Contents

1 Introduction

InterMine constitutes a powerful open source data warehouse system which integrates diverse biological data sets and provides a growing number of analysis tools, including enrichment analysis widgets (Smith et al. 2012; Kalderimis et al. 2014).

Specifically, the gene set enrichment tool looks for annotations to a set of genes that occur more than would be expected by chance, given a background population of annotations. The hypergeometric distribution is used to calculate a P-value for each annotation and a choice of correction methods for multiple testing (Bonferonni, Holm-Bonferonni and Benjamini Hochberg) are available (Smith et al. 2012; Kalderimis et al. 2014).

InterMine provides Gene Ontology enrichment statistics as well as enrichment statistics for other annotation types including protein domains, pathways, human diseases, mammalian phenotypes and publications. The default background population is all genes in the genome with the specified annotation type. However, the background population can be changed by specifying another list. More information can be found in the online documentation.

2 Perform enrichment analysis with InterMineR

Performing enrichment analysis with InterMineR is preceded by two steps:

  1. Retrieve the list of bioentities of interest (Genes, Proteins, SNPs, etc.) for which the analysis will be performed.

  2. Get the enrichment widget name which indicates the annotation types that you want to investigate for enrichment (e.g. Gene Ontology Terms, protein domains, KEGG and Reactome Pathways, human diseases, mammalian phenotypes and publications).

2.1 Retrieve list containing the genes of interest from InterMineR

In this example, enrichment analysis is performed using a list of genes which are associated with all forms of Diabetes according to OMIM. All query results and calculations are correct according to HumanMine release 5.1 (November 2018). Genomic coordinates and p-values are likely to change between database releases but the methods will remain the same.

The PL_DiabetesGenes dataset is included in the InterMineR package and can also be found online in HumanMine.

library(InterMineR)

Retrieve PL_DiabetesGenes data and get gene identifiers as HumanrMine Gene.symbol and Gene.primaryIdentifier values (ENTREZ IDs).

# get HumanMine instance
im.human = initInterMine(listMines()["HumanMine"])

# retrieve data model for HumanMine
hsa_model = getModel(im.human)

# all targets of HumanMine enrichment widgets possess InterMine ids
subset(hsa_model, type %in% c("Gene", "Protein", "SNP") & child_name == "id")
## [1] type       child_name child_type
## <0 rows> (or 0-length row.names)
# load human genes which are associated with Diabetes (retrieved from HumanMine)
data("PL_DiabetesGenes")

head(PL_DiabetesGenes, 3)
##   Gene.symbol                                 Gene.name
## 1       ABCC8 ATP binding cassette subfamily C member 8
## 2         ACE           angiotensin I converting enzyme
## 3        AKT2             AKT serine/threonine kinase 2
##   Gene.primaryIdentifier Gene.secondaryIdentifier Gene.length
## 1                   6833          ENSG00000006071       83961
## 2                   1636          ENSG00000159640       21320
## 3                    208          ENSG00000105221       55215
##   Gene.organism.name
## 1       Homo sapiens
## 2       Homo sapiens
## 3       Homo sapiens
# get Gene.symbol
hsa_gene_names = as.character(PL_DiabetesGenes$Gene.symbol)

head(hsa_gene_names, 3)
## [1] "ABCC8" "ACE"   "AKT2"
# get Gene.primaryIdentifiers (ENTREZ Gene identifiers)
hsa_gene_entrez = as.character(PL_DiabetesGenes$Gene.primaryIdentifier)

head(hsa_gene_entrez, 3)
## [1] "6833" "1636" "208"

2.2 Get enrichment widgets

After obtaining the Gene.id values of interest, we must determine the type of annotation for the enrichment analysis.

To retrieve all available widgets for a Mine we can use the getWidgets function.

