Networks can be used for two main purposes but often go hand in hand.

• Analysis
• Topological properties
• Hubs and subnetworks
  
• Cluster, classify, and diffuse
  
• Data integration
• Visualization
• Data overlays
• Layouts and animation
• Exploratory analysis
• Context and interpretation

Networks offer us a useful way to represent our biological data. But how do we seamlessly translate our data from R into Cytoscape?

In order to create networks in Cytoscape from R you need:

• RCy3 - a biocondutor package

if(!"RCy3" %in% installed.packages()){
                        install.packages("BiocManager")
                        BiocManager::install("RCy3")
                    }
                    library(RCy3)
                    

• Cytoscape - Download and install Cytoscape 3.6.1. or higher. Java 9 in not supported. Please make sure that Java 8 is installed.
  • Install additional cytoscape apps that will be used in this workshop. If using cytoscape 3.6.1 or older the apps need to manually installed through the app manager in Cytoscape or through your web browser. (click on the method to see detailed instructions)
    • Functional Enrichment Collection -a collection of apps to retrieve networks and pathways, integrate and explore the data, perform functional enrichment analysis, and interpret and display your results.
    • EnrichmentMap Pipeline Collection - a collection of apps including EnrichmentMap(Merico et al.), AutoAnnotate(Kucera et al.), WordCloud(Oesper et al.) and clusterMaker2(Morris et al.) used to visualize and analysis enrichment results.

If you are using Cytoscape 3.7 or higher (Cytoscape 3.7 will be released in August 2018) then apps can be installed directly from R.

#list of cytoscape apps to install
installation_responses <- c()

#list of app to install
cyto_app_toinstall <- c("clustermaker2", "enrichmentmap", "autoannotate", "wordcloud", "stringapp", "aMatReader")

cytoscape_version <- unlist(strsplit( cytoscapeVersionInfo()['cytoscapeVersion'],split = "\\."))
if(length(cytoscape_version) == 3 && as.numeric(cytoscape_version[1]>=3) 
   && as.numeric(cytoscape_version[2]>=7)){
  for(i in 1:length(cyto_app_toinstall)){
    #check to see if the app is installed.  Only install it if it hasn't been installed
    if(!grep(commandsGET(paste("apps status app=\"", cyto_app_toinstall[i],"\"", sep="")), 
             pattern = "status: Installed")){
      installation_response <-commandsGET(paste("apps install app=\"", 
                                                cyto_app_toinstall[i],"\"", sep=""))
      installation_responses <- c(installation_responses,installation_response)
    } else{
      installation_responses <- c(installation_responses,"already installed")
    }
  }
  installation_summary <- data.frame(name = cyto_app_toinstall, 
                                     status = installation_responses)

  knitr::kable(list(installation_summary),
  booktabs = TRUE, caption = 'A Summary of automated app installation'
)
}

Make sure that Cytoscape is open

 cytoscapePing ()

Confirm that Cytoscape is installed and opened

 cytoscapeVersionInfo ()

Depending on what apps you have installed there is different functionality available.

To see all the functions available in RCy3 package

help(package=RCy3)
                        

cyrestAPI()  # CyREST API
                        

commandsAPI()  # Commands API
                        

To get information about an individual command from the R environment you can also use the commandsHelp function. Simply specify what command you would like to get information on by adding its name to the command. For example “commandsHelp("help string”)“

commandsHelp("help")
                        

Create a Cytoscape network from some basic R objects

nodes <- data.frame(id=c("node 0","node 1","node 2","node 3"),
                                   group=c("A","A","B","B"), # categorical strings
                                   score=as.integer(c(20,10,15,5)), # integers
                                   stringsAsFactors=FALSE)
                        edges <- data.frame(source=c("node 0","node 0","node 0","node 2"),
                                   target=c("node 1","node 2","node 3","node 3"),
                                   interaction=c("inhibits","interacts","activates","interacts"),  # optional
                                   weight=c(5.1,3.0,5.2,9.9), # numeric
                                   stringsAsFactors=FALSE)
                        

