Package 'rGREAT'

Title: GREAT Analysis - Functional Enrichment on Genomic Regions
Description: GREAT (Genomic Regions Enrichment of Annotations Tool) is a type of functional enrichment analysis directly performed on genomic regions. This package implements the GREAT algorithm (the local GREAT analysis), also it supports directly interacting with the GREAT web service (the online GREAT analysis). Both analysis can be viewed by a Shiny application. rGREAT by default supports more than 600 organisms and a large number of gene set collections, as well as self-provided gene sets and organisms from users. Additionally, it implements a general method for dealing with background regions.
Authors: Zuguang Gu [aut, cre]
Maintainer: Zuguang Gu <[email protected]>
License: MIT + file LICENSE
Version: 2.9.0
Built: 2024-11-18 04:15:04 UTC
Source: https://github.com/bioc/rGREAT

Help Index

Available ontology categories of the GREAT job

Description

Available ontology categories of the GREAT job

Usage

## S4 method for signature 'GreatJob'
availableCategories(object)

Arguments

object

A GreatJob-class object returned by submitGreatJob.

Details

The values of the supported categories sometime change. You should run the function to get the real-time values. The meaning of categories returned is quite self-explained by the name.

Value

The returned value is a vector of categories.

Author(s)

Zuguang gu <[email protected]>

Examples

job = readRDS(system.file("extdata", "GreatJob.rds", package = "rGREAT"))
availableCategories(job)

All available ontology names of the GREAT job

Description

All available ontology names of the GREAT job

Usage

## S4 method for signature 'GreatJob'
availableOntologies(object, category = NULL)

Arguments

object

A GreatJob-class object returned by submitGreatJob.
category

one or multiple categories. All available categories can be got by availableCategories.

Details

The values of the supported ontologies sometime change. You should run the function to get the real-time values. The meaning of ontology returned is quite self-explained by the name.

Value

The returned values is a vector of ontologies.

Author(s)

Zuguang gu <[email protected]>

Examples

job = readRDS(system.file("extdata", "GreatJob.rds", package = "rGREAT"))
availableOntologies(job)

Extend TSS

Description

Extend TSS

Usage

extendTSS(gene, seqlengths = NULL, genome = NULL,
    gene_id_type = NULL, mode = "basalPlusExt", extend_from = c("TSS", "gene"),
    basal_upstream = 5000, basal_downstream = 1000, extension = 1000000,
    verbose = great_opt$verbose, .attr = list())

Arguments

gene

A GRanges object of gene (or TSS) coordinates.
extend_from

Should the gene be extended only from its TSS or the complete gene?
seqlengths

A named vector of chromosome lengths. If it is not provided, it is taken by seqlengths(gene).
genome

UCSC genome can be set here, then seqlengths will be automatically retrieved from UCSC server.
gene_id_type

Gene ID types in gene. You need to set this argument if you use built-in gene sets in great so that genes can be correctly mapped. The value can only be one of "SYMBOL", "ENTREZ", "ENSEMBL" and "REFSEQ".
mode

The mode to extend TSS. Value should be one of 'basalPlusExt', 'twoClosest' and 'oneClosest'. See "Details" section.
basal_upstream

In 'basalPlusExt' mode, number of base pairs extending to the upstream of TSS to form the basal domains.
basal_downstream

In 'basalPlusExt' mode, number of base pairs extending to the downstream of TSS to form the basal domains.
extension

Extensions from the basal domains. The value can also be a vector of length two which corresponds to extension to upstream and downstream respectively.
verbose

Whether to print messages.
.attr

Only used internally.

Details

Following are general explanations of the three modes for extending TSS:

basalPlusExt

1. TSS are extended into basal domains (e.g. by upstream 5kb, downstream 1kb); 2. basal domains are sorted by their genomic coordinates; 3. each basal domain is extended to its both sides until it reaches the next TSS's basal domain or it reaches the maximal extension (e.g. 1000kb).

twoClosest

1. TSS are sorted by their genomic coordinates; 2. each TSS is extended to its both sides until it reaches the next TSS or it reaches the maximal extension (e.g. 1000kb).

oneClosest

1. TSS are sorted by their genomic coordinates; 2. each TSS is extended to its both sides until it reaches the middle point of itself and the next TSS or it reaches the maximal extension (e.g. 1000kb).

The official explanation is at https://great-help.atlassian.net/wiki/spaces/GREAT/pages/655443/Association+Rules .

Value

A GRanges object with one meta column 'gene_id'.

Examples

# There is no example
NULL

Extend TSS

Description

Extend TSS

Usage

extendTSSFromDataFrame(df, seqlengths, genome = NULL,
    strand = NULL, gene_id = NULL,
    gene_id_type = NULL, verbose = great_opt$verbose, ...)