# get all HumanMine widgets
human.widgets = as.data.frame(getWidgets(im.human))

human.widgets
##    startClassDisplay
## 1  primaryIdentifier
## 2               <NA>
## 3  primaryIdentifier
## 4  primaryIdentifier
## 5               <NA>
## 6  primaryIdentifier
## 7  primaryIdentifier
## 8  primaryIdentifier
## 9  primaryIdentifier
## 10              <NA>
##                                                 enrichIdentifier
## 1                         GWASResults.study.publication.pubMedId
## 2                                                           <NA>
## 3                   goAnnotation.ontologyTerm.parents.identifier
## 4                                          publications.pubMedId
## 5                                                           <NA>
## 6                                            pathways.identifier
## 7                         GWASResults.study.publication.pubMedId
## 8  proteins.proteinDomainRegions.proteinDomain.primaryIdentifier
## 9           proteinDomainRegions.proteinDomain.primaryIdentifier
## 10                                                          <NA>
##                                name
## 1         snp_gwas_study_enrichment
## 2   chromosome_distribution_for_snp
## 3            go_enrichment_for_gene
## 4            publication_enrichment
## 5  chromosome_distribution_for_gene
## 6                pathway_enrichment
## 7        snp_publication_enrichment
## 8      prot_dom_enrichment_for_gene
## 9   prot_dom_enrichment_for_protein
## 10                     interactions
##                                                                                                                                                                                                                       description
## 1                                                                                                                                                                                    GWAS studies enriched for SNPs in this list.
## 2  Actual: number of items in this list found on each chromosome.  Expected: given the total number of items on the chromosome and the number of items in this list, the number of items expected to be found on each chromosome.
## 3                                                                                                                                                                                       GO terms enriched for items in this list.
## 4                                                                                                                                                                                   Publications enriched for genes in this list.
## 5  Actual: number of items in this list found on each chromosome.  Expected: given the total number of items on the chromosome and the number of items in this list, the number of items expected to be found on each chromosome.
## 6                                                                                                                                                          Pathways enriched for genes in this list - data from KEGG and Reactome
## 7                                                                                                                                                                                    Publications enriched for SNPs in this list.
## 8                                                                                                                                                                                Protein Domains enriched for items in this list.
## 9                                                                                                                                                                                Protein Domains enriched for items in this list.
## 10                                                               Genes (from the list or not) that interact with genes in this list.  Counts may include the same interaction more than once if observed in multiple experiments.
##                                              enrich startClass
## 1                            GWASResults.study.name        SNP
## 2                                              <NA>        SNP
## 3            goAnnotation.ontologyTerm.parents.name       Gene
## 4                                publications.title       Gene
## 5                                              <NA>       Gene
## 6                                     pathways.name       Gene
## 7               GWASResults.study.publication.title        SNP
## 8  proteins.proteinDomainRegions.proteinDomain.name       Gene
## 9           proteinDomainRegions.proteinDomain.name    Protein
## 10                                             <NA>       <NA>
##                              title targets widgetType   chartType
## 1   GWAS study Enrichment for SNPs     SNP enrichment        <NA>
## 2          Chromosome Distribution     SNP      chart ColumnChart
## 3         Gene Ontology Enrichment    Gene enrichment        <NA>
## 4           Publication Enrichment    Gene enrichment        <NA>
## 5          Chromosome Distribution    Gene      chart ColumnChart
## 6               Pathway Enrichment    Gene enrichment        <NA>
## 7  Publication Enrichment for SNPs     SNP enrichment        <NA>
## 8        Protein Domain Enrichment    Gene enrichment        <NA>
## 9        Protein Domain Enrichment Protein enrichment        <NA>
## 10                    Interactions    Gene      table        <NA>
##                                                     filters
## 1                                                      <NA>
## 2                                      organism.name=[list]
## 3  biological_process,cellular_component,molecular_function
## 4                                                      <NA>
## 5                                      organism.name=[list]
## 6              All,KEGG pathways data set,Reactome data set
## 7                                                      <NA>
## 8                                                      <NA>
## 9                                                      <NA>
## 10                                                     <NA>
##                labels
## 1                <NA>
## 2  Chromosome & Count
## 3                <NA>
## 4                <NA>
## 5  Chromosome & Count
## 6                <NA>
## 7                <NA>
## 8                <NA>
## 9                <NA>
## 10               <NA>

HumanMine provides enrichment for genes, proteins and SNPs, but here we are interested only in the gene-related, enrichment widgets.