• Nodes table:

id	group	score
node 0	A	20
node 1	A	10
node 2	B	15
node 3	B	5

• Edges table:

source	target	interaction	weight
node 0	node 1	inhibits	5.1
node 0	node 2	interacts	3.0
node 0	node 3	activates	5.2
node 2	node 3	interacts	9.9

createNetworkFromDataFrames(nodes,edges, title="my first network", collection="DataFrame Example")
                        

Remember. All networks we make are created in Cytoscape so get an image of the resulting network and include it in your current analysis if desired.

initial_network_png_file_name <- file.path(getwd(),"230_Isserlin_RCy3_intro", "images","initial_example_network.png")
                        

if(file.exists(initial_network_png_file_name)){
                          #cytoscape hangs waiting for user response if file already exists.  Remove it first
                          file.remove(initial_network_png_file_name)
                          } 

                        #export the network
                        exportImage(initial_network_png_file_name, type = "png")
                        

We downloaded gene expression data from the Ovarian Serous Cystadenocarcinoma project of The Cancer Genome Atlas (TCGA)(International Genome et al.), http://cancergenome.nih.gov via the Genomic Data Commons (GDC) portal(Grossman et al.) on 2017-06-14 using TCGABiolinks R package(Colaprico et al.).

• 300 samples available as RNA-seq data
• 79 classified as Immunoreactive, 72 classified as Mesenchymal, 69 classified as Differentiated, and 80 classified as Proliferative samples
• RNA-seq read counts were converted to CPM values and genes with CPM > 1 in at least 50 of the samples are retained for further study
• The data was normalized and differential expression was calculated for each cancer class relative to the rest of the samples.

• How do I represent my data as a network?
• Unfortunately, there is not a simple answer.
  
  

It depends on your biological question!

• Example use cases:
1. Omics data - I have a ----------- fill in the blank (microarray, RNASeq, Proteomics, ATACseq, MicroRNA, GWAS …) dataset. I have normalized and scored my data. How do I overlay my data on existing interaction data?
2. Coexpression data -I have a dataset that represents relationships. How do I represent it as a network.
3. Omics data - I have a -----------fill in the blank (microarray, RNASeq, Proteomics, ATACseq, MicroRNA, GWAS …) dataset. I have normalized and scored my data. I have run my data through a functional enrichment tool and now have a set of enriched terms associated with my dataset. How do I represent my functional enrichments as a network?

Omics data - I have a -----------fill in the blank (microarray, RNASeq, Proteomics, ATACseq, MicroRNA, GWAS …) dataset. I have normalized and scored my data. How do I overlay my data on existing interaction data?

There are endless amounts of databases storing interaction data.

Thankfully we don't have to query each independent ally. In addition to many specialized (for example, for specific molecules, interaction type, or species) interaction databases there are also databases that collate these databases to create a broad resource that is easier to use. For example:

• StringApp - is a protein - protein and protein- chemical database that imports data from String(Szklarczyk et al.), [STITCH] into a unified, queriable database.
• PSICQUIC(Aranda et al.) - a REST-ful service that is the responsibility of the database provider to set up and maintain. PSICQUIC is an additional interface that allows users to search all available databases (that support this REST-ful service). The databases are required to represent their interaction data in Proteomic Standards Initiative - molecular interaction (PSI-MI) format. To see a list of all the available data source see here
• nDex(Pratt et al.) - a network data exchange repository.
• GeneMANIA(Mostafavi et al.) - contains multiple networks (shared domains, physical interactions, pathways, predicted, co-expression, genetic interactions and co-localized network). Given a set of genes GeneMANIA selects and weights networks that optimize the connectivity between the query genes. GeneMANIA will also return additional genes that are highly related to your query set.
• PathwayCommons - (access the data through the CyPath2App) is a pathway and interaction data source. Data is collated from a large set of resources (list here ) and stored in the BioPAX(Demir et al.) format. BioPAX is a data standard that allows for detailed representation of pathway mechanistic details as opposed to collapsing it to the set of interactions between molecules. BioPAX pathways from Pathway commons can also be loaded directly into R using the PaxToolsR(Luna et al.) Bioconductor package.
  

