.gitattributes

 *.cys filter=lfs diff=lfs merge=lfs -text
 *.cluego filter=lfs diff=lfs merge=lfs -text
 *.tsv filter=lfs diff=lfs merge=lfs -text
+*.pdf filter=lfs diff=lfs merge=lfs -text

Day_3/Solutions/Exercise_2_R_code_2020.R

+#---------------------------------------------------------------------------------------
+# Exercise 2
+#---------------------------------------------------------------------------------------
+
+# Given a list of hits and a background in folder /Exercise 2
+# background.txt and id_list.txt
+
+# perform enrichment analysis against 
+# 1. Gene Ontology,
+# 2. Kegg modules, and
+# 3. DisGeNet (diseases)
+
+# For each result produce a cnetplot and an upsetplot.
+# - you will need to provide the correct id's for the functions to execute - use converters
+# - vary the p-value q-value thresholds in case of an empty result
+# - make the disgenet output readable (gene names, not entrez id's)
+
+
+background <- read.csv("/Users/rogon/Work/01. Teaching/CBNA Courses/2020 Online Course Series/2020 CBNA-DeNBI Enrichment course - my materials/Part 2 - R-code session ClusterProfiler, ReactomePA, pathfindR/Exercise 2 (custom background)/Background.txt", sep="")
+id_list <- read.delim("/Users/rogon/Work/01. Teaching/CBNA Courses/2020 Online Course Series/2020 CBNA-DeNBI Enrichment course - my materials/Part 2 - R-code session ClusterProfiler, ReactomePA, pathfindR/Exercise 2 (custom background)/id_list.txt")
+
+
+
+keytypes(org.Hs.eg.db)
+entrez_map <- bitr(background$GeneID, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db", drop= FALSE)
+
+
+# or biomart:
+
+# you will need to convert identifiers - for that purpose I suggest using the biomaRt package in R, alternatively you can use the website itselt
+# ensembl biomart http://www.ensembl.org/biomart/martview/
+library("biomaRt")
+ensembl = biomaRt::useMart("ensembl")
+ensembl = useDataset("hsapiens_gene_ensembl", mart=ensembl)
+datasets_biomart <- listDatasets(ensembl)
+attributes_biomart <- listAttributes(ensembl)
+
+id_list_biomart= getBM(attributes = c("hgnc_symbol","entrezgene_id", "ensembl_gene_id"),
+               filters = c(filters = "entrezgene_id"),
+               values = id_list$EntrezGene.ID,
+               mart = ensembl)
+
+
+background_biomart= getBM(attributes = c("hgnc_symbol","entrezgene_id", "ensembl_gene_id"),
+                          filters = c(filters = "hgnc_symbol"),
+                          values = background$GeneID,
+                          mart = ensembl)
+
+
+# part 1
+id_list_go <- enrichGO(gene = id_list_biomart$entrezgene_id, 'org.Hs.eg.db', pvalueCutoff = 0.05, pAdjustMethod = "BH", universe = names(background_biomart$entrezgene_id), minGSSize = 5, maxGSSize = 500, qvalueCutoff = 0.05, readable = TRUE)
+head(id_list_go)
+cnetplot(id_list_go)
+upsetplot(id_list_go)
+
+# part 2 
+id_list_mkegg <- enrichMKEGG(gene = id_list_biomart$entrezgene_id, pvalueCutoff = 0.1, pAdjustMethod = "BH", universe = names(background_biomart$entrezgene_id), minGSSize = 5, maxGSSize = 500, qvalueCutoff = 0.1)
+head(id_list_mkegg)
+cnetplot(id_list_mkegg)
+upsetplot(id_list_mkegg)
+
+# part 3
+id_list_dgn <- enrichDGN(gene = id_list_biomart$entrezgene_id, pvalueCutoff = 0.05, pAdjustMethod = "BH", universe = names(background_biomart$entrezgene_id), minGSSize = 5, maxGSSize = 500, qvalueCutoff = 0.05, readable = TRUE)
+head(id_list_dgn)
+cnetplot(id_list_dgn)
+upsetplot(id_list_dgn)
+
+# GO without background 
+id_list_go_noBckg <- enrichGO(gene = id_list_biomart$entrezgene_id, 'org.Hs.eg.db', pvalueCutoff = 0.05, pAdjustMethod = "BH", minGSSize = 5, maxGSSize = 500, qvalueCutoff = 0.05, readable = TRUE)
+head(id_list_go_noBckg)
+cnetplot(id_list_go_noBckg)
+upsetplot(id_list_go_noBckg)
+
+