# get enrichment, gene-related widgets for HumanMine
subset(human.widgets, widgetType == 'enrichment' & targets == "Gene")
##   startClassDisplay
## 3 primaryIdentifier
## 4 primaryIdentifier
## 6 primaryIdentifier
## 8 primaryIdentifier
##                                                enrichIdentifier
## 3                  goAnnotation.ontologyTerm.parents.identifier
## 4                                         publications.pubMedId
## 6                                           pathways.identifier
## 8 proteins.proteinDomainRegions.proteinDomain.primaryIdentifier
##                           name
## 3       go_enrichment_for_gene
## 4       publication_enrichment
## 6           pathway_enrichment
## 8 prot_dom_enrichment_for_gene
##                                                              description
## 3                              GO terms enriched for items in this list.
## 4                          Publications enriched for genes in this list.
## 6 Pathways enriched for genes in this list - data from KEGG and Reactome
## 8                       Protein Domains enriched for items in this list.
##                                             enrich startClass
## 3           goAnnotation.ontologyTerm.parents.name       Gene
## 4                               publications.title       Gene
## 6                                    pathways.name       Gene
## 8 proteins.proteinDomainRegions.proteinDomain.name       Gene
##                       title targets widgetType chartType
## 3  Gene Ontology Enrichment    Gene enrichment      <NA>
## 4    Publication Enrichment    Gene enrichment      <NA>
## 6        Pathway Enrichment    Gene enrichment      <NA>
## 8 Protein Domain Enrichment    Gene enrichment      <NA>
##                                                    filters labels
## 3 biological_process,cellular_component,molecular_function   <NA>
## 4                                                     <NA>   <NA>
## 6             All,KEGG pathways data set,Reactome data set   <NA>
## 8                                                     <NA>   <NA>

The column names provides the character strings that can be passed as arguments to the doEnrichment function and thus define the type of enrichment analysis.

2.3 Perform enrichment analysis with InterMineR

We will perform Gene Ontology Enrichment analysis for the genes in our list using the Gene.id values and the ‘go_enrichment_for_gene’ widget.

# Assign directly the doEnrichment.string from getGeneIds function to the ids argument of doEnrichment to perform the analysis

# Perform enrichment analysis
GO_enrichResult = doEnrichment(
  im = im.human,
  ids = hsa_gene_entrez,
  widget = "go_enrichment_for_gene"
  # organism = "Homo sapiens" # optional if results from more than one organism are retrieved
)

As mentioned above ‘PL_DiabetesGenes’ constitutes a genelist which already exists in HumanMine. Therefore, the enrichment analysis could also be performed by passing the name of the list to the genelist argument, with the same results:

# Perform enrichment analysis with genelist name instead of ids
GO_enrichResult = doEnrichment(
  im = im.human,
  genelist = "PL_DiabetesGenes",
  # ids = hsa_gene_entrez,
  widget = "go_enrichment_for_gene"
)

doEnrichment function returns a list which contains:

  • a data.frame with the results of the enrichment analysis which was performed in the defined InterMine platform. p-values are given after applying the correction algorithm. Count and populationAnnotationCount columns contain the number of genes that belong to each GO term in the list and in the background population respectively.
# data.frame containing the results of the enrichment analysis
head(GO_enrichResult$data)
##   identifier                             description                 pValue
## 1 GO:0030072               peptide hormone secretion   9.81265853781751E-18
## 2 GO:0046879                       hormone secretion 1.5986231795125633E-17
## 3 GO:0009914                       hormone transport 2.2282615946760607E-17
## 4 GO:0030073                       insulin secretion  3.210684287248625E-15
## 5 GO:0090276 regulation of peptide hormone secretion  3.210684287248625E-15
## 6 GO:0023061                          signal release  4.101837256141138E-15
##   count populationAnnotationCount
## 1    20                       245
## 2    21                       307
## 3    21                       318
## 4    17                       204
## 5    17                       204
## 6    21                       423
dim(GO_enrichResult$data)
## [1] 605   5
  • the size of the reference background population (populationCount)
GO_enrichResult$populationCount
## [1] 16550
  • the number of input features that were not included in the enrichment analysis (notAnalyzed)
GO_enrichResult$notAnalysed
## [1] 6
  • the name and url of the Mine (im)
GO_enrichResult$im
## $mine
##                            HumanMine 
## "http://www.humanmine.org/humanmine" 
## 
## $token
## [1] ""
  • the rest of the parameters used to perform the analysis (parameters)
GO_enrichResult$parameters
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               ids 
## "1176615,1273304,1167277,1130443,1161589,1300326,1206165,1213742,1198102,1192997,1100314,1065127,1145376,1040643,1133124,1033908,1067150,1264712,1243570,1245338,1052014,1287859,1304691,1063722,1283605,1121747,1091639,1190601,1104281,1177734,1086981,1042053,1127304,1084493,1037546,1255362,1045572,1037142,1218365,1053251,1273987,1228058,1246465,1130482,1202061,1010690,1092985,1077614,1155494,1167204,1168443,1092852,1121081,1145392,1236876,1276032,1288428,1185812,1241593,1047002,1122939,1114206,1017442,1125484,1238606,1123997,1044162,1085573" 
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            widget 
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          "go_enrichment_for_gene" 
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              maxp 
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            "0.05" 
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        correction 
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              "Benjamini Hochberg"

2.4 Apply filters on enrichment analysis

Some of the enrichment widgets contain filters which, when applied, limit the annotation types of the enrichment analysis.

In our example, if we want to retrieve only the enriched GO terms in our list of genes that are related to molecular function, we will use the appropriate filter:

# get available filter values for Gene Ontology Enrichment widget
as.character(subset(human.widgets, name == "go_enrichment_for_gene")$filters)
## [1] "biological_process,cellular_component,molecular_function"
# Perform enrichment analysis for GO molecular function terms
GO_MF_enrichResult = doEnrichment(
  im = im.human,
  ids = hsa_gene_entrez,
  widget = "go_enrichment_for_gene",
  filter = "molecular_function")

head(GO_MF_enrichResult$data)
##   identifier                                                   description
## 1 GO:0005158                                      insulin receptor binding
## 2 GO:0005102                                    signaling receptor binding
## 3 GO:0000976 transcription regulatory region sequence-specific DNA binding
## 4 GO:0001067                        regulatory region nucleic acid binding
## 5 GO:1990837                 sequence-specific double-stranded DNA binding
## 6 GO:0005159                   insulin-like growth factor receptor binding
##                 pValue count populationAnnotationCount
## 1 0.004245591773598556     4                        21
## 2   0.0131638206592664    18                      1537
## 3 0.016014287998993088    12                       765
## 4  0.01618448730649538    13                       881
## 5 0.018939590980716323    12                       805
## 6 0.019256729825164885     3                        16
dim(GO_MF_enrichResult$data)
## [1] 17  5

2.5 Apply multiple test correction

To reduce the probability of false positive errors, three different multiple correction algorithms can be applied to the results of the enrichment analysis:

  • Benjamini Hochberg (default)
  • Holm-Bonferroni
  • Bonferroni

The application of one of these algorithms changes the p-values and determines the number of the results which will be returned from the analysis:

# Perform enrichment analysis for Protein Domains in list of genes
PD_FDR_enrichResult = doEnrichment(
  im = im.human,
  ids = hsa_gene_entrez,
  widget = "prot_dom_enrichment_for_gene",
  correction = "Benjamini Hochberg"
) 

head(PD_FDR_enrichResult$data)
## NULL
# Perform enrichment analysis for Protein Domains in list of genes
# but without a correction algoritm this time
PD_None_enrichResult = doEnrichment(
  im = im.human,
  ids = hsa_gene_entrez,
  widget = "prot_dom_enrichment_for_gene",
  correction = "None"
)