Layout the network

layoutNetwork('force-directed')

Check what other layout algorithms are available to try out

getLayoutNames()

Get the parameters for a specific layout

getLayoutPropertyNames(layout.name='force-directed')

Re-layout the network using the force directed layout but specify some of the parameters

layoutNetwork('force-directed defaultSpringCoefficient=0.0000008 defaultSpringLength=70')

Get a screenshot of the re-laid out network

relayout_string_network_png_file_name <- file.path(getwd(),"230_Isserlin_RCy3_intro", 
"images","relayout_string_network.png")

if(file.exists(relayout_string_network_png_file_name)){
  #cytoscape hangs waiting for user response if file already exists.  Remove it first
  response<- file.remove(relayout_string_network_png_file_name)
  } 
response <- exportImage(relayout_string_network_png_file_name, type = "png")

To do this we will be using the loadTableData function from RCy3. It is important to make sure that that your identifiers types match up. You can check what is used by String by pulling in the column names of the node attribute table.

getTableColumnNames('node')

If you are unsure of what each column is and want to further verify the column to use you can also pull in the entire node attribute table.

node_attribute_table_topmesen <- getTableColumns(table="node")
head(node_attribute_table_topmesen[,3:7])

The column “display name” contains HGNC gene names which are also found in our Ovarian Cancer dataset.

To import our expression data we will match our dataset to the “display name” node attribute.

?loadTableData

loadTableData(RNASeq_gene_scores,table.key.column = "display name",
data.key.column = "Name")  #default data.frame key is row.names

Modify the visual style Create your own visual style to visualize your expression data on the String network.

Start with a default style

style.name = "MesenchymalStyle"
defaults.list <- list(NODE_SHAPE="ellipse",
                 NODE_SIZE=60,
                 NODE_FILL_COLOR="#AAAAAA",
                 EDGE_TRANSPARENCY=120)
# p for passthrough; nothing else needed
node.label.map <- mapVisualProperty('node label','display name','p') 
createVisualStyle(style.name, defaults.list, list(node.label.map))
setVisualStyle(style.name=style.name)

Update your created style with a mapping for the Mesenchymal logFC expression. The first step is to grab the column data from Cytoscape (we can reuse the node_attribute table concept from above but we have to call the function again as we have since added our expression data) and pull out the min and max to define our data mapping range of values.

Note: you could define the min and max based on the entire dataset or just the subset that is represented in Cytoscape currently. The two methods will give you different results. If you intend on comparing different networks created with the same dataset then it is best to calculate the min and max from the entire dataset as opposed to a subset.

min.mesen.logfc = min(RNASeq_gene_scores$logFC.mesen,na.rm=TRUE)
max.mesen.logfc = max(RNASeq_gene_scores$logFC.mesen,na.rm=TRUE)
data.values = c(min.mesen.logfc,0,max.mesen.logfc)

Remember, String includes your query proteins as well as other proteins that associate with your query proteins (including the strongest connection first). Not all of the proteins in this network are your top hits. How can we visualize which proteins are our top Mesenchymal hits?

Add a different border color or change the node shape for our top hits.

getNodeShapes()

#select the Nodes of interest
#selectNode(nodes = top_mesenchymal_genes$Name, by.col="display name")
setNodeShapeBypass(node.names = top_mesenchymal_genes$Name, new.shapes = "TRIANGLE")

Change the size of the node to be correlated with the Mesenchymal p-value.

setNodeSizeMapping(table.column = 'LR.mesen', 
                   table.column.values = c(min(RNASeq_gene_scores$LR.mesen), 
                                           mean(RNASeq_gene_scores$LR.mesen), 
                                           max(RNASeq_gene_scores$LR.mesen)), 
                   sizes = c(30, 60, 150),mapping.type = "c", style.name = style.name)