Day_3/Solutions/Exercise_3_Multicluster_R_code_2020.R

+# Let's learn how to perform comparative multi cluster analysis
+#
+# Matt Rogon
+# rogon@embl.de
+# 10.2020
+# ver. 0.5
+
+library(dplyr)
+library(STRINGdb)
+library(reshape2)
+library(ReactomePA)
+library(clusterProfiler)
+library(pathview)
+library(DOSE)
+library(org.Mm.eg.db)
+
+
+setwd("/Users/rogon/Work/01. Teaching/CBNA Courses/2020 CBNA-DeNBI Enrichment course - my materials/Part 2 - R-code session ClusterProfiler, ReactomePA, pathfindR/Exercise 4/")
+
+# load complete 5 cluster-dataset
+hit_list <- read.delim("550prot_clusters.txt", stringsAsFactors=FALSE)
+
+#--------------------------------------------------------------
+# let's annotate uniprot to entrez for ClusterProfiler
+#------------------------------------------------------------------------------------
+# Annotation with bitr in ClusterProfiler for enrichment check
+#------------------------------------------------------------------------------------
+keytypes(org.Mm.eg.db)
+entrez_map <- bitr(hit_list$UniProt, fromType="UNIPROT", toType=c("ENTREZID", "SYMBOL"), OrgDb="org.Mm.eg.db", drop= FALSE)
+write.table(entrez_map, file="output/entrez_map.txt", row.names = F, quote = FALSE, sep="\t")
+
+
+#--------------------------------------------------------------
+# Enrichment with ClusterProfiler
+#--------------------------------------------------------------
+# expression values:
+#data(geneList)
+#de <- names(geneList)[geneList > 1]
+
+# to do enrichment on a 'per cluster' basis I need the cluster column from the data frame: main_string_mapping_select
+input_for_enrichment <- merge(hit_list, entrez_map, by.x = "UniProt", by.y = "UNIPROT", all = TRUE)
+input_for_enrichment <- input_for_enrichment[complete.cases(input_for_enrichment),]
+
+
+
+# extract entrez id's from the annotation frame 'results' created earlier
+# for each cluster separately
+
+#c1 <- subset(input_for_enrichment, cluster==1)
+#c1_s <- dplyr::select(c1, ENTREZID)
+#c1_s <- unique(c1_s)
+#c1_list <- as.list(c1_s$ENTREZID)
+
+#same in 2 lines
+c1 <- dplyr::select(subset(input_for_enrichment, cluster ==1), ENTREZID)
+c1_list <- as.list(c1$ENTREZID)
+
+c2 <- dplyr::select(subset(input_for_enrichment, cluster ==2), ENTREZID)
+c2_list <- as.list(c2$ENTREZID)
+
+c3 <- dplyr::select(subset(input_for_enrichment, cluster ==3), ENTREZID)
+c3_list <- as.list(c3$ENTREZID)
+
+c4 <- dplyr::select(subset(input_for_enrichment, cluster ==4), ENTREZID)
+c4_list <- as.list(c4$ENTREZID)
+
+c5 <- dplyr::select(subset(input_for_enrichment, cluster ==5), ENTREZID)
+c5_list <- as.list(c5$ENTREZID)
+
+
+hit_list_Entrez_flat <- unlist(hit_list_Entrez)
+hit_list_Entrez2_flat <- unlist(hit_list_Entrez2)
+
+
+#---------------------------------------------------------------------------------------
+# Individual cluster enrichment
+#---------------------------------------------------------------------------------------
+
+go_1 <- enrichGO(c1_list, OrgDb = "org.Mm.eg.db", ont="BP", universe = input_for_enrichment$ENTREZID, pvalueCutoff=0.05, pAdjustMethod="BH", qvalueCutoff=0.1, minGSSize = 5)
+head(as.data.frame(go_1))
+
+
+#----
+# Multi-cluster analysis
+#----
+
+input_all = list()
+input_all$X1 <- c1_list
+input_all$X2 <- c2_list
+input_all$X3 <- c3_list
+input_all$X4 <- c4_list
+input_all$X5 <- c5_list
+summary(input_all)
+
+# Gene Ontology comparison
+res_go <- compareCluster(input_all, fun="enrichGO", OrgDb="org.Mm.eg.db", pvalueCutoff=0.1) 
+dotplot(res_go, title="GO Enrichment Comparison")
+
+res_reactome <- compareCluster(input_all, fun="enrichPathway", organism='mouse', pvalueCutoff=0.01, pAdjustMethod="BH", qvalueCutoff=0.1)
+dotplot(res_reactome, title="Reactome Enrichment Comparison")
+
+
+
+
+
+