Arguments

df

A bed-like data frame where the first three columns should be chromosomes, start positions, end positions. It does not matter whether regions correspond to genes or TSS.

seqlengths

A named vector of chromosome lengths.
genome

UCSC genome can be set here, then seqlengths will be automatically retrieved from UCSC server.
strand

The strand information can be provided in df as a column named "strand" or as a column with "+"/"-"/"*", or the strand information can be provided as a vector and be assigined to this argument.
gene_id

The gene ID information can be provided in df as a column named "gene_id", or it can be provided as a vector and be assigned to this argument.
gene_id_type

Gene ID types in df. You need to set this argument if you use built-in gene sets in great so that genes can be correctly mapped. The value can only be one of "SYMBOL", "ENTREZ", "ENSEMBL" and "REFSEQ".
verbose

Whether to print messages.
...

All pass to extendTSS.

Value

A GRanges object with one meta column 'gene_id'.

Examples

# There is no example
NULL

Extend TSS

Description

Extend TSS

Usage

extendTSSFromOrgDb(orgdb, verbose = great_opt$verbose, ...)

Arguments

orgdb

Name of "org.*" packages from Bioconductor. All supported OrgDb packages are in rGREAT:::BIOC_ANNO_PKGS$orgdb.

verbose

Whether to print messages.
...

All pass to extendTSS.

Value

A GRanges object with one meta column 'gene_id'.

Examples

if(FALSE) {
extendTSSFromOrgDb("Org.Hs.eg.db")
extendTSSFromOrgDb("hg19")
}

Extend TSS

Description

Extend TSS

Usage

extendTSSFromTxDb(txdb, verbose = great_opt$verbose, ...)

Arguments

txdb

Name of "TxDb.*" packages from Bioconductor. All supported TxDb packages are in rGREAT:::BIOC_ANNO_PKGS$txdb. Note short genome version can also be used here such as "hg19" or "hg19.knownGene".
verbose

Whether to print messages.
...

All pass to extendTSS.

Value

A GRanges object with one meta column 'gene_id'.

Examples

if(FALSE) {
extendTSSFromTxDb("TxDb.Hsapiens.UCSC.hg19.knownGene")
extendTSSFromTxDb("hg19")
}

Method dispatch page for getEnrichmentTable

Description

Method dispatch page for getEnrichmentTable.

Dispatch

getEnrichmentTable can be dispatched on following classes:

Examples

# no example
NULL

Get a single enrichment table from GREAT web server

Description

Get a single enrichment table from GREAT web server

Usage

## S4 method for signature 'GreatJob'
getEnrichmentTable(object, ontology, ...)

Arguments

object

A GreatJob-class object returned by submitGreatJob.
ontology

A single ontology names. Valid values are in availableOntologies.

...

All pass to getEnrichmentTables,GreatJob-method.

Value

A data frame of the enrichment results for a single ontology.

Examples

job = readRDS(system.file("extdata", "GreatJob.rds", package = "rGREAT"))
tb = getEnrichmentTable(job, ontology = "GO Molecular Function")
head(tb)

Get enrichment table

Description

Get enrichment table

Usage

## S4 method for signature 'GreatObject'
getEnrichmentTable(object, min_region_hits = 5)

Arguments

object

A GreatObject-class object returned by great.
min_region_hits

Minimal number of input regions overlapping to the geneset associated regions.

Details

Note: adjusted p-values are re-calculated based on min_region_hits.

Value

A data frame of enrichment results

Examples

obj = readRDS(system.file("extdata", "GreatObject.rds", package = "rGREAT"))
getEnrichmentTable(obj)

Method dispatch page for getEnrichmentTables

Description

Method dispatch page for getEnrichmentTables.

Dispatch

getEnrichmentTables can be dispatched on following classes:

Examples

# no example
NULL

Get enrichment tables from GREAT web server

Description

Get enrichment tables from GREAT web server

Usage

## S4 method for signature 'GreatJob'
getEnrichmentTables(object, ontology = NULL, category = "GO",
    request_interval = 10, max_tries = 100, download_by = c("json", "tsv"),
    verbose = TRUE)

Arguments

object

A GreatJob-class object returned by submitGreatJob.
ontology

Ontology names. Valid values are in availableOntologies. ontology is prior to category argument.
category

Pre-defined ontology categories. One category can contain more than one ontologies. Valid values are in availableCategories
request_interval

Time interval for two requests. Default is 300 seconds.
max_tries

Maximal times for automatically reconnecting GREAT web server.
download_by

Internally used. The complete enrichment table is provided as json data on the website, but there is no information of gene-region association. By setting download_by = 'tsv', another URL from GREAT will be envoked which also contains detailed information of which genes are associated with each input region, but due to the size of the output, only top 500 terms will be returned. So if you do not really want the gene-region association column, take the default value of this argument. The columns that contain statistics are identical.
verbose

Whether to print messages.