Get a screenshot of the resulting network

mesen_string_network_png_file_name <- file.path(getwd(),"230_Isserlin_RCy3_intro", "images","mesen_string_network.png")

if(file.exists(mesen_string_network_png_file_name)){
  #cytoscape hangs waiting for user response if file already exists.  Remove it first
  response<- file.remove(mesen_string_network_png_file_name)
  } 
response <- exportImage(mesen_string_network_png_file_name, type = "png")

Instead of querying existing resources look for correlations in your own dataset to find out which genes have similar expression. There are many tools that can analyze your data for correlation. A popular tool is Weighted Gene Correlation Network Analysis (WGCNA)(Langfelder et al.) which takes expression data and calculates functional modules. As a simple example we can transform our expression dataset into a correlation matrix.

Using the Cytoscape App, aMatReader(Settle et al.), we transform our adjacency matrix into an interaction network. First we filter the correlation matrix to contain only the strongest connections (for example, only correlations greater than 0.9).

amat_url <- "aMatReader/v1/import"
amat_params = list(files = list(correlation_filename),
                   delimiter = "TAB",
                   undirected = FALSE,
                   ignoreZeros = TRUE,
                   interactionName = "correlated with",
                   rowNames = FALSE
                   )

response <- cyrestPOST(operation = amat_url, body = amat_params, 
base.url = "http://localhost:1234")

current_network_id <- response$data["suid"]

Relayout

#relayout network
layoutNetwork('cose',
              network = as.numeric(current_network_id))

Rename

renameNetwork(title = "Coexpression_network_pear0_9",
              network = as.numeric(current_network_id))

Modify the visualization to see where each genes is predominantly expressed. Look at the 4 different p-values associated with each gene and color the nodes with the type associated with the lowest FDR.

Load in the scoring data. Specify the cancer type where the genes has the lowest FDR value.

nodes_in_network <- rownames(RNAseq_correlation_matrix)

#add an additional column to the gene scores table to indicate in which samples
# the gene is significant
node_class <- vector(length = length(nodes_in_network),mode = "character")
for(i in 1:length(nodes_in_network)){
  current_row <- which(RNASeq_gene_scores$Name == nodes_in_network[i])
  min_pvalue <- min(RNASeq_gene_scores[current_row,
                                       grep(colnames(RNASeq_gene_scores), pattern = "FDR")])
  if(RNASeq_gene_scores$FDR.mesen[current_row] <=min_pvalue){
    node_class[i] <- paste(node_class[i],"mesen",sep = " ")
  }
  if(RNASeq_gene_scores$FDR.diff[current_row] <=min_pvalue){
    node_class[i] <- paste(node_class[i],"diff",sep = " ")
  }
  if(RNASeq_gene_scores$FDR.prolif[current_row] <=min_pvalue){
    node_class[i] <- paste(node_class[i],"prolif",sep = " ")
  }
  if(RNASeq_gene_scores$FDR.immuno[current_row] <=min_pvalue){
    node_class[i] <- paste(node_class[i],"immuno",sep = " ")
  }
}
node_class <- trimws(node_class)
node_class_df <-data.frame(name=nodes_in_network, node_class,stringsAsFactors = FALSE)

head(node_class_df)

    name node_class
1  ABCA6      mesen
2  ABCA8      mesen
3   ABI3     immuno
4   ACAN     prolif
5  ACAP1     immuno
6 ADAM12      mesen

Map the new node attribute and the all the gene scores to the network.

#default data.frame key is row.names
loadTableData(RNASeq_gene_scores,table.key.column = "name",data.key.column = "Name")  

#default data.frame key is row.names
loadTableData(node_class_df,table.key.column = "name",data.key.column = "name")  

Create a color mapping for the different cancer types.