Day_3/Solutions/Exercise_4_cluster_comparative_analysis_2020.R

+
+#---------------------------------------------------------------------------------------
+# Exercise 4
+# Comparative analysis using ClusterProfiler and custom annotation with enricher
+# go to the subdirectory 
+# /Exercise 4
+#---------------------------------------------------------------------------------------
+# here you will find 2 files:
+# cluster1.txt
+# and
+# cluster2.txt
+# all_gene_disease_associations.tsv
+
+
+# Matt Rogon
+# rogon@embl.de
+
+#source("http://bioconductor.org/biocLite.R") 
+# biocLite("ReactomePA")
+
+library(clusterProfiler)
+library(ReactomePA)
+library(pathview)
+library(org.Hs.eg.db)
+
+#----------------------------------------------------------------------------------------
+# load data - atm and dlblc hit lists (gene symbols)
+setwd("/Users/rogon/Work/01. Teaching/CBNA Courses/2020 CBNA-DeNBI Enrichment course - my materials/Part 2 - R-code session ClusterProfiler, ReactomePA, pathfindR/Exercise 4 (custom ontology with enricher, and multicluster)")
+
+# perform comparative cluster analysis for all clusters in the source file against the custom annotation file
+# compareCluster accepts fun= switch which works in a similar way of passing fun='enrichGO' or fun='enrichKEGG'.
+# you can pass fun='enricher'
+#----------------------------------------------------------------------------------------
+
+
+# load data - cluster 1 and cluster 2 into individual hit lists 
+# the first column contains gene symbols
+# convert them to entrez id's
+# here is an example on how to proceed with bitr function for annotation: 
+# keytypes(org.Hs.eg.db)
+# hit_ids <- bitr(hit_list$gene, fromType="SYMBOL", toType=c("ENTREZID"), OrgDb="org.Hs.eg.db")
+
+# create a custom annotation for use with enricher for that we will use the 
+# all_gene_disease_associations.tsv file and create two dataframes from this annotation:
+# 1. disease2gene
+# 2. disease2name
+
+# the first maps disease id to gene id
+# the second maps disease id to disease names
+# as you may imagine you will need the gene id to be entrez for both the hit list and the annotation file
+# first column of the source annotation file is the entrezID
+
+
+# Once all is constructed you will use the compareCluster function to
+# run enricher with the above created annotations
+
+# produce dotplot comparing the two clusters
+
+
+#----------------------------------------------------------------------------------------
+# load clusters and create the input list of lists
+#----------------------------------------------------------------------------------------
+
+atm_hit_list <- read.delim("cluster1.txt", stringsAsFactors=FALSE)
+dlbcl_hit_list <- read.delim("cluster2.txt", stringsAsFactors=FALSE)
+
+#Convert id's to entrez
+# If organism is not supported by AnnotationHub, user can use AnnotationForge to build OrgDb.
+keytypes(org.Hs.eg.db)
+hit_ids <- bitr(atm_hit_list$gene, fromType="SYMBOL", toType=c("ENTREZID"), OrgDb="org.Hs.eg.db")