Value

The structure of the data frames are same as the tables available on GREAT website.

See

availableOntologies, availableCategories

Author(s)

Zuguang gu <[email protected]>

Examples

job = readRDS(system.file("extdata", "GreatJob.rds", package = "rGREAT"))
tbl = getEnrichmentTables(job)
names(tbl)
head(tbl[[1]])
job

tbl = getEnrichmentTables(job, ontology = "GO Molecular Function")
tbl = getEnrichmentTables(job, category = "GO")

Get enrichment table

Description

Get enrichment table

Usage

## S4 method for signature 'GreatObject'
getEnrichmentTables(object, ...)

Arguments

object

A GreatObject-class object returned by great.
...

All passed to getEnrichmentTable,GreatObject-method.

Details

Please use getEnrichmentTable,GreatObject-method directly.

Value

A data frame of enrichment results

Examples

# There is no example
NULL

Get gap regions from UCSC

Description

Get gap regions from UCSC

Usage

getGapFromUCSC(genome, seqnames = NULL)

Arguments

genome

UCSC genome, such as "hg19".
seqnames

A vector of chromosome names.

Value

A GRanges object.

Examples

getGapFromUCSC("hg19")

Get GO gene sets from BioMart

Description

Get GO gene sets from BioMart

Usage

getGeneSetsFromBioMart(dataset, ontology = "bp")

Arguments

dataset

Name of the dataset.
ontology

Value should be bp, mf or cc.

Details

GO gene sets are from BioMartGOGeneSets::getBioMartGOGeneSets.

Value

A list of vectors where each vector contains Ensembl IDs annotated to a GO term.

Examples

# There is no example
NULL

Get GO gene sets from OrgDb object

Description

Get GO gene sets from OrgDb object

Usage

getGeneSetsFromOrgDb(orgdb, ontology = "BP")

Arguments

orgdb

An OrgDb object.
ontology

Value should be bp, mf or cc.

Value

A list of vectors where each vector contains Entrez IDs annotated to a GO term.

Examples

# There is no example
NULL

Get Gencode genes

Description

Get Gencode genes

Usage

getGenesFromGencode(version)

Arguments

version

Gencode version, e.g. v19 for human, vM21 for mouse.

Details

Only the protein coding genes.

Value

A GRanges object.

Examples

# There is no example
NULL

Get genome data from NCBI

Description

Get genome data from NCBI

Usage

getGenomeDataFromNCBI(refseq_assembly_accession, return_granges = FALSE)

Arguments

refseq_assembly_accession

The RefSeq accession number for the assembly, such as "GCF_000001405.40" for human.
return_granges

If the assembly is already on chromosome level, it will directly construct a GRanges object where "chromosomes" are only used and chromosome lengths are corrected fitted in its seqlengths.

Details

Only protein coding genes are used.

Value

If return_granges is set to FALSE, it returns a list of two data frames:

genome

A data frame of several columns.

gene

A data frame for genes. The first column contains the RefSeq accession numbers of the corresponding contigs. If the genome is assembled on the chromosome level, the first column corresponds to chromosomes. The contig names can be converted to other names with the information in the genome data frame.

Examples

if(FALSE) {
getGenomeDataFromNCBI("GCF_000001405.40", return_granges = TRUE)
getGenomeDataFromNCBI("GCF_000001405.40")
}

Get built-in TSS from GREAT

Description

Get built-in TSS from GREAT

Usage

getGREATDefaultTSS(genome)

Arguments

genome

Only support "hg19", "hg38", "mm10", "mm9". Files are downloaded from https://great-help.atlassian.net/wiki/spaces/GREAT/pages/655445/Genes .

Value

A GRanges object.

Examples

# There is no example
NULL

Get the corresponding assembly id for a kegg organism

Description

Get the corresponding assembly id for a kegg organism

Usage

getKEGGGenome(organism)

Arguments

organism

The organism code on KEGG.

Value

The Refseq access ID for the genome.

Examples

# There is no example
NULL

Get KEGG pathway gene sets

Description

Get KEGG pathway gene sets

Usage

getKEGGPathways(organism, as_table = FALSE)

Arguments

organism

The organism code on KEGG.
as_table

Whether to return the gene sets as a two-column table.

Value

A list of a data frame, depends on the value of as_table.

Examples

# There is no example
NULL

Get RefSeq genes from UCSC

Description

Get RefSeq genes from UCSC

Usage

getRefSeqGenesFromUCSC(genome, subset = c("RefSeqSelect", "RefSeqCurated"))

Arguments

genome

UCSC genome, such as "hg19".
subset

Subset of RefSeq genes. See https://genome.ucsc.edu/cgi-bin/hgTrackUi?db=hg38&g=refSeqComposite .

