# Analyze with GREAT

Author: Zuguang Gu ( z.gu@dkfz.de )

Date: 2016-10-17

GREAT (Genomic Regions Enrichment of Annotations Tool) is a popular web-based tool to associate biological functions to genomic regions. The rGREAT package makes GREAT anlaysis automatic by first constructing a HTTP POST request according to user's input and retrieving results from GREAT web server afterwards.

Load the package:

    library(rGREAT)

The input data is either a `GRanges` object or a BED-format data frame, no matter it is sorted or not. In following example, we use a data frame which is randomly generated.

    set.seed(123)
    bed = circlize::generateRandomBed(nr = 1000, nc = 0)
    bed[1:2, ]

    ##    chr   start      end
    ## 1 chr1  155726  2608935
    ## 2 chr1 6134977 10483365

Submit genomic regions by `submitGreatJob()`. Before submitting, genomic regions will be sorted and overlapping regions will be merged.

The returned variable `job` is a `GreatJob` class instance which can be used to retrieve results from GREAT server and stored results which are already downloaded.

    job = submitGreatJob(bed)

You can get the summary of your job by directly calling `job` variable.

    job

    ## Submit time: 2016-10-17 20:10:27
    ## Version: default
    ## Species: hg19
    ## Background: wholeGenome
    ## Model: Basal plus extension
    ##   Proximal: 5 kb upstream, 1 kb downstream,
    ##   plus Distal: up to 1000 kb
    ## Include curated regulatory domains
    ##
    ## Enrichment tables for following ontologies have been downloaded:
    ##   None

More parameters can be set for the job:

    job = submitGreatJob(bed, species = "mm9")
    job = submitGreatJob(bed, bg, species = "mm9")
    job = submitGreatJob(bed, adv_upstream = 10, adv_downstream = 2, adv_span = 2000)
    job = submitGreatJob(bed, rule = "twoClosest", adv_twoDistance = 2000)
    job = submitGreatJob(bed, rule = "oneClosest", adv_oneDistance = 2000)

Also you can choose different versions of GREAT for the analysis.

    job = submitGreatJob(bed, version = "3.0")
    job = submitGreatJob(bed, version = "2.0")

Available parameters are (following content is copied from GREAT website):

- `species`: `hg19` and `hg18` for human, `mm9` for mouse and `danRer7` for zebrafish
- `bgChoise`: Background regions. `wholeGenome` and `data`. If this value is set to `data`, `bg` argument should be specified
- `includeCuratedRegDoms`: Whether to include curated regulatory domains.
- `rule`: How to associate genomic regions to genes.
  - `basalPlusExt`: mode 'Basal plus extension'. Gene regulatory domain definition: Each gene is assigned a basal regulatory domain of a minimum distance upstream and downstream of the TSS (regardless of other nearby genes). The gene regulatory domain is extended in both directions to the nearest gene's basal domain but no more than the maximum extension in one direction.
    - `adv_upstream`: proximal extension to upstream (unit: kb)
    - `adv_downstream`: proximal extension to downstream (unit: kb)
    - `adv_span`: maximum extension (unit: kb)
  - `twoClosest`: mode 'Two nearest genes'. Gene regulatory domain definition: Each gene is assigned a regulatory domain that extends in both directions to the nearest gene's TSS but no more than the maximum extension in one direction.
    - `adv_twoDistance`: maximum extension (unit: kb)
  - `oneClosest`: mode 'Single nearest gene'. Gene regulatory domain definition: Each gene is assigned a regulatory domain that extends in both directions to the midpoint between the gene's TSS and the nearest gene's TSS but no more than the maximum extension in one direction.
    - `adv_oneDistance`: maximum extension (unit: kb)

With `job`, we can now retrieve results from GREAT. The first and the primary results are the tables which contain enrichment statistics for the analysis. By default it will retrieve results from three GO Ontologies and all pathway ontologies. All tables contains statistics for all terms no matter they are significant or not. Users can then make filtering yb self-defined cutoff.

One thing that users should note is that there is no column for adjusted p-values. But it is can be easily done by using `p.adjust()`.

The returned value of `getEnrichmentTables()` is a list of data frames in which each one corresponds to tables for single ontology. The structure of data frames are same as the tables on GREAT website.

    tb = getEnrichmentTables(job)
    names(tb)

    ## [1] "GO Molecular Function" "GO Biological Process" "GO Cellular Component"

    tb[[1]][1:2, ]

    ##           ID                                     name Binom_Genome_Fraction Binom_Expected
    ## 1 GO:0070002         glutamic-type peptidase activity          8.106316e-06    0.008146847
    ## 2 GO:0004731 purine-nucleoside phosphorylase activity          1.435477e-05    0.014426540
    ##   Binom_Observed_Region_Hits Binom_Fold_Enrichment Binom_Region_Set_Coverage Binom_Raw_PValue
    ## 1                          1             122.74690              0.0009950249      0.008113784
    ## 2                          1              69.31669              0.0009950249      0.014323080
    ##   Hyper_Total_Genes Hyper_Expected Hyper_Observed_Gene_Hits Hyper_Fold_Enrichment
    ## 1                 1     0.08785544                        1              11.38233
    ## 2                 1     0.08785544                        1              11.38233
    ##   Hyper_Gene_Set_Coverage Hyper_Term_Gene_Coverage Hyper_Raw_PValue
    ## 1            0.0006309148                        1       0.08785544
    ## 2            0.0006309148                        1       0.08785544

