ENTREZID))
    tbl
    #> # A tibble: 14,589 x 8
    #>    ENSEMBL      baseMean log2FoldChange  lfcSE   stat   pvalue     padj ENTREZID
    #>    <chr>           <dbl>          <dbl>  <dbl>  <dbl>    <dbl>    <dbl> <chr>
    #>  1 ENSG0000000…    709.         -0.381  0.101  -3.79   1.52e-4  1.28e-3 7105
    #>  2 ENSG0000000…    520.          0.207  0.112   1.84   6.53e-2  1.97e-1 8813
    #>  3 ENSG0000000…    237.          0.0379 0.143   0.264  7.92e-1  9.11e-1 57147
    #>  4 ENSG0000000…     57.9        -0.0882 0.287  -0.307  7.59e-1  8.95e-1 55732
    #>  5 ENSG0000000…   5817.          0.426  0.0883  4.83   1.38e-6  1.82e-5 3075
    #>  6 ENSG0000000…   1282.         -0.241  0.0887 -2.72   6.58e-3  3.28e-2 2519
    #>  7 ENSG0000000…    610.         -0.0476 0.167  -0.286  7.75e-1  9.03e-1 2729
    #>  8 ENSG0000000…    369.         -0.500  0.121  -4.14   3.48e-5  3.42e-4 4800
    #>  9 ENSG0000000…    183.         -0.124  0.180  -0.689  4.91e-1  7.24e-1 90529
    #> 10 ENSG0000000…   2814.         -0.0411 0.103  -0.400  6.89e-1  8.57e-1 57185
    #> # … with 14,579 more rows

Now that the data is ready for GSEA we can go ahead and use `goana()` and make the results into a tibble. This tibble will be considered our ‘set’ tibble.

    library(limma)
    #>
    #> Attaching package: 'limma'
    #> The following object is masked from 'package:DESeq2':
    #>
    #>     plotMA
    #> The following object is masked from 'package:BiocGenerics':
    #>
    #>     plotMA
    go_ids <- goana(tbl$ENTREZID[tbl$padj < 0.05], tbl$ENTREZID, "Hs") %>%
        as.data.frame() %>%
        as_tibble(rownames = "GOALL")
    go_ids
    #> # A tibble: 21,307 x 6
    #>    GOALL     Term                                     Ont       N    DE     P.DE
    #>    <chr>     <chr>                                    <chr> <dbl> <dbl>    <dbl>
    #>  1 GO:00017… cell activation                          BP      944   334 8.24e-12
    #>  2 GO:00022… immune effector process                  BP      819   256 1.64e- 4
    #>  3 GO:00022… cell activation involved in immune resp… BP      500   148 2.73e- 2
    #>  4 GO:00022… myeloid leukocyte activation             BP      465   149 1.22e- 3
    #>  5 GO:00022… myeloid cell activation involved in imm… BP      399   118 4.55e- 2
    #>  6 GO:00022… neutrophil activation involved in immun… BP      361   108 4.03e- 2
    #>  7 GO:00023… leukocyte activation involved in immune… BP      496   148 2.10e- 2
    #>  8 GO:00023… immune system process                    BP     1993   662 7.62e-16
    #>  9 GO:00024… leukocyte mediated immunity              BP      544   167 4.79e- 3
    #> 10 GO:00024… myeloid leukocyte mediated immunity      BP      409   124 2.01e- 2
    #> # … with 21,297 more rows

The last thing we need to do is create a tibble that we will consider our ‘elementset’ tibble. This tibble will be a mapping of all the elements and sets.

    foo <- AnnotationDbi::select(
        org.Hs.eg.db,
        tbl$ENTREZID,
        "GOALL",
        "ENTREZID") %>% as_tibble()
    #> 'select()' returned many:many mapping between keys and columns
    foo <- foo %>% dplyr::select(-EVIDENCEALL) %>% distinct()
    foo <- foo %>% filter(ONTOLOGYALL == "BP") %>% dplyr::select(-ONTOLOGYALL)
    foo
    #> # A tibble: 1,159,245 x 2
    #>    ENTREZID GOALL
    #>    <chr>    <chr>
    #>  1 7105     GO:0001816
    #>  2 7105     GO:0001817
    #>  3 7105     GO:0001819
    #>  4 7105     GO:0002218
    #>  5 7105     GO:0002221
    #>  6 7105     GO:0002252
    #>  7 7105     GO:0002253
    #>  8 7105     GO:0002376
    #>  9 7105     GO:0002682
    #> 10 7105     GO:0002683
    #> # … with 1,159,235 more rows