+hit_ids2 <- bitr(dlbcl_hit_list$gene, fromType="SYMBOL", toType=c("ENTREZID"), OrgDb="org.Hs.eg.db")
+
+# entrez lists for each analysis
+hit_list_Entrez <- as.list(hit_ids$ENTREZID)
+hit_list_Entrez <- unique(hit_list_Entrez)
+
+hit_list_Entrez2 <- as.list(hit_ids2$ENTREZID)
+hit_list_Entrez2 <- unique(hit_list_Entrez2)
+#hit_list_Entrez2 <- noquote(hit_list_Entrez2)
+
+hit_list_Entrez_flat <- unlist(hit_list_Entrez)
+hit_list_Entrez2_flat <- unlist(hit_list_Entrez2)
+
+input = list()
+input$X1 <- hit_list_Entrez_flat
+input$X2 <- hit_list_Entrez2_flat
+summary(input)
+
+
+
+
+
+#----------------------------------------------------------------------------------------
+# 
+# Build a custom ontology from the provided annotation file
+# all_gene_disease_associations.tsv
+#----------------------------------------------------------------------------------------
+# Currently clusterProfiler supports user’s own annotations via the enricher and GSEA functions, 
+# which require users provide their own annotation in a data frame 
+#----------------------------------------------------------------------------------------
+# Custom enrichment with clusterProfiler - preparation of 2 data frames
+# TERM2GENE is a data.frame with first column of term ID and second column of corresponding mapped gene 
+# TERM2NAME is a data.frame with first column of term ID and second column of corresponding term name. 
+# TERM2NAME is optional.
+#----------------------------------------------------------------------------------------
+
+require(DOSE)
+require(clusterProfiler)
+
+# load this file and look at the content of the data frame
+# load the annotation file (this is from DisGeNet)
+gda <- read.delim("/Users/rogon/Work/01. Teaching/CBNA Courses/2020 CBNA-DeNBI Enrichment course - my materials/Part 2 - R-code session ClusterProfiler, ReactomePA, pathfindR/Exercise 3 comparing conditions/all_gene_disease_associations.tsv")
+dim(gda)
+View(gda)
+
+# to create a custom annotation file we need to create two dataframes from this annotation
+# 1. disease2gene
+# 2. disease2name
+
+# Let's create the two required dataframes - diseaseID to geneID, and diseaseID to diseaseName
+disease2gene=gda[, c("diseaseId", "geneId")]
+disease2name=gda[, c("diseaseId", "diseaseName")]
+
+
+
+#----------------------------------------------------------------------------------------
+# # Explore the enricher function in ClusterProfiler
+#
+# Build an enrichment against the custom ontology/annotation
+# and compare the input clusters all in one
+#----------------------------------------------------------------------------------------
+# set p-value to 0.05
+# q value to 0.1
+# Benjamini-Hochberg correction
+
+res_go <- compareCluster(input, fun="enricher", TERM2GENE=disease2gene, TERM2NAME=disease2name,pvalueCutoff = 0.05, pAdjustMethod = "BH", qvalueCutoff = 0.1)
+
+# create a dotplot and a cnetplot
+dotplot(res_go, showCategory=10,includeAll=TRUE, title="Custom Disease Enrichment")
+cnetplot(res_go)
+
+
+
+