#create a new mapping with the different types
unique_types <- sort(unique(node_class))

coul = brewer.pal(4, "Set1") 

# I can add more tones to this palette :
coul = colorRampPalette(coul)(length(unique_types))

setNodeColorMapping(table.column = "node_class",table.column.values = unique_types,
                    colors = coul,mapping.type = "d")

correlation_network_png_file_name <- file.path(getwd(),"230_Isserlin_RCy3_intro", "images","correlation_network.png")

if(file.exists(correlation_network_png_file_name)){
  #cytoscape hangs waiting for user response if file already exists.  Remove it first
  file.remove(correlation_network_png_file_name)
  } 

#export the network
exportImage(correlation_network_png_file_name, type = "png")

#make sure it is set to the right network
  setCurrentNetwork(network = getNetworkName(suid=as.numeric(current_network_id)))

  #cluster the network
  clustermaker_url <- paste("cluster mcl network=SUID:",current_network_id, sep="")
  commandsGET(clustermaker_url)

  #get the clustering results
  default_node_table <- getTableColumns(table= "node",network = as.numeric(current_network_id))

  head(default_node_table)

Perform pathway Enrichment on one of the clusters using g:Profiler(Reimand et al.). g:Profiler is an online functional enrichment web service that will take your gene list and return the set of enriched pathways. For automated analysis g:Profiler has created an R library to interact with it directly from R instead of using the web page.

Create a function to call g:Profiler and convert the returned results into a generic enrichment map input file.

tryCatch(expr = { library("gProfileR")}, 
         error = function(e) { install.packages("gProfileR")}, finally = library("gProfileR"))

#function to run gprofiler using the gprofiler library
# 
# The function takes the returned gprofiler results and formats it to the generic EM input file
#
# function returns a data frame in the generic EM file format.
runGprofiler <- function(genes,current_organism = "hsapiens", 
                         significant_only = F, set_size_max = 200, 
                         set_size_min = 3, filter_gs_size_min = 5 , exclude_iea = F){

  gprofiler_results <- gprofiler(genes ,
                                 significant=significant_only,ordered_query = F,
                                exclude_iea=exclude_iea,max_set_size = set_size_max,
                                 min_set_size = set_size_min,
                                 correction_method = "fdr",
                                 organism = current_organism,
                                src_filter = c("GO:BP","REAC"))

  #filter results
  gprofiler_results <- gprofiler_results[which(gprofiler_results[,'term.size'] >= 3
                                        & gprofiler_results[,'overlap.size'] >= filter_gs_size_min ),]

  # gProfileR returns corrected p-values only.  Set p-value to corrected p-value
  if(dim(gprofiler_results)[1] > 0){
    em_results <- cbind(gprofiler_results[,
                                c("term.id","term.name","p.value","p.value")], 1,
                                gprofiler_results[,"intersection"])
  colnames(em_results) <- c("Name","Description", "pvalue","qvalue","phenotype","genes")

  return(em_results)
  } else {
    return("no gprofiler results for supplied query")
  }
}

Run g:Profiler. g:Profiler will return a set of pathways and functions that are found to be enriched in our query set of genes.

  current_cluster <- "1"
  #select all the nodes in cluster 1
  selectednodes <- selectNodes(current_cluster, by.col="__mclCluster")

  #create a subnetwork with cluster 1
  subnetwork_suid <- createSubnetwork(nodes="selected")

  renameNetwork("Cluster1_Subnetwork", network=as.numeric(subnetwork_suid))

  subnetwork_node_table <- getTableColumns(table= "node",network = as.numeric(subnetwork_suid))

  em_results <- runGprofiler(subnetwork_node_table$name)

 #write out the g:Profiler results
 em_results_filename <-file.path(getwd(),
                            "230_Isserlin_RCy3_intro", "data",paste("gprofiler_cluster",current_cluster,"enr_results.txt",sep="_"))

  write.table(em_results,em_results_filename,col.name=TRUE,sep="\t",row.names=FALSE,quote=FALSE)


  head(em_results)

Create an enrichment map with the returned g:Profiler results. An enrichment map is a different sort of network. Instead of nodes representing genes, nodes represent pathways or functions. Edges between these pathways or functions represent shared genes or pathway crosstalk. An enrichment map is a way to visualize your enrichment results to help reduce redundancy and uncover main themes. Pathways can also be explored in detail using the features available through the App in Cytoscape.