The function `BiocSet_from_elementset()` allows for users to create a BiocSet object from tibbles. This function is helpful when there is metadata contained in the tibble that should be in the BiocSet object. For this function to work properly, the columns that are being joined on need to be named correctly. For instance, in order to use this function on the tibbles we created we need to change the column in the ‘element’ tibble to ‘element’, the column in the ‘set’ tibble to ‘set’ and the same will be for the ‘elementset’ tibble. We demonstrate this below and then create the BiocSet object with the simple function.

    foo <- foo %>% dplyr::rename(element = ENTREZID, set = GOALL)
    tbl <- tbl %>% dplyr::rename(element = ENTREZID)
    go_ids <- go_ids %>% dplyr::rename(set = GOALL)
    es <- BiocSet_from_elementset(foo, tbl, go_ids)
    #> Joining, by = "element"
    #> Joining, by = "set"
    #> Joining, by = c("element", "set")
    #> more elements in 'element' than in 'elementset'
    #> more elements in 'set' than in 'elementset'
    es
    #> class: BiocSet
    #>
    #> es_element():
    #> # A tibble: 12,270 x 8
    #>   element ENSEMBL      baseMean log2FoldChange  lfcSE   stat   pvalue       padj
    #>   <chr>   <chr>           <dbl>          <dbl>  <dbl>  <dbl>    <dbl>      <dbl>
    #> 1 3980    ENSG0000000…     206.        -0.393  0.172  -2.29  2.23e- 2    8.66e-2
    #> 2 1890    ENSG0000002…     145.         1.23   0.199   6.18  6.58e-10    1.49e-8
    #> 3 50484   ENSG0000004…    1407.        -0.0701 0.0902 -0.778 4.37e- 1    6.80e-1
    #> # … with 12,267 more rows
    #>
    #> es_set():
    #> # A tibble: 15,079 x 6
    #>   set        Term                             Ont       N    DE       P.DE
    #>   <chr>      <chr>                            <chr> <dbl> <dbl>      <dbl>
    #> 1 GO:0000002 mitochondrial genome maintenance BP       29     6 0.796
    #> 2 GO:0000003 reproduction                     BP      948   308 0.00000102
    #> 3 GO:0000012 single strand break repair       BP        8     1 0.908
    #> # … with 15,076 more rows
    #>
    #> es_elementset() <active>:
    #> # A tibble: 1,159,245 x 2
    #>   element set
    #>   <chr>   <chr>
    #> 1 3980    GO:0000002
    #> 2 1890    GO:0000002
    #> 3 50484   GO:0000002
    #> # … with 1,159,242 more rows

For those users that may need to put this metadata filled BiocSet object back into an object similar to GRanges or SummarizedExperiment, we have created functions that allow for the BiocSet object to be created into a tibble or data.frame.