Value

A GenomicRanges object.

Examples

# There is no example
NULL

Method dispatch page for getRegionGeneAssociations

Description

Method dispatch page for getRegionGeneAssociations.

Dispatch

getRegionGeneAssociations can be dispatched on following classes:

Examples

# no example
NULL

Get region-gene associations

Description

Get region-gene associations

Usage

## S4 method for signature 'GreatJob'
getRegionGeneAssociations(object, ontology = NULL, term_id = NULL,
    request_interval = 10, max_tries = 100, verbose = great_opt$verbose)

Arguments

object

A GreatJob-class object returned by submitGreatJob.
ontology

ontology name
term_id

Term id in the selected ontology.
request_interval

Time interval for two requests. Default is 300 seconds.
max_tries

Maximal times for automatically reconnecting GREAT web server.
verbose

Whether to show messages.

Value

A GRanges object. Please the two meta columns are in formats of CharacterList and IntegerList because a region may associate to multiple genes.

Please note, the distance is from the middle points of input regions to TSS.

Author(s)

Zuguang gu <[email protected]>

Examples

job = readRDS(system.file("extdata", "GreatJob.rds", package = "rGREAT"))

gr = getRegionGeneAssociations(job)
gr

Get region-gene associations

Description

Get region-gene associations

Usage

## S4 method for signature 'GreatObject'
getRegionGeneAssociations(object, term_id = NULL, by_middle_points = FALSE,
    use_symbols = TRUE)

Arguments

object

A GreatObject-class object returned by great.
term_id

Term ID.
by_middle_points

Whether the distances are calculated from the middle points of input regions?
use_symbols

Whether to use gene symbols

Value

A GRanges object. Please the two meta columns are in formats of CharacterList and IntegerList because a region may associate to multiple genes.

Examples

obj = readRDS(system.file("extdata", "GreatObject.rds", package = "rGREAT"))
getRegionGeneAssociations(obj)

Get the internally used TSS

Description

Get the internally used TSS

Usage

getTSS(tss_source, biomart_dataset = NULL)

Arguments

tss_source

The same format as in great.
biomart_dataset

The same format as in great.

Value

A GRanges object.

Examples

# There is no example
NULL

Perform GREAT analysis

Description

Perform GREAT analysis

Usage

great(gr, gene_sets, tss_source, biomart_dataset = NULL,
    min_gene_set_size = 5, mode = "basalPlusExt", extend_from = c("TSS", "gene"),
    basal_upstream = 5000, basal_downstream = 1000, extension = 1000000,
    extended_tss = NULL, background = NULL, exclude = "gap",
    cores = 1, verbose = great_opt$verbose)

Arguments

gr

A GRanges object. This is the input regions. It is important to keep consistent for the chromosome names of the input regions and the internal TSS regions. Use getTSS to see the format of internal TSS regions.
gene_sets

A single string of defautly supported gene sets collections (see the full list in "Genesets" section), or a named list of vectors where each vector correspond to a gene set.
tss_source

Source of TSS. See "TSS" section.
biomart_dataset

The value should be in BioMartGOGeneSets::supportedOrganisms.

min_gene_set_size

Minimal size of gene sets.
mode

The mode to extend genes. Value should be one of 'basalPlusExt', 'twoClosest' and 'oneClosest'. See extendTSS for details.
extend_from

Should the gene be extended only from its TSS or the complete gene?
basal_upstream

In 'basalPlusExt' mode, number of base pairs extending to the upstream of TSS to form the basal domains.
basal_downstream

In 'basalPlusExt' mode, number of base pairs extending to the downstream of TSS to form the basal domains.
extension

Extensions from the basal domains.
extended_tss

If your organism is not defaultly supported, you can first prepare one by extendTSSFromDataFrame or extendTSS, and set the object to this argument. Please see more examples in the vignette.
background

Background regions. The value can also be a vector of chromosome names.
exclude

Regions that are excluded from analysis such as gap regions (which can be get by getGapFromUCSC). The value can also be a vector of chromosome names. It also allows a special character value "gap" so that gap regions for corresponding organism will be removed from the analysis.
cores

Number of cores to use.
verbose

Whether to print messages.

Details

When background or exclude is set, the analysis is restricted in the background regions, still by using Binomial method. Note this is different from the original GREAT method which uses Fisher's exact test if background regions is set. See submitGreatJob for explanations.

By default, gap regions are excluded from the analysis.