Day_3/Solutions/Exercise_5_pathfindR_tutorial.txt

+# PathfindR tutorial and exercises
+# 10.2020
+# v.0.1
+# Matt Rogon EMBL CBNA
+
+install.packages("pathfindR")
+library(pathfindR)
+
+openVignette(pathfindR)
+# Let's explore the package and functions
+# https://github.com/egeulgen/pathfindR
+# paper
+# https://www.frontiersin.org/articles/10.3389/fgene.2019.00858/full
+# website
+# https://egeulgen.github.io/pathfindR/
+
+
+# load sample data into a dataframe
+input_df <- pathfindR.data::RA_input
+
+
+# have a look at the formatting
+View(input_df)
+
+# all you need are 3 columns to execute the pathfindR main wrapper
+output_df <- run_pathfindR(input_df)
+
+# visualise the output_df results
+
+
+
+#--------------------------------------------------------------------------------------
+# Exercise 1:
+# Load the following file: 
+# pathfinder_input_exercise_clusters.tsv
+#
+# prepare the dataframe for pathfindeR
+# you will need 3 columns:
+# Gene name
+# logFC
+# adj P Val
+# Run pathfindR wrapper function on this dataset
+# visualise
+
+#--------------------------------------------------------------------------------------
+
+# Exercise 2
+# Create a custom script that will execute pathfindR with the following settings:
+# Benjamini-Hochberg FDR
+# adjusted p_val_threshold 0.01
+# gene sets: GO-BP
+# minimum gene set size 5, maximum 100
+# PIN protein interaction reference network: IntAct
+# 
+# generate a fuzzy-clustered result network of terms
+# term gene heatmap
+# term gene graph
+# upset plot
+
+#--------------------------------------------------------------------------------------
+
+# Exercise 3
+# Use the original file pathfinder_input_exercise_clusters.txt and compare clusters 1 and 2 with pathfindR
+# using combine_pathfindR_results function
+# produce visualisations
+
+#--------------------------------------------------------------------------------------
+
+
+
+
+
+
+
+
+
+
+# load the prepared file (columns must be numeric)
+pathfinder_input_exercise_noClusters <- read.delim("/Users/rogon/Work/01. Teaching/CBNA Courses/2020 CBNA-DeNBI Enrichment course - my materials/Part 2 - R-code session ClusterProfiler, ReactomePA, pathfindR/pathfindR/exercise/pathfinder_input_exercise_noClusters.txt")
+pathfinder_input_exercise_noClusters$adj.P.Val <- as.numeric(pathfinder_input_exercise_noClusters$adj.P.Val)
+pathfinder_input_exercise_noClusters$logFC <- as.numeric(pathfinder_input_exercise_noClusters$logFC)
+
+  
+# make sure rows are complete
+pathfinder_input_exercise_noClusters <- pathfinder_input_exercise_noClusters[complete.cases(pathfinder_input_exercise_noClusters), ]
+
+# run it
+output_noClust <- run_pathfindR(pathfinder_input_exercise_noClusters)
+
+# define threshold
+output_df <- run_pathfindR(pathfinder_input_exercise_noClusters, p_val_threshold = 0.01)
+
+# define gene sets
+# output_df <- run_pathfindR(pathfinder_input_exercise_noClusters, gene_sets = "GO-MF")
+
+## Including more terms for enrichment analysis
+output_df <- run_pathfindR(pathfinder_input_exercise_noClusters, 
+                           gene_sets = "GO-MF",
+                           min_gset_size = 5,
+                           max_gset_size = 500)
+
+# By default, run_pathfindR() adjusts the enrichment p values via the “bonferroni” method 
+# and filters the enriched terms by adjusted-p value < 0.05. 
+# To change this adjustment method and the threshold, set adj_method and enrichment_threshold,
+# respectively, here we have benjamini hochberg fdr with 0.01 thresholding:
+output_df <- run_pathfindR(pathfinder_input_exercise_noClusters, 
+                           adj_method = "fdr",
+                           enrichment_threshold = 0.01)
+
+
+# Protein-protein Interaction Network and Background
+# For the active subnetwork search process, a protein-protein interaction network (PIN) is used. run_pathfindR() maps the input genes onto this PIN and identifies active subnetworks which are then be used for enrichment analyses. To change the default PIN (“Biogrid”), use the pin_name_path argument:
+# The pin_name_path argument can be one of “Biogrid”, “STRING”, “GeneMania”, “IntAct”, “KEGG”, “mmu_STRING” or it can be the path to a custom PIN file provided by the user.
+# NOTE: the PIN is also used for generating the background genes (in this case, all unique genes in the PIN) during hypergeometric-distribution-based tests in enrichment analyses. Therefore, a large PIN will generally result in better results.