    tibble_from_element(es)
    #> Joining, by = "set"
    #> Joining, by = "element"
    #> # A tibble: 12,262 x 14
    #>    element set   Term  Ont   N     DE    P.DE  ENSEMBL baseMean log2FoldChange
    #>    <chr>   <lis> <lis> <lis> <lis> <lis> <lis> <list>  <list>   <list>
    #>  1 1       <chr… <chr… <chr… <dbl… <dbl… <dbl… <chr [… <dbl [2… <dbl [29]>
    #>  2 100     <chr… <chr… <chr… <dbl… <dbl… <dbl… <chr [… <dbl [4… <dbl [482]>
    #>  3 1000    <chr… <chr… <chr… <dbl… <dbl… <dbl… <chr [… <dbl [2… <dbl [252]>
    #>  4 10000   <chr… <chr… <chr… <dbl… <dbl… <dbl… <chr [… <dbl [1… <dbl [159]>
    #>  5 10001   <chr… <chr… <chr… <dbl… <dbl… <dbl… <chr [… <dbl [7… <dbl [79]>
    #>  6 10003   <chr… <chr… <chr… <dbl… <dbl… <dbl… <chr [… <dbl [3… <dbl [31]>
    #>  7 10004   <chr… <chr… <chr… <dbl… <dbl… <dbl… <chr [… <dbl [1… <dbl [19]>
    #>  8 100048… <chr… <chr… <chr… <dbl… <dbl… <dbl… <chr [… <dbl [9… <dbl [93]>
    #>  9 10005   <chr… <chr… <chr… <dbl… <dbl… <dbl… <chr [… <dbl [1… <dbl [153]>
    #> 10 10006   <chr… <chr… <chr… <dbl… <dbl… <dbl… <chr [… <dbl [1… <dbl [169]>
    #> # … with 12,252 more rows, and 4 more variables: lfcSE <list>, stat <list>,
    #> #   pvalue <list>, padj <list>

    head(data.frame_from_elementset(es))
    #> Joining, by = "set"
    #> Joining, by = "element"
    #>   element        set                             Term Ont  N DE      P.DE
    #> 1    3980 GO:0000002 mitochondrial genome maintenance  BP 29  6 0.7959496
    #> 2    1890 GO:0000002 mitochondrial genome maintenance  BP 29  6 0.7959496
    #> 3   50484 GO:0000002 mitochondrial genome maintenance  BP 29  6 0.7959496
    #> 4    4205 GO:0000002 mitochondrial genome maintenance  BP 29  6 0.7959496
    #> 5    9093 GO:0000002 mitochondrial genome maintenance  BP 29  6 0.7959496
    #> 6    6742 GO:0000002 mitochondrial genome maintenance  BP 29  6 0.7959496
    #>           ENSEMBL  baseMean log2FoldChange      lfcSE       stat       pvalue
    #> 1 ENSG00000005156  205.6627    -0.39272067 0.17180717 -2.2858223 2.226466e-02
    #> 2 ENSG00000025708  144.6965     1.23104228 0.19933257  6.1758212 6.582046e-10
    #> 3 ENSG00000048392 1406.6344    -0.07014829 0.09020226 -0.7776778 4.367590e-01
    #> 4 ENSG00000068305 1565.5675     0.52905471 0.09186326  5.7591547 8.453618e-09
    #> 5 ENSG00000103423  670.6312    -0.04006479 0.09900566 -0.4046718 6.857188e-01
    #> 6 ENSG00000106028  680.9599     0.02649354 0.10141139  0.2612482 7.939011e-01
    #>           padj
    #> 1 8.661787e-02
    #> 2 1.494473e-08
    #> 3 6.803673e-01
    #> 4 1.636969e-07
    #> 5 8.544470e-01
    #> 6 9.121070e-01

# 6 Information look up

A final feature to `BiocSet` is the ability to add reference information about all of the elements/sets. A user could utilize the function `url_ref()` to add information to the `BiocSet` object. If a user has a question about a specific id then they can follow the reference url to get more informtion. Below is an example of using `url_ref()` to add reference urls to the `go` data set we worked with above.

    url_ref(go)
    #> class: BiocSet
    #>
    #> es_element():
    #> # A tibble: 22,512 x 2
    #>   element        url
    #>   <chr>          <chr>
    #> 1 ENSG000001517… https://www.ensembl.org/Homo_sapiens/Gene/Summary?db=core;g=EN…
    #> 2 ENSG000000257… https://www.ensembl.org/Homo_sapiens/Gene/Summary?db=core;g=EN…
    #> 3 ENSG000000683… https://www.ensembl.org/Homo_sapiens/Gene/Summary?db=core;g=EN…
    #> # … with 22,509 more rows
    #>
    #> es_set():
    #> # A tibble: 18,175 x 2
    #>   set        url
    #>   <chr>      <chr>
    #> 1 GO:0000002 http://amigo.geneontology.org/amigo/medial_search?q=GO:0000002
    #> 2 GO:0000003 http://amigo.geneontology.org/amigo/medial_search?q=GO:0000003
    #> 3 GO:0000010 http://amigo.geneontology.org/amigo/medial_search?q=GO:0000010
    #> # … with 18,172 more rows
    #>
    #> es_elementset() <active>:
    #> # A tibble: 318,556 x 2
    #>   element         set
    #>   <chr>           <chr>
    #> 1 ENSG00000151729 GO:0000002
    #> 2 ENSG00000025708 GO:0000002
    #> 3 ENSG00000068305 GO:0000002
    #> # … with 318,553 more rows