Value

A GreatObject-class object. The following methods can be applied on it:

Tss

rGREAT supports TSS from many organisms. The value of tss_source should be encoded in a special format:

  • Name of TxDb.* packages. Supported packages are in rGREAT:::BIOC_ANNO_PKGS$txdb.

  • Genome version of the organism, e.g. "hg19". Then the corresponding TxDb will be used.

  • In a format of RefSeqCurated:$genome where $genome is the genome version of an organism, such as hg19. RefSeqCurated subset will be used.

  • In a format of RefSeqSelect:$genome where $genome is the genome version of an organism, such as hg19. RefSeqSelect subset will be used.

  • In a format of Gencode_v$version where $version is gencode version, such as 19 (for human) or M21 for mouse. Gencode protein coding genes will be used.

  • In a format of GREAT:$genome, where $genome can only be mm9, mm10, hg19, hg38. The TSS from GREAT will be used.

Genesets

rGREAT supports the following built-in GO gene sets for all organisms (note "GO:" can be omitted):

"GO:BP":

Biological Process, from GO.db package.

"GO:CC":

Cellular Component, from GO.db package.

"GO:MP":

Molecular Function, from GO.db pacakge.

rGREAT also supports built-in gene sets collections from MSigDB (note this is only for human, "msigdb:" can be omitted):

"msigdb:H"

Hallmark gene sets.

"msigdb:C1"

Positional gene sets.

"msigdb:C2"

Curated gene sets.

"msigdb:C2:CGP"

C2 subcategory: chemical and genetic perturbations gene sets.

"msigdb:C2:CP"

C2 subcategory: canonical pathways gene sets.

"msigdb:C2:CP:BIOCARTA"

C2 subcategory: BioCarta subset of CP.

"msigdb:C2:CP:KEGG"

C2 subcategory: KEGG subset of CP.

"msigdb:C2:CP:PID"

C2 subcategory: PID subset of CP.

"msigdb:C2:CP:REACTOME"

C2 subcategory: REACTOME subset of CP.

"msigdb:C2:CP:WIKIPATHWAYS"

C2 subcategory: WIKIPATHWAYS subset of CP.

"msigdb:C3"

Regulatory target gene sets.

"msigdb:C3:MIR:MIRDB"

miRDB of microRNA targets gene sets.

"msigdb:C3:MIR:MIR_LEGACY"

MIR_Legacy of MIRDB.

"msigdb:C3:TFT:GTRD"

GTRD transcription factor targets gene sets.

"msigdb:C3:TFT:TFT_LEGACY"

TFT_Legacy.

"msigdb:C4"

Computational gene sets.

"msigdb:C4:CGN"

C4 subcategory: cancer gene neighborhoods gene sets.

"msigdb:C4:CM"

C4 subcategory: cancer modules gene sets.

"msigdb:C5"

Ontology gene sets.

"msigdb:C5:GO:BP"

C5 subcategory: BP subset.

"msigdb:C5:GO:CC"

C5 subcategory: CC subset.

"msigdb:C5:GO:MF"

C5 subcategory: MF subset.

"msigdb:C5:HPO"

C5 subcategory: human phenotype ontology gene sets.

"msigdb:C6"

Oncogenic signature gene sets.

"msigdb:C7"

Immunologic signature gene sets.

"msigdb:C7:IMMUNESIGDB"

ImmuneSigDB subset of C7.

"msigdb:C7:VAX"

C7 subcategory: vaccine response gene sets.

"msigdb:C8"

Cell type signature gene sets.

If the defaultly supported TxDb is used, Entrez gene ID is always used as the main gene ID. If you provide a self-defined gene_sets or extended_tss, you need to make sure they two have the same gene ID types.

Biomart

rGREAT supports a large number of organisms of which the information is retrieved from Ensembl BioMart. The name of a BioMart dataset can be assigned to argument biomart_dataset. All supported organisms can be found with BioMartGOGeneSets::supportedOrganisms.

Examples

if(FALSE) {
gr = randomRegions(genome = "hg19")
res = great(gr, "MSigDB:H", "txdb:hg19")
res = great(gr, "MSigDB:H", "TxDb.Hsapiens.UCSC.hg19.knownGene")
res = great(gr, "MSigDB:H", "RefSeq:hg19")
res = great(gr, "MSigDB:H", "GREAT:hg19")
res = great(gr, "MSigDB:H", "Gencode_v19")
res = great(gr, "GO:BP", "hsapiens_gene_ensembl")
}

Global parameters for rGREAT

Description

Global parameters for rGREAT

Usage

great_opt(..., RESET = FALSE, READ.ONLY = NULL, LOCAL = FALSE, ADD = FALSE)

Arguments

...

Arguments for the parameters, see "details" section
RESET

Reset to default values.
READ.ONLY

Please ignore.
LOCAL

Pllease ignore.
ADD

Please ignore.