 em_command = paste('enrichmentmap build analysisType="generic" ', 
                   'pvalue=',"0.05", 'qvalue=',"0.05",
                   'similaritycutoff=',"0.25",
                   'coeffecients=',"JACCARD",
                   'enrichmentsDataset1=',em_results_filename ,
                   sep=" ")

  #enrichment map command will return the suid of newly created network.
  em_network_suid <- commandsRun(em_command)

  renameNetwork("Cluster1_enrichmentmap", network=as.numeric(em_network_suid))

Export image of resulting Enrichment map.

cluster1em_png_file_name <- file.path(getwd(),"230_Isserlin_RCy3_intro", "images","cluster1em.png")

if(file.exists(cluster1em_png_file_name)){
  #cytoscape hangs waiting for user response if file already exists.  Remove it first
  file.remove(cluster1em_png_file_name)
  } 

#export the network
exportImage(cluster1em_png_file_name, type = "png")

Annotate the Enrichment map to get the general themes that are found in the enrichment results of cluster 1

#get the column from the nodetable and node table
  nodetable_colnames <- getTableColumnNames(table="node",  network =  as.numeric(em_network_suid))

  descr_attrib <- nodetable_colnames[grep(nodetable_colnames, pattern = "_GS_DESCR")]

  #Autoannotate the network
  autoannotate_url <- paste("autoannotate annotate-clusterBoosted labelColumn=", descr_attrib," maxWords=3 ", sep="")
    current_name <-commandsGET(autoannotate_url)

Export image of resulting Annotated Enrichment map.

cluster1em_annot_png_file_name <- file.path(getwd(),"230_Isserlin_RCy3_intro", "images","cluster1em_annot.png")

if(file.exists(cluster1em_annot_png_file_name)){
  #cytoscape hangs waiting for user response if file already exists.  Remove it first
  file.remove(cluster1em_annot_png_file_name)
  } 

#export the network
exportImage(cluster1em_annot_png_file_name, type = "png")

Dense networks small or large never look like network figures we so often see in journals. A lot of manual tweaking, reorganization and optimization is involved in getting that perfect figure ready network. The above network is what the network starts as. The below figure is what it can look like after a few minutes of manual reorganiazation. (individual clusters were selected from the auto annotate panel and separated from other clusters)

Reducing our large OMICs expression set to a simple list of genes eliminates the majority of the signals present in the data. Thresholding will only highlight the strong signals neglecting the often more interesting subtle signals. In order to capitalize on the wealth of data present in the data we need to perform pathway enrichment analysis on the entire expression set. There are many tools in R or as standalone apps that perform this type of analysis.

To demonstrate how you can use Cytoscape and RCy3 in your enrichment analysis pipeline we will use EnrichmentBrowser package (as demonstrated in detail in the workshop Functional enrichment analysis of high-throughput omics data) to perform pathway analysis.

if(!"EnrichmentBrowser" %in% installed.packages()){
    install.packages("BiocManager")
    BiocManager::install("EnrichmentBrowser")
}

suppressPackageStartupMessages(library(EnrichmentBrowser))

Download the latest pathway definition file from the Baderlab download site. Baderlab genesets are updated on a monthly basis. Detailed information about the sources can be found here.

Only Human, Mouse and Rat gene set files are currently available on the Baderlab downloads site. If you are working with a species other than human (and it is either rat or mouse) change the gmt_url below to correct species.

tryCatch(expr = { suppressPackageStartupMessages(library("RCurl"))}, 
         error = function(e) {  install.packages("RCurl")}, 
         finally = library("RCurl"))

gmt_url = "http://download.baderlab.org/EM_Genesets/current_release/Human/symbol/"


#list all the files on the server
filenames = getURL(gmt_url)
tc = textConnection(filenames)
contents = suppressWarnings(readLines(tc))
close(tc)

