# Begin by activating the InterMineR library
# Syntax: library(LIBRARY_NAME_HERE)





# Syntax: listMines()["MINE _NAME_HERE"] 

# Print the value to make sure it's what we expect


# Syntax: initInterMine(mine=MINE_URL_HERE) or, if you're accessing a personal list, you'll also need your API token: 
#         initInterMine(mine=MINE_URL_HERE, "YOUR_TOKEN_HERE")



# First, we'll retrieve the widgets available from HumanMine and store it in a variable called `humanWidgets`. 
# This will tell us what types of enrichment are available.
# 
# Syntax: getWidgets(im)


# Print out the widgets so we can see what there is

humanWidgets


# We'll put the widget list `humanWidgets` in to a data frame, so it's easy to filter.
#
# Syntax: as.data.frame(LIST_NAME_HERE)

humanWidgetsDataFrame <- #put data frame code here

# Syntax: subset(YOU_DATA_FRAME, COLUMN1 == 'VALUE1' & COLUMN2 == 'VALUE2' )


# We're using the widget `go_enrichment_for_gene` against the list name 
# `PL_GenomicsEngland_GenePanel:Genetic_Epilepsy_Syndromes`
#
#Syntax: 
#
# GOEnrichmentResult <- doEnrichment(
#   im = YOUR_CHOSEN_INTERMINE_HERE
#   list = LIST_NAME_SAVE_ON_YOUR_CHOSEN_INTERMINE,
#   widget = "go_enrichment_for_gene" #  Or whichever widget you're using
#   organism = "Homo sapiens" # optional if results from more than one organism are retrieved
# )

GOEnrichmentResult <- doEnrichment(
    # Put enrichment details here
)

# Print the results 
GOEnrichmentResult

# We'll be using the setQuery method here
# 
# Syntax: 
# setQuery( 
#  select = c("VIEW.NAME1", "VIEW.NAME2", .... ),
#  where = setConstraints(
#    paths = c("CONSTRAINT.PATH.A", "CONSTRAINT.PATH.B"),
#    operators = c("CONSTRAINT.OPERATOR.A", "CONSTRAINT.OPERATOR.B"),
#    values = list("CONSTRAINT.VALUE.A","CONSTRAINT.VALUE.B")
#  )
#)
queryEpilepsy <- setQuery(
    # fill out your view and constraints here
)

# Run the query using the syntax `runQuery(im, QUERY_HERE)` 
queryEpilepsyResults <- # Add run query details here

# print the results:


# load GeneAnswers package. Remember the syntax is `library(LIBRARYNAME)`



# convert InterMineR Gene Ontology Enrichment analysis results to GeneAnswers object
# Syntax: 
# convertToGeneAnswers(
#  enrichmentResult = AN_INTERMINE_ENRICHMENT_RESULT,
#  geneInput = data.frame(GeneID = ORIGINAL_LIST_OF_IDS_WE_ENRICHED_ON, 
#                         stringsAsFactors = FALSE),
#  geneInputType = "TYPE_OF_IDS_PROVIDED" (e.g. "Gene.primaryIdentifier" or "Gene.symbol"),
#  categoryType = "GO" # could also be null for a user-provided annotation list
#)

queryEpilepsyForGeneAnswers <- convertToGeneAnswers(
    # Put the arguments here
    )


# Run the summary on the Gene Answers object we made in the previous code cell. 
# The variable name we used was `queryEpilepsyForGeneAnswers` 



geneAnswersConceptNet(queryEpilepsyForGeneAnswers, # This is the Gene Answers object we created
                      colorValueColumn=NULL,       # Default colours
                      centroidSize='correctedPvalue', # the column used to size graph nodes
                      output='fixed',              # don't spawn a new pop-up window 
                      geneSymbol = TRUE)           # Show gene symbols as labels

geneAnswersConceptRelation(queryEpilepsyForGeneAnswers, # This is the Gene Answers object we created
                           directed=TRUE,               # show arrows to indicate the direction of relationship
                           output="fixed",              # don't spawn a new pop-up window 
                           netMode='connection') 

buildNet(getGeneInput(queryEpilepsyForGeneAnswers)[,1], # queryEpilepsyForGeneAnswers
         idType='GeneInteraction', 
         layers=3,
         output="fixed",            # don't spawn a new pop-up window 
         filterGraphIDs=getGeneInput(queryEpilepsyForGeneAnswers)[,1],
         filterLayer=2,             # How many layers deep to look for interactors. Try changing it to 3! 
         netMode='connection')

geneAnswersHeatmap(queryEpilepsyForGeneAnswers, 
                   catTerm=TRUE, 
                   geneSymbol=TRUE,
                   cex.axis = 0.75)