Details

There are following parameters:

verbose

Whether to show messages.

Examples

great_opt

Constructor method for GreatJob class

Description

Constructor method for GreatJob class

Usage

GreatJob(...)

Arguments

...

arguments.

Details

There is no public constructor method for the GreatJob-class.

Value

No value is returned.

Author(s)

Zuguang Gu <[email protected]>

Examples

# There is no example
NULL

Class to store and retrieve GREAT results

Description

Class to store and retrieve GREAT results

Details

After submitting request to GREAT server, the generated results will be available on GREAT server for some time. The GreatJob-class is defined to store parameters that user has set and result tables what were retrieved from GREAT server.

Constructor

Users don't need to construct by hand, submitGreatJob is used to generate a GreatJob-class instance.

Workflow

After submitting request to GREAT server, users can perform following steps:

Author(s)

Zuguang gu <[email protected]>

Examples

# There is no example
NULL

Constructor method for GreatObject class

Description

Constructor method for GreatObject class

Usage

GreatObject(...)

Arguments

...

arguments.

Details

There are following methods that can be applied on GreatObject-class object:

Value

No value is returned.

Author(s)

Zuguang Gu <[email protected]>

Examples

# There is no example
NULL

Class for local GREAT analysis

Description

Class for local GREAT analysis

Details

great returns A GreatObject-class object.

Examples

# There is no example
NULL

Plot region-gene associations

Description

Plot region-gene associations

Usage

## S4 method for signature 'GreatJob'
plotRegionGeneAssociationGraphs(object, ...)

Arguments

object

A GreatJob-class object returned by submitGreatJob.
...

All passed to plotRegionGeneAssociations,GreatJob-method.

Details

This function will be removed in the future, please use plotRegionGeneAssociations,GreatJob-method instead.

Examples

# There is no example
NULL

Method dispatch page for plotRegionGeneAssociations

Description

Method dispatch page for plotRegionGeneAssociations.

Dispatch

plotRegionGeneAssociations can be dispatched on following classes:

Examples

# no example
NULL

Plot region-gene associations

Description

Plot region-gene associations

Usage

## S4 method for signature 'GreatJob'
plotRegionGeneAssociations(object, ontology = NULL, term_id = NULL, which_plot = 1:3,
    request_interval = 10, max_tries = 100, verbose = great_opt$verbose)

Arguments

object

A GreatJob-class object returned by submitGreatJob.
ontology

A single ontology names. Valid values are in availableOntologies.

term_id

Term id in the selected ontology
which_plot

Which plots to draw? The value should be in 1, 2, 3. See "Details" section for explanation.
request_interval

Time interval for two requests. Default is 300 seconds.
max_tries

Maximal times for automatically reconnecting GREAT web server.
verbose

Whether to show messages.

Details

There are following figures:

  • Association between regions and genes (which_plot = 1).

  • Distribution of distance to TSS (which_plot = 2).

  • Distribution of absolute distance to TSS (which_plot = 3).

If ontology and term_id are set, only regions and genes corresponding to selected ontology term will be used. Valid value for ontology is in availableOntologies and valid value for term_id is from 'id' column in the table which is returned by getEnrichmentTables.

Author(s)

Zuguang gu <[email protected]>

Examples

job = readRDS(system.file("extdata", "GreatJob.rds", package = "rGREAT"))

plotRegionGeneAssociations(job)
plotRegionGeneAssociations(job, which_plot = 1)
# Do not use other term_id for this example, or you need to generate a new `job` object.
plotRegionGeneAssociations(job, ontology = "GO Molecular Function",
    term_id = "GO:0004984")

Plot region-gene associations

Description

Plot region-gene associations

Usage

## S4 method for signature 'GreatObject'
plotRegionGeneAssociations(object, term_id = NULL, which_plot = 1:3)

Arguments

object

A GreatObject-class object returned by great.
term_id

Term ID.
which_plot

Which plots to draw? The value should be in 1, 2, 3. See "Details" section for explanation.

Details

There are following figures:

  • Association between regions and genes (which_plot = 1).

  • Distribution of distance to TSS (which_plot = 2).

  • Distribution of absolute distance to TSS (which_plot = 3).

Examples

obj = readRDS(system.file("extdata", "GreatObject.rds", package = "rGREAT"))
plotRegionGeneAssociations(obj)

Method dispatch page for plotVolcano

Description

Method dispatch page for plotVolcano.