Information stored in `job` will be updated after retrieving enrichment tables.

    job

    ## Submit time: 2016-10-17 20:10:27
    ## Version: default
    ## Species: hg19
    ## Background: wholeGenome
    ## Model: Basal plus extension
    ##   Proximal: 5 kb upstream, 1 kb downstream,
    ##   plus Distal: up to 1000 kb
    ## Include curated regulatory domains
    ##
    ## Enrichment tables for following ontologies have been downloaded:
    ##   GO Biological Process
    ##   GO Cellular Component
    ##   GO Molecular Function

You can get results by either specifying the ontologies or by the pre-defined categories (categories already contains pre-defined sets of ontologies):

    tb = getEnrichmentTables(job, ontology = c("GO Molecular Function", "BioCyc Pathway"))
    tb = getEnrichmentTables(job, category = c("GO"))

All available ontology names for given species can be get by `availableOntologies()` and all available ontology categories can be get by `availableCategories()`. Here you do not need to provide species information because `job` already contains it.

    availableOntologies(job)

    ##  [1] "GO Molecular Function"            "GO Biological Process"
    ##  [3] "GO Cellular Component"            "Mouse Phenotype"
    ##  [5] "Human Phenotype"                  "Disease Ontology"
    ##  [7] "MSigDB Oncogenic Signatures"      "MSigDB Immunologic Signatures"
    ##  [9] "MSigDB Cancer Neighborhood"       "Placenta Disorders"
    ## [11] "PANTHER Pathway"                  "BioCyc Pathway"
    ## [13] "MSigDB Pathway"                   "MGI Expression: Detected"
    ## [15] "MSigDB Perturbation"              "MSigDB Predicted Promoter Motifs"
    ## [17] "MSigDB miRNA Motifs"              "InterPro"
    ## [19] "TreeFam"                          "HGNC Gene Families"
    ## [21] "Ensembl Genes"

    availableCategories(job)

    ## [1] "GO"                               "Phenotype Data and Human Disease"
    ## [3] "Pathway Data"                     "Gene Expression"
    ## [5] "Regulatory Motifs"                "Gene Families"

    availableOntologies(job, category = "Pathway Data")

    ## [1] "PANTHER Pathway" "BioCyc Pathway"  "MSigDB Pathway"

Association between genomic regions and genes can be get by `plotRegionGeneAssociationGraphs()`. The function will make the three plots which are same as on GREAT website and returns a `GRanges` object which contains the gene-region associations.

    par(mfrow = c(1, 3))
    res = plotRegionGeneAssociationGraphs(job)

    res[1:2, ]

    ## GRanges object with 2 ranges and 2 metadata columns:
    ##       seqnames              ranges strand |        gene   distTSS
    ##          <Rle>           <IRanges>  <Rle> | <character> <numeric>
    ##   [1]     chr1 [ 155726,  2608935]      * |      ATAD3C     -2738
    ##   [2]     chr1 [6134977, 10483365]      * |      ERRFI1   -222803
    ##   -------
    ##   seqinfo: 24 sequences from an unspecified genome; no seqlengths

For those regions that are not associated with any genes under current settings, the corresponding `gene` and `distTSS` columns will be `NA`.

You can also choose only plotting one of the three figures.

    plotRegionGeneAssociationGraphs(job, type = 1)

By specifying ontology and term ID, you can get the association in a certain term. Here the term ID is from the first column of the data frame which is returned by `getEnrichmentTables()`.

    par(mfrow = c(1, 3))
    res = plotRegionGeneAssociationGraphs(job, ontology = "GO Molecular Function",
        termID = "GO:0004984")

    res[1:2, ]

    ## GRanges object with 2 ranges and 2 metadata columns:
    ##       seqnames               ranges strand |        gene   distTSS
    ##          <Rle>            <IRanges>  <Rle> | <character> <numeric>
    ##   [1]     chr3 [97539225, 98605059]      * |       OR5K4      -556
    ##   [2]    chr11 [ 5451667,  5687557]      * |      OR52H1     -2833
    ##   -------
    ##   seqinfo: 6 sequences from an unspecified genome; no seqlengths

## Session info

    sessionInfo()

    ## R version 3.3.1 (2016-06-21)
    ## Platform: x86_64-w64-mingw32/x64 (64-bit)
    ## Running under: Windows Server 2012 R2 x64 (build 9600)
    ##
    ## locale:
    ## [1] LC_COLLATE=C                           LC_CTYPE=English_United States.1252
    ## [3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C
    ## [5] LC_TIME=English_United States.1252
    ##
    ## attached base packages:
    ## [1] parallel  stats4    stats     graphics  grDevices utils     datasets  methods   base
    ##
    ## other attached packages:
    ## [1] rGREAT_1.6.0         GenomicRanges_1.26.0 GenomeInfoDb_1.10.0  IRanges_2.8.0
    ## [5] S4Vectors_0.12.0     BiocGenerics_0.20.0  knitr_1.14
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] circlize_0.3.9       bitops_1.0-6         grid_3.3.1           formatR_1.4
    ##  [5] magrittr_1.5         evaluate_0.10        GlobalOptions_0.0.10 stringi_1.1.2
    ##  [9] zlibbioc_1.20.0      XVector_0.14.0       GetoptLong_0.1.5     rjson_0.2.15
    ## [13] tools_3.3.1          stringr_1.1.0        RCurl_1.95-4.8       colorspace_1.2-7
    ## [17] shape_1.4.2