# 7 Session info

    sessionInfo()
    #> R version 4.0.0 (2020-04-24)
    #> Platform: x86_64-apple-darwin17.0 (64-bit)
    #> Running under: macOS Mojave 10.14.6
    #>
    #> Matrix products: default
    #> BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
    #> LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
    #>
    #> locale:
    #> [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
    #>
    #> attached base packages:
    #> [1] parallel  stats4    stats     graphics  grDevices utils     datasets
    #> [8] methods   base
    #>
    #> other attached packages:
    #>  [1] limma_3.43.11               tibble_3.0.1
    #>  [3] DESeq2_1.27.32              BiocFileCache_1.11.6
    #>  [5] dbplyr_1.4.3                GO.db_3.10.0
    #>  [7] org.Hs.eg.db_3.10.0         AnnotationDbi_1.49.1
    #>  [9] airway_1.7.0                SummarizedExperiment_1.17.5
    #> [11] DelayedArray_0.13.12        matrixStats_0.56.0
    #> [13] Biobase_2.47.3              GenomicRanges_1.39.3
    #> [15] GenomeInfoDb_1.23.17        IRanges_2.21.8
    #> [17] S4Vectors_0.25.15           BiocGenerics_0.33.3
    #> [19] BiocSet_1.1.4               dplyr_0.8.5
    #> [21] BiocStyle_2.15.8
    #>
    #> loaded via a namespace (and not attached):
    #>  [1] httr_1.4.1               splines_4.0.0            bit64_0.9-7
    #>  [4] assertthat_0.2.1         BiocManager_1.30.10      blob_1.2.1
    #>  [7] GenomeInfoDbData_1.2.3   Rsamtools_2.3.7          yaml_2.2.1
    #> [10] pillar_1.4.3             RSQLite_2.2.0            lattice_0.20-41
    #> [13] glue_1.4.0               digest_0.6.25            RColorBrewer_1.1-2
    #> [16] XVector_0.27.2           colorspace_1.4-1         htmltools_0.4.0
    #> [19] Matrix_1.2-18            plyr_1.8.6               XML_3.99-0.3
    #> [22] pkgconfig_2.0.3          genefilter_1.69.0        bookdown_0.18
    #> [25] zlibbioc_1.33.1          purrr_0.3.4              xtable_1.8-4
    #> [28] scales_1.1.0             BiocParallel_1.21.3      annotate_1.65.1
    #> [31] KEGGREST_1.27.1          ggplot2_3.3.0            ellipsis_0.3.0
    #> [34] cli_2.0.2                survival_3.1-12          magrittr_1.5
    #> [37] crayon_1.3.4             memoise_1.1.0            evaluate_0.14
    #> [40] fansi_0.4.1              tools_4.0.0              formatR_1.7
    #> [43] lifecycle_0.2.0          stringr_1.4.0            locfit_1.5-9.4
    #> [46] munsell_0.5.0            Biostrings_2.55.7        compiler_4.0.0
    #> [49] rlang_0.4.5              grid_4.0.0               RCurl_1.98-1.2
    #> [52] rappdirs_0.3.1           bitops_1.0-6             rmarkdown_2.1
    #> [55] gtable_0.3.0             DBI_1.1.0                curl_4.3
    #> [58] R6_2.4.1                 GenomicAlignments_1.23.2 knitr_1.28
    #> [61] rtracklayer_1.47.0       bit_1.1-15.2             utf8_1.1.4
    #> [64] stringi_1.4.6            Rcpp_1.0.4.6             geneplotter_1.65.0
    #> [67] vctrs_0.2.4              png_0.1-7                tidyselect_1.0.0
    #> [70] xfun_0.13