Dispatch

plotVolcano can be dispatched on following classes:

Examples

# no example
NULL

Make volcano plot

Description

Make volcano plot

Usage

## S4 method for signature 'GreatJob'
plotVolcano(object, ontology, min_region_hits = 5,
    x_values = c("fold_enrichment", "z-score"),
    y_values = c("p_value", "p_adjust"),
    main = NULL)

Arguments

object

A GreatJob-class object returned by submitGreatJob.
ontology

A single ontology names. Valid values are in availableOntologies.

min_region_hits

Minimal number of input regions overlapping to the geneset associated regions.
x_values

Which values for the x-axis.
y_values

Which values for the y-axis.
main

Title of the plot.

Details

Since the enrichment is an over-representation test, it is only the half volcano.

Examples

# There is no example
NULL

Make volcano plot

Description

Make volcano plot

Usage

## S4 method for signature 'GreatObject'
plotVolcano(object, min_region_hits = 5,
    x_values = c("fold_enrichment", "z-score"),
    y_values = c("p_value", "p_adjust"),
    main = NULL)

Arguments

object

A GreatObject-class object returned by great.
min_region_hits

Minimal number of input regions overlapping to the geneset associated regions.
x_values

Which values for the x-axis.
y_values

Which values for the y-axis.
main

Title of the plot.

Details

Since the enrichment is an over-representation test, it is only the half volcano.

Examples

# There is no example
NULL

Generate random regions

Description

Generate random regions

Usage

randomRegions(genome = NULL, nr = 1000, seqlengths = NULL,
    width_fun = function(n) runif(n, min = 1000, max = 10000))

Arguments

genome

UCSC genome version, e.g. "hg19".
nr

Number of regions.
seqlengths

Alternatively, you can also specify a named vector of seqlengths (chromosome lengths).
width_fun

A function which defines the distribution of region widths.

Details

The number of regions per chromosome is proportional to the chromsome length.

Examples

gr = randomRegions(genome = "hg19")
quantile(width(gr))

Generate random regions from a BioMart genome

Description

Generate random regions from a BioMart genome

Usage

randomRegionsFromBioMartGenome(biomart_dataset, nr = 1000, ...)

Arguments

biomart_dataset

A BioMart dataset. Values should be in BioMartGOGeneSets::supportedOrganisms.
nr

Number of regions.
...

Pass to randomRegions.

Details

The number of regions per chromosome is proportional to the chromsome length.

Examples

if(FALSE) {
    # Giant panda
    gr = randomRegionsFromBioMartGenome("amelanoleuca_gene_ensembl")
}

Read gmt gene sets file

Description

Read gmt gene sets file

Usage

read_gmt(x, from = NULL, to = NULL, orgdb = NULL)

Arguments

x

The file name of a .gmt file.
from

Gene ID type in the original gmt file. Value can only take values in 'ENTREZ/SYMBOL/ENSEMBL/REFSEQ'.
to

Gene ID type that you want to convert to. Value can only take values in 'ENTREZ/SYMBOL/ENSEMBL/REFSEQ'.
orgdb

The name of an OrgDb database.

Value

A named list of vectors.

Examples

read_gmt(url("http://dsigdb.tanlab.org/Downloads/D2_LINCS.gmt"))

Reduce by start and end

Description

Reduce by start and end

Usage

reduce_by_start_and_end(s, e)

Arguments

s

Start positions. Sorted.
e

End positions. Sorted.

Details

Only internally used.

Value

Sum of total widths of the reduced regions.

Examples

# There is no example
NULL

Method dispatch page for shinyReport

Description

Method dispatch page for shinyReport.

Dispatch

shinyReport can be dispatched on following classes:

Examples

# no example
NULL

Shiny app on the GreatJob object

Description

Shiny app on the GreatJob object

Usage

## S4 method for signature 'GreatJob'
shinyReport(object)

Arguments

object

The GreatJob object returned by submitGreatJob.

Value

A shiny app object.

Examples

if(FALSE) {
# pseudo code
job = submitGreatJob(...)
shinyReport(job)
}

Shiny app on the GreatObject object

Description

Shiny app on the GreatObject object

Usage

## S4 method for signature 'GreatObject'
shinyReport(object)

Arguments

object

The GreatObject object returned by great.

Value

A shiny app object.

Examples

if(FALSE) {
# pseudo code
obj = great(...)
shinyReport(obj)
}

Perform online GREAT analysis

Description

Perform online GREAT analysis

Usage

submitGreatJob(gr, bg = NULL,
    gr_is_zero_based      = FALSE,
    species               = "hg19",
    genome                = species,
    includeCuratedRegDoms = TRUE,
    rule                  = c("basalPlusExt", "twoClosest", "oneClosest"),
    adv_upstream          = 5.0,
    adv_downstream        = 1.0,
    adv_span              = 1000.0,
    adv_twoDistance       = 1000.0,
    adv_oneDistance       = 1000.0,
    request_interval = 60,
    max_tries = 10,
    version = DEFAULT_VERSION,
    base_url = "http://great.stanford.edu/public/cgi-bin",
    use_name_column = FALSE,
    verbose = help, help = great_opt$verbose)