+# to use an external PIN of your choice
+# output_df <- run_pathfindR(input_df, pin_name_path = "/path/to/myPIN.sif")
+
+output_df <- run_pathfindR(pathfinder_input_exercise_noClusters, pin_name_path = "IntAct")
+
+
+# plot the results with various graphs
+term_gene_heatmap(result_df = output_df, genes_df = pathfinder_input_exercise_noClusters)
+term_gene_graph(result_df = output_df, use_description = TRUE)
+UpSet_plot(result_df = output_df, genes_df = RA_input)
+
+
+# lets cluster the output enriched terms - fuzzy clustering 
+RA_clustered_fuzzy <- cluster_enriched_terms(output_df, method = "fuzzy")
+
+#This function first calculates the pairwise kappa statistics between the terms in the data frame obtained from run_pathfindR(). It then clusters the terms and partitions them into relevant clusters. As can be seen in the diagram, there are two options for clustering: (1) hierarchical clustering and (2) fuzzy clustering.
+# https://github.com/egeulgen/pathfindR/wiki/Clustering%20Documentation
+
+
+
+# run Comparison of 2 pathfindR Results
+The function combine_pathfindR_results() allows combination of two pathfindR active-subnetwork-oriented enrichment analysis results for investigating common and distinct terms between the groups. Below is an example for comparing results using two different rheumatoid arthritis-related data sets(RA_output and RA_comparison_output).
+
+combined_df <- combine_pathfindR_results(result_A = RA_output, 
+                                         result_B = RA_comparison_output, 
+                                         plot_common = FALSE)
+                                         
+                                         
+                 
+                 
+
+
+pathfinder_input_exercise_clusters <- read.delim("/Users/rogon/Work/01. Teaching/CBNA Courses/2020 CBNA-DeNBI Enrichment course - my materials/Part 2 - R-code session ClusterProfiler, ReactomePA, pathfindR/pathfindR/exercise/pathfinder_input_exercise_clusters.txt")
+
+
+                        
+
+
+non human
+https://cran.r-project.org/web/packages/pathfindR/vignettes/non_hs_analysis.html
+
+
+
+
+# Running pathfindR manually to control the environment and statistics
+
+# https://egeulgen.github.io/pathfindR/articles/manual_execution.html
+
+#---------------------------------------------------------------------------------------
+#1
+#Process input data
+# After checking that the data frame complies with the requirements, 
+# input_processing() filters the input so that genes with p values larger than 
+# p_val_threshold are excluded. 
+# Next, gene symbols that are not in the PIN are identified and excluded. 
+# For human genes, if aliases of these missing gene symbols are found in the PIN, 
+# these symbols are converted to the corresponding aliases (controlled by the argument 
+# convert2alias). This step is performed to best map the input data onto the PIN.
+#---------------------------------------------------------------------------------------
+
+RA_processed <- input_processing(input = RA_input, # the input: in this case, differential expression results
+                                 p_val_threshold = 0.05, # p value threshold to filter significant genes
+                                 pin_name_path  = "Biogrid", # the name of the PIN to use for active subnetwork search
+                                 convert2alias = TRUE) # boolean indicating whether or not to convert missing symbols to alias symbols in the PIN
+
+
+
+
+#---------------------------------------------------------------------------------------
+# 2
+# Obtain Gene Set Data necessary for enrichment analyses using fetch_gene_set():
+# using "BioCarta" as our gene sets for enrichment
+# available gene sets are 
+# “KEGG”, “Reactome”, “BioCarta”, “GO-All”, “GO-BP”, “GO-CC” and “GO-MF”. 
+# If the user prefers to use another gene set source, the gene_sets argument should be
+# set to "Custom" and the custom gene sets (list) and the custom gene set descriptions 
+# (named vector) should be supplied via the arguments custom_genes and custom_descriptions, 
+# respectively. See ?fetch_gene_set for more details.
+#---------------------------------------------------------------------------------------
+
+biocarta_list <- fetch_gene_set(gene_sets = "BioCarta",