#get the gmt that has all the pathways and does not include terms inferred from electronic annotations(IEA)
#start with gmt file that has pathways only
rx = gregexpr("(?<=<a href=\")(.*.GOBP_AllPathways_no_GO_iea.*.)(.gmt)(?=\">)",
  contents, perl = TRUE)
gmt_file = unlist(regmatches(contents, rx))

dest_gmt_file <- file.path(getwd(), "230_Isserlin_RCy3_intro", "data", gmt_file)

download.file(
    paste(gmt_url,gmt_file,sep=""),
    destfile=dest_gmt_file
)

Load in the gmt file

library(EnrichmentBrowser)
baderlab.gs <- getGenesets(dest_gmt_file)
baderlab.gs[1:2]

$`THIO-MOLYBDENUM COFACTOR BIOSYNTHESIS%HUMANCYC%PWY-5963`
[1] "MOCOS"

$`PROLINE BIOSYNTHESIS I%HUMANCYC%PROSYN-PWY`
[1] "PYCR2"    "ALDH18A1" "PYCR1"    "PYCRL"   

Create the dataset required by EnrichmentBrowser tools

#create the expression file - A tab separated text file containing expression values. Columns = samples/subjects; rows = features/probes/genes; NO headers, row or column names.
expr <- RNASeq_expression

sumexpr_filename <- file.path(getwd(), "230_Isserlin_RCy3_intro","data","SummarizeExperiment_expression.txt") 
write.table( expr ,  file = sumexpr_filename , col.names  = FALSE, row.names = FALSE, sep = "\t", quote=FALSE)


rowData <- RNASeq_gene_scores[,grep(colnames(RNASeq_gene_scores), pattern="mesen")]
rowData <- cbind(RNASeq_gene_scores$Name,rowData)
colnames(rowData)[2] <- "FC"
colnames(rowData)[6] <- "ADJ.PVAL"

sumexpr_rdat_filename <- file.path(getwd(), "230_Isserlin_RCy3_intro","data","SummarizeExperiment_rdat.txt") 
write.table( rowData[,1] ,  file = sumexpr_rdat_filename , col.names  = FALSE, row.names = FALSE, sep = "\t", quote=FALSE)

#load in the data classification data
# A tab separated text file containing annotation information for the samples in either *two or three* columns. NO headers, row or column names. The number of rows/samples in this file should match the number of columns/samples of the expression matrix. The 1st column is reserved for the sample IDs; The 2nd column is reserved for a *BINARY* group assignment. Use '0' and '1' for unaffected (controls) and affected (cases) sample class
classDefinitions_RNASeq <- read.table( 
  file.path(getwd(), "230_Isserlin_RCy3_intro","data","RNASeq_classdefinitions.txt"), header = TRUE, sep = "\t", quote="\"", stringsAsFactors = FALSE)

colData <- data.frame(Sample = colnames(RNASeq_expression),
                      GROUP = classDefinitions_RNASeq$SUBTYPE, 
                      stringsAsFactors = FALSE)
rownames(colData) <- colnames(RNASeq_expression)
colData$GROUP[which(colData$GROUP != "Mesenchymal")] <- 0
colData$GROUP[which(colData$GROUP == "Mesenchymal")] <- 1

sumexpr_cdat_filename <- file.path(getwd(), "230_Isserlin_RCy3_intro","data","SummarizeExperiment_cdat.txt") 
write.table( colData ,  file = sumexpr_cdat_filename , col.names  = FALSE, row.names = FALSE, sep = "\t", quote=FALSE)

#create the Summarize Experiment object
se_OV <- EnrichmentBrowser::readSE(assay.file = sumexpr_filename , cdat.file = sumexpr_cdat_filename, rdat.file = sumexpr_rdat_filename)

Put our precomputed p-values and fold change values into the Summarized Experiment object so we can use our rankings for the analysis

#set the Summarized Experiment to our computed p-values and FC
rowData(se_OV) <- rowData

Run basic Over representation analysis (ORA) using our ranked genes and our gene set file downloaded from the Baderlab genesets.

ora.all <- sbea(method="ora", se=se_OV, gs=baderlab.gs, perm=0, alpha=0.05)
gsRanking(ora.all)