Arguments

gr

A GRanges object or a data frame which contains at least three columns (chr, start and end).
bg

Not supported any more. See explanations in section "When_background_regions_are_set".
gr_is_zero_based

Are start positions in gr zero-based?
genome

Genome. "hg38", "hg19", "mm10", "mm9" are supported in GREAT version 4.x.x, "hg19", "mm10", "mm9", "danRer7" are supported in GREAT version 3.x.x and "hg19", "hg18", "mm9", "danRer7" are supported in GREAT version 2.x.x.
species

The same as genome but it will be deprecated soon.
includeCuratedRegDoms

Whether to include curated regulatory domains, see https://great-help.atlassian.net/wiki/spaces/GREAT/pages/655443/Association+Rules#AssociationRules-CuratedRegulatoryDomains .
rule

How to associate genomic regions to genes. See 'Details' section.
adv_upstream

Unit: kb, only used when rule is basalPlusExt.
adv_downstream

Unit: kb, only used when rule is basalPlusExt.
adv_span

Unit: kb, only used when rule is basalPlusExt.
adv_twoDistance

Unit: kb, only used when rule is twoClosest.
adv_oneDistance

Unit: kb, only used when rule is oneClosest.
request_interval

Time interval for two requests. Default is 300 seconds.
max_tries

Maximal times for aotumatically reconnecting GREAT web server.
version

Version of GREAT. The value should be "4.0.4", "3.0.0", "2.0.2". Shorten version numbers can also be used, such as using "4" or "4.0" is same as "4.0.4".
base_url

the url of cgi-bin path, only used when it is explicitly specified.
use_name_column

If the input is a data frame, whether to use the fourth column as the "names" of regions?
verbose

Whether to print help messages.
help

Whether to print help messages. This argument will be replaced by verbose in future versions.

Details

Note: On Aug 19 2019 GREAT released version 4(https://great-help.atlassian.net/wiki/spaces/GREAT/pages/655442/Version+History ) where it supports hg38 genome and removes some ontologies such pathways. submitGreatJob still takes hg19 as default. hg38 can be specified by the genome = "hg38" argument. To use the older versions such as 3.0.0, specify as submitGreatJob(..., version = "3.0.0").

Note it does not use the standard GREAT API. This function directly send data to GREAT web server by HTTP POST.

Following text is copied from GREAT web site ( http://great.stanford.edu/public/html/ )

Explanation of rule and settings with names started with 'adv_' (advanced settings):

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, controlled by adv_upstream and adv_downstream argument). 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 (controlled by adv_span).

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 (controlled by adv_twoDistance) but no more than the maximum extension in one direction.

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 (controlled by adv_oneDistance) but no more than the maximum extension in one direction.

Value

A GreatJob-class object which can be used to get results from GREAT server. The following methods can be applied on it:

When_background_regions_are_set

Note when bg argument is set to a list of background regions, GREAT uses a completely different test!

When bg is set, gr should be exactly subset of bg. For example, let's say a background region list contains five regions: [1, 10], [15, 23], [34, 38], [40, 49], [54, 63], gr can only be a subset of the five regions, which means gr can take [15, 23], [40, 49], but it cannot take [16, 20], [39, 51]. In this setting, regions are taken as single units and Fisher's exact test is applied for calculating the enrichment (by testing number of regions in the 2x2 contigency table).

Check https://great-help.atlassian.net/wiki/spaces/GREAT/pages/655452/File+Formats#FileFormats-Whatshouldmybackgroundregionsfilecontain? for more explanations.

Please note from rGREAT 1.99.0, setting bg is not supported any more and this argument will be removed in the future. You can either directly use GREAT website or use other Bioconductor packages such as "LOLA" to perform the Fisher's exact test-based analysis.

If you want to restrict the input regions to background regions (by intersections) and still to apply Binomial test there, please consider to use local GREAT by great.

Author(s)

Zuguang gu <[email protected]>

See Also

great for the local implementation of GREAT algorithm.

Examples

set.seed(123)
gr = randomRegions(nr = 1000, genome = "hg19")
job = submitGreatJob(gr)
job

# more parameters can be set for the job
if(FALSE) { # suppress running it when building the package
    # current GREAT version is 4.0.4
    job = submitGreatJob(gr, genome = "hg19")
    job = submitGreatJob(gr, adv_upstream = 10, adv_downstream = 2, adv_span = 2000)
    job = submitGreatJob(gr, rule = "twoClosest", adv_twoDistance = 2000)
    job = submitGreatJob(gr, rule = "oneClosest", adv_oneDistance = 2000)
}