head(PD_None_enrichResult$data)
##   identifier                                                description
## 1  IPR006897      Hepatocyte nuclear factor 1, beta isoform, C-terminal
## 2  IPR023219 Hepatocyte nuclear factor 1, N-terminal domain superfamily
## 3  IPR006899                    Hepatocyte nuclear factor 1, N-terminal
## 4  IPR006020                                              PTB/PI domain
## 5  IPR000734                              Triacylglycerol lipase family
## 6  IPR013818                                        Lipase/vitellogenin
##                  pValue count populationAnnotationCount
## 1 1.1119293663751075E-5     2                         2
## 2 1.1119293663751075E-5     2                         2
## 3  3.328549670935905E-5     2                         3
## 4   3.27206451574323E-4     3                        40
## 5   4.91754736757544E-4     2                        10
## 6   4.91754736757544E-4     2                        10

3 Visualization of InterMineR Gene Ontology Enrichment analysis results

3.1 Convert InterMineR enrichment analysis results to a GeneAnswer-class object

In order to visualize the InterMineR enrichment analysis results, we will use the function convertToGeneAnswers. This function was created to facilitate the visualization of doEnrichment function results by converting them into a GeneAnswers-class object.

This way we can utilize the functions of the package GeneAnswers to visualize the results of the enrichment analysis and the relations between the features (e.g. genes) and/or the annoatation categories (e.g. GO terms) (Feng et al. 2010, 2012; Huang et al. 2014).

# load GeneAnswers package
library(GeneAnswers)

# convert InterMineR Gene Ontology Enrichment analysis results to GeneAnswers object
geneanswer_object = convertToGeneAnswers(
  
  # assign with doEnrichment result:
  enrichmentResult = GO_enrichResult,
  
  # assign with list of genes:
  geneInput = data.frame(GeneID = as.character(hsa_gene_entrez), 
                         stringsAsFactors = FALSE),
  
  # assign with the type of gene identifier
  # in our example we use Gene.primaryIdentifier values (ENTREZ IDs):
  geneInputType = "Gene.primaryIdentifier",
  
  # assign with Bioconductor annotation package:
  annLib = 'org.Hs.eg.db',
  
  # assign with annotation category type
  # in our example we use Gene Ontology (GO) terms:
  categoryType = "GO"
  
  #optional define manually if 'enrichIdentifier' is missing from getWidgets:
  #enrichCategoryChildName = "Gene.goAnnotation.ontologyTerm.parents.identifier"
)