+                                min_gset_size = 10,
+                                max_gset_size = 300)
+biocarta_gsets <- biocarta_list[[1]]
+biocarta_descriptions <- biocarta_list[[2]]
+
+
+#---------------------------------------------------------------------------------------
+# 3. Active Subnetwork Search and Enrichment Analyses
+# As outlined in the vignette Introduction to pathfindR, run_pathfindR() initially identifies and filters active subnetworks, then performs enrichment analyses on these subnetworks and summarize the results.
+# To perform these steps manually, we utilize the function active_snw_search() for identifying and filtering active subnetworks and the function enrichment_analyses() for obtaining enriched terms using these subnetworks. Because the active subnetwork search algorithms are stochastic, we suggest iterating these subnetwork identification and enrichment steps multiple times (especially for “SA”)1:
+#---------------------------------------------------------------------------------------
+
+
+Active Subnetwork Search Method
+
+# Currently, there are three algorithms implemented in pathfindR for active subnetwork search: Greedy Algorithm (default, based on Ideker et al. 2), Simulated Annealing Algorithm (based on Ideker et al. 3) and Genetic Algorithm (based on Ozisik et al. 4). For a detailed discussion on which algorithm to use see this wiki entry
+# for simulated annealing:
+#output_df <- run_pathfindR(input_df, search_method = "SA")
+# for genetic algorithm:
+# output_df <- run_pathfindR(input_df, search_method = "GA")
+#
+
+
+n_iter <- 10 ## number of iterations
+combined_res <- NULL ## to store the result of each iteration
+
+for (i in 1:n_iter) {
+  
+  ###### Active Subnetwork Search
+  snws_file <- paste0("active_snws_", i) # Name of output file
+  active_snws <- active_snw_search(input_for_search = RA_processed, 
+                                   pin_name_path = "Biogrid", 
+                                   snws_file = snws_file,
+                                   score_quan_thr = 0.8, # you may tweak these arguments for optimal filtering of subnetworks
+                                   sig_gene_thr = 0.02, # you may tweak these arguments for optimal filtering of subnetworks
+                                   search_method = "GR")
+  
+  ###### Enrichment Analyses
+  current_res <- enrichment_analyses(snws = active_snws,
+                                     sig_genes_vec = RA_processed$GENE,
+                                     pin_name_path = "Biogrid", 
+                                     genes_by_term = biocarta_gsets,
+                                     term_descriptions = biocarta_descriptions,
+                                     adj_method = "bonferroni",
+                                     enrichment_threshold = 0.05,
+                                     list_active_snw_genes = TRUE) # listing the non-input active snw genes in output
+  
+  ###### Combine results via `rbind`
+  combined_res <- rbind(combined_res, current_res)
+}
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+custom_result <- run_pathfindR(input_df,
+                               gene_sets = "Custom",
+                               custom_genes = custom_genes,
+                               custom_descriptions = custom_descriptions,
+                               max_gset_size = Inf, # DO NOT LIMIT GENE SET SIZE
+                               iterations = 1, # for demo, setting number of iterations to 1
+                               output_dir = "misc/v1.4/CREB_MYC")
+
+
+
+
+
+
+
+# since we are using pathfindR we will not need pcsf, however have a look at it
+# source("http://bioconductor.org/biocLite.R")
+#biocLite("topGO")
+#install.packages("devtools", dependencies=TRUE)
+#devtools::install_github("IOR-Bioinformatics/PCSF", repos=BiocInstaller::biocinstallRepos(),
+                         dependencies=TRUE, type="source", force=TRUE)

Day_4/1-background/UniProt_accession_list_COSMIC_expert_curated.txt

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Day_4/1-background/UniProt_accession_list_TSG.txt

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\ No newline at end of file

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Day_4/1-background/UniProt_accession_list_oncogene.txt

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\ No newline at end of file