Take the enrichment results and create a generic enrichment map input file so we can create an Enrichment map. Description of format of the generic input file can be found here and example generic enrichment map files can be found here

#manually adjust p-values
ora.all$res.tbl <- cbind(ora.all$res.tbl, p.adjust(ora.all$res.tbl$P.VALUE, "BH"))
colnames(ora.all$res.tbl)[ncol(ora.all$res.tbl)] <- "Q.VALUE" 

#create a generic enrichment map file
em_results_mesen <- data.frame(name = ora.all$res.tbl$GENE.SET,descr = ora.all$res.tbl$GENE.SET, 
                               pvalue=ora.all$res.tbl$P.VALUE, qvalue=ora.all$res.tbl$Q.VALUE, stringsAsFactors = FALSE)

#write out the ora results
 em_results_mesen_filename <-file.path(getwd(),
                            "230_Isserlin_RCy3_intro","data","mesen_ora_enr_results.txt")

  write.table(em_results_mesen,em_results_mesen_filename,col.name=TRUE,sep="\t",row.names=FALSE,quote=FALSE)

Create an enrichment map with the returned ORA results.

 em_command = paste('enrichmentmap build analysisType="generic" ', "gmtFile=", dest_gmt_file,
                   'pvalue=',"0.05", 'qvalue=',"0.05",
                   'similaritycutoff=',"0.25",
                   'coeffecients=',"JACCARD",
                   'enrichmentsDataset1=',em_results_mesen_filename ,
                   sep=" ")

  #enrichment map command will return the suid of newly created network.
  em_mesen_network_suid <- commandsRun(em_command)

  renameNetwork("Mesenchymal_ORA_enrichmentmap", network=as.numeric(em_mesen_network_suid))

Export image of resulting Enrichment map.

mesenem_png_file_name <- file.path(getwd(),"230_Isserlin_RCy3_intro", "images","mesenem.png")

if(file.exists(mesenem_png_file_name)){
  #cytoscape hangs waiting for user response if file already exists.  Remove it first
  file.remove(mesenem_png_file_name)
  } 

#export the network
exportImage(mesenem_png_file_name, type = "png")

Annotate the Enrichment map to get the general themes that are found in the enrichment results. Often for very busy networks annotating the networks doesn't help to reduce the complexity but instead adds to it. To get rid of some of the pathway redundancy and density in the network create a summary of the underlying network. The summary network collapses each cluster to a summary node. Each summary node is annotated with a word tag (the top 3 words associated with the nodes of the cluster) that is computed using the Wordcloud app.

#get the column from the nodetable and node table
  nodetable_colnames <- getTableColumnNames(table="node",  network =  as.numeric(em_mesen_network_suid))

  descr_attrib <- nodetable_colnames[grep(nodetable_colnames, pattern = "_GS_DESCR")]

  #Autoannotate the network
  autoannotate_url <- paste("autoannotate annotate-clusterBoosted labelColumn=", descr_attrib," maxWords=3 ", sep="")
    current_name <-commandsGET(autoannotate_url)

  #create a summary network 
  commandsGET("autoannotate summary network='current'")

  #change the network name
  summary_network_suid <- getNetworkSuid()

  renameNetwork(title = "Mesen_ORA_summary_netowrk",
              network = as.numeric(summary_network_suid))

  #get the summary node names
  summary_nodes <- getTableColumns(table="node",columns=c("name"))

  #clear bypass style the summary network has
  clearNodePropertyBypass(node.names = summary_nodes$name,visual.property = "NODE_SIZE")

  #relayout network
  layoutNetwork('cose',
              network = as.numeric(summary_network_suid))

Export image of resulting Summarized Annotated Enrichment map.

mesenem_summary_png_file_name <- file.path(getwd(),"230_Isserlin_RCy3_intro","images","mesenem_summary_network.png")

if(file.exists(mesenem_summary_png_file_name)){
  #cytoscape hangs waiting for user response if file already exists.  Remove it first
  file.remove(mesenem_summary_png_file_name)
  } 

#export the network
exportImage(mesenem_summary_png_file_name, type = "png")