class(geneanswer_object)
## [1] "GeneAnswers"
## attr(,"package")
## [1] "GeneAnswers"
summary(geneanswer_object)
## This GeneAnswers instance was build from GO based on hyperG test.
## Statistical information of 605 categories with p value less than 0.05 are reported. Other categories are considered as nonsignificant.
## There are 605 categories related to the given 68 genes
## 
## Summary of GeneAnswers instance information:
## 
## Slot: geneInput
##   GeneID
## 1   6833
## 2   1636
## 3    208
## 4  26060
## 5    359
## 6    551
## ......
## 
## Slot: testType
## [1] "hyperG"
## 
## Slot: pvalueT
## [1] 0.05
## 
## Slot: genesInCategory
## $`GO:0030072`
##  [1] "6833"   "640"    "11132"  "2645"   "3077"   "6928"   "3172"   "3557"  
##  [9] "3569"   "3630"   "3667"   "8660"   "3710"   "3767"   "4544"   "4760"  
## [17] "3651"   "6514"   "169026" "6934"  
## 
## $`GO:0046879`
##  [1] "6833"   "640"    "11132"  "2645"   "3077"   "6928"   "3172"   "3557"  
##  [9] "3569"   "3630"   "3667"   "8660"   "3710"   "3767"   "4544"   "4760"  
## [17] "3651"   "56729"  "6514"   "169026" "6934"  
## 
## $`GO:0009914`
##  [1] "6833"   "640"    "11132"  "2645"   "3077"   "6928"   "3172"   "3557"  
##  [9] "3569"   "3630"   "3667"   "8660"   "3710"   "3767"   "4544"   "4760"  
## [17] "3651"   "56729"  "6514"   "169026" "6934"  
## 
## $`GO:0030073`
##  [1] "6833"   "640"    "11132"  "2645"   "6928"   "3172"   "3557"   "3667"  
##  [9] "8660"   "3710"   "3767"   "4544"   "4760"   "3651"   "6514"   "169026"
## [17] "6934"  
## 
## $`GO:0090276`
##  [1] "6833"   "640"    "11132"  "2645"   "3077"   "3172"   "3630"   "3667"  
##  [9] "8660"   "3710"   "3767"   "4544"   "4760"   "3651"   "6514"   "169026"
## [17] "6934"  
## 
## $`GO:0023061`
##  [1] "6833"   "640"    "11132"  "2645"   "3077"   "6928"   "3172"   "3557"  
##  [9] "3569"   "3630"   "3667"   "8660"   "3710"   "3767"   "4544"   "4760"  
## [17] "3651"   "56729"  "6514"   "169026" "6934"  
## 
## ......
## 
## Slot: geneExprProfile
## NULL
## 
## Slot: annLib
## [1] "org.Hs.eg.db"
## 
## Slot: categoryType
## [1] "GO"
## 
## Slot: enrichmentInfo
##            genes in Category percent in the observed List
## GO:0030072                20                    0.2941176
## GO:0046879                21                    0.3088235
## GO:0009914                21                    0.3088235
## GO:0030073                17                    0.2500000
## GO:0090276                17                    0.2500000
## GO:0023061                21                    0.3088235
##            percent in the genome fold of overrepresents odds ratio
## GO:0030072            0.01480363               19.86795   27.72959
## GO:0046879            0.01854985               16.64830   23.64010
## GO:0009914            0.01921450               16.07242   22.80690
## GO:0030073            0.01232628               20.28186   26.70915
## GO:0090276            0.01232628               20.28186   26.70915
## GO:0023061            0.02555891               12.08281   17.03471
##                 p value  fdr p value
## GO:0030072 9.812659e-18 9.812659e-18
## GO:0046879 1.598623e-17 1.598623e-17
## GO:0009914 2.228262e-17 2.228262e-17
## GO:0030073 3.210684e-15 3.210684e-15
## GO:0090276 3.210684e-15 3.210684e-15
## GO:0023061 4.101837e-15 4.101837e-15
## ......
#slotNames(geneanswer_object)

GeneAnswers is package designed for the enrichment analysis and visualization of gene lists. It is worth mentioning that the InterMineR filters for the widgets of Gene Ontology and Pathway Enrichment:

as.character(
subset(human.widgets,
       title %in% c("Gene Ontology Enrichment",
                    "Pathway Enrichment"))$filters
)
## [1] "biological_process,cellular_component,molecular_function"
## [2] "All,KEGG pathways data set,Reactome data set"

match the following available values:

InterMineR widget name InterMineR filter convertToGeneAnswers categoryType
go_enrichment_for_gene biological_process GO.BP
go_enrichment_for_gene cellular_component GO.CC
go_enrichment_for_gene molecular_function GO.MF
pathway_enrichment KEGG pathways data set KEGG
pathway_enrichment Reactome data set REACTOME.PATH

for the categoryType argument of the geneAnswerBuilder function, and can be assigned accordingly in the categoryType argument of the convertToGeneAnswers, therby facilitating the conversion of InterMineR Gene Ontology and Pathway Enrichment analysis results to GeneAnswers-class objects.

# convert to GeneAnswers results for GO terms associated with molecular function
geneanswer_MF_object = convertToGeneAnswers(
  enrichmentResult = GO_MF_enrichResult,
  geneInput = data.frame(GeneID = as.character(hsa_gene_entrez), 
                         stringsAsFactors = FALSE),
  geneInputType = "Gene.primaryIdentifier",
  annLib = 'org.Hs.eg.db',
  categoryType = "GO.MF"
  #enrichCategoryChildName = "Gene.goAnnotation.ontologyTerm.parents.identifier"
)

class(geneanswer_MF_object)
## [1] "GeneAnswers"
## attr(,"package")
## [1] "GeneAnswers"
summary(geneanswer_MF_object)
## This GeneAnswers instance was build from GO.MF based on hyperG test.
## Statistical information of 17 categories with p value less than 0.05 are reported. Other categories are considered as nonsignificant.
## There are 17 categories related to the given 68 genes
## 
## Summary of GeneAnswers instance information:
## 
## Slot: geneInput
##   GeneID
## 1   6833
## 2   1636
## 3    208
## 4  26060
## 5    359
## 6    551
## ......
## 
## Slot: testType
## [1] "hyperG"
## 
## Slot: pvalueT
## [1] 0.05
## 
## Slot: genesInCategory
## $`GO:0005158`
## [1] "3630" "3667" "8660" "5770"
## 
## $`GO:0005102`
##  [1] "1636" "551"  "640"  "2056" "3077" "3082" "3159" "3172" "3557" "3569"
## [11] "3630" "3643" "3667" "8660" "5468" "5770" "6934" "7422"
## 
## $`GO:0000976`
##  [1] "50943" "3159"  "6927"  "6928"  "3172"  "4760"  "5078"  "3651"  "5325" 
## [10] "5468"  "6925"  "6934" 
## 
## $`GO:0001067`
##  [1] "50943" "3159"  "6927"  "6928"  "3172"  "8462"  "4760"  "5078"  "3651" 
## [10] "5325"  "5468"  "6925"  "6934" 
## 
## $`GO:1990837`
##  [1] "50943" "3159"  "6927"  "6928"  "3172"  "4760"  "5078"  "3651"  "5325" 
## [10] "5468"  "6925"  "6934" 
## 
## $`GO:0005159`
## [1] "3630" "3643" "3667"
## 
## ......
## 
## Slot: geneExprProfile
## NULL
## 
## Slot: annLib
## [1] "org.Hs.eg.db"
## 
## Slot: categoryType
## [1] "GO.MF"
## 
## Slot: enrichmentInfo
##            genes in Category percent in the observed List
## GO:0005158                 4                   0.05882353
## GO:0005102                18                   0.26470588
## GO:0000976                12                   0.17647059
## GO:0001067                13                   0.19117647
## GO:1990837                12                   0.17647059
## GO:0005159                 3                   0.04411765
##            percent in the genome fold of overrepresents odds ratio
## GO:0005158          0.0012825211              45.865546  48.669643
## GO:0005102          0.0938683278               2.819970   3.475159
## GO:0000976          0.0467204104               3.777163   4.372269
## GO:0001067          0.0538048125               3.553148   4.156620
## GO:1990837          0.0491633077               3.589478   4.144366
## GO:0005159          0.0009771589              45.148897  47.186538
##                p value fdr p value
## GO:0005158 0.004245592 0.004245592
## GO:0005102 0.013163821 0.013163821
## GO:0000976 0.016014288 0.016014288
## GO:0001067 0.016184487 0.016184487
## GO:1990837 0.018939591 0.018939591
## GO:0005159 0.019256730 0.019256730
## ......
#slotNames(geneanswer_MF_object)

Briefly, in the following sections we present how to use several functions of the GeneAnswers package to visualize the results of the Gene Ontology Enrichment analysis on the PL_DiabetesGenes gene list.

For more information about the usage of GeneAnswers package, the user should look at the respective documentation.

3.2 PieChart and BarPlot graphs

# Make GeneAnswers Instance readable
geneanswer_object_readable = geneAnswersReadable(geneanswer_object)
# GeneAnswers pieChart
geneAnswersChartPlots(geneanswer_object_readable, 
                      chartType='pieChart',
                      sortBy = 'correctedPvalue',
                      top = 3)

Figure 1: Pie chart of the top three GO terms with the smallest FDR-corrected p-values.

# GeneAnswers barplot
geneAnswersChartPlots(geneanswer_object_readable, 
                      chartType='barPlot',
                      sortBy = 'correctedPvalue',
                      top = 5)