Required Files

Recall that ActivePathways writes four files to be used in Cytoscape.

files <- c(system.file('extdata', 'pathways.txt', package='ActivePathways'),
           system.file('extdata', 'subgroups.txt', package='ActivePathways'),
           system.file('extdata', 'pathways.gmt', package='ActivePathways'),
           system.file('extdata', 'legend.pdf', package ='ActivePathways'))

# Not run. 
res <- ActivePathways(dat, gmt, cytoscape.file.dir='path/to/results/directory')

The files written are:

• pathways.txt contains the list of significant pathways and the associated p-values. Note that only terms with p.val <= significant are written, even if the option return.all=TRUE is specified.

• subgroups.txt contains a matrix indicating the columns of the original scores matrix which contributed significant pathways. A value of 1 indicates that the pathway is also significant if ActivePathways is run on the p-values in that column, indicating that the pathway would be also identified by only analysing that dataset alone.

• pathways.gmt contains a shortened version of the supplied GMT file which includes only the significant pathways and associated genes. This abridged file can accelerate the process of creating the network using Cytoscape.

• legend.pdf is a graphics file of PDF format that displays the color legend that can be used as a reference to the generated Cytoscape network.

cat(paste(readLines(files[1])[1:3], collapse='\n'))

## term.id  term.name   adjusted.p.val
## REAC:2424491 DAP12 signaling 4.49126833230489e-05
## REAC:422475  Axon guidance   0.0202896586319552

cat(paste(readLines(files[2])[1:5], collapse='\n'))

## term.id  CDS X3UTR   promCore    combined    instruct
## REAC:2424491 1   0   0   0   piechart: attributelist="CDS,X3UTR,promCore,combined" colorlist="#FF0000FF,#80FF00FF,#00FFFFFF,#8000FFFF" showlabels=FALSE
## REAC:422475  0   1   1   0   piechart: attributelist="CDS,X3UTR,promCore,combined" colorlist="#FF0000FF,#80FF00FF,#00FFFFFF,#8000FFFF" showlabels=FALSE
## REAC:177929  1   0   0   0   piechart: attributelist="CDS,X3UTR,promCore,combined" colorlist="#FF0000FF,#80FF00FF,#00FFFFFF,#8000FFFF" showlabels=FALSE
## REAC:2559583 1   0   0   0   piechart: attributelist="CDS,X3UTR,promCore,combined" colorlist="#FF0000FF,#80FF00FF,#00FFFFFF,#8000FFFF" showlabels=FALSE

cat(paste(readLines(files[3])[1], collapse='\n'))

## REAC:2424491 DAP12 signaling IL17RD  PSMC1   PDGFRB  PSMD14  TNRC6C  CD80    DUSP10  SPTBN4  PIP5K1B NRG1    TNRC6A  FGF22   ADCY5   CHUK    PSME2   CUL3    NRG2    PSMB8   PSMA2   PSMB3   PSMD13  AC010132.3  PIK3CB  FGF18   PRKCE   FN1 SHC2    RBX1    PSMD5   FGF19   NF1 ARRB1   FGFR1   PRKACG  FGB PSME1   VAV1    PSMD3   ITGA2B  HBEGF   ERBB3   FGF3    KSR2    PPP2R5C VAV3    PPP2CB  IL5RA   PSMB9   CDKN1B  RASA2   PPP2R5E PIK3AP1 PSMF1   FOXO4   PSMC3   PSMA6   AL358075.4  FGF2    ICOS    IL2RB   BTC ADCY2   RAPGEF2 CDKN1A  DUSP6   ERBB4   PTK2    MET FGA TSC2    NRAS    SPTB    SPRED3  TNRC6B  FOXO3   GSK3A   FGF1    IL3RA   PIP5K1A IL2RA   GFRA4   PSMD1   TEK RICTOR  PSMD10  CASP9   CAMK2G  RASAL3  PPP2R1A PSMB6   FGF6    IL3 MAPKAP1 SYK CNKSR1  RASGRF2 GRIN2C  PSMB7   CD86    ADCY3   TLN1    PSMC4   HRAS    MLST8   VAV2    LAMTOR2 THEM4   DUSP7   RASGRF1 CALM3   RAP1B   PSMC2   KSR1    RAC1    ADCY1   JAK1    SPRED2  PDE1A   FGF10   PSME4   EGF PDE1B   GRIN2A  IRS2    VWF PIK3CD  FGFR4   PHB AKT1    IQGAP1  PTPRA   PSMB1   PRKAR1B KL  PRKCD   PSMD9   PSMB2   EGFR    MAP2K2  PRKAR2B KRAS    CAMK2D  SRC PIK3CA  NRTN    IL2 CAMK2B  KIT CSF2RA  CSK UBC SPTBN1  RASA4   CD19    DUSP2   LAMTOR3 ADCY4   PHLPP2  ARAF    PSMD2   PDGFB   PSMA8   FGFR3   NEFL    TRAT1   MIR26A2 FGF23   AGO3    PTPN11  PSMB10  GAB1    MAPK1   TREM2   PRKAR2A LCK ADCY6   SPRED1  MIR26A1 SPTAN1  PSMB11  PDPK1   ITPR3   KBTBD7  RET PSMD8   FGG GFRA1   AHCYL1  FOXO1   PIK3R1  RASGEF1A    JAK2    B2M FGF20   PIK3R2  FGF4    RASAL1  FGF7    PIP4K2C PDGFA   PRR5    TYROBP  EREG    PPP2R5B GRAP2   SOS1    DUSP8   PRKACA  RASA1   PSMA5   DUSP4   PRKCA   ADCY7   CSF2    DUSP16  PHLPP1  CAMK4   KLB GDNF    AKT2    CREB1   PPP2CA  FGF8    FGF9    ITPR2   BAD PAQR3   SYNGAP1 APBB1IP SEM1    RPS6KB2 AKT1S1  PPP5C   PLCG1   PSMA7   SHC1    ARTN    PRKCG   PSMA3   KITLG   GRK2    AKAP9   ANGPT1  FGF17   MTOR    PDE1C   GRIN2B  NR4A1   ITGB3   PSMC5   AGO1    KLRD1   BRAF    AKT3    PSMD4   PIP4K2A TRIB3   RASA3   MDM2    PSMA4   SPTBN2  DAB2IP  DUSP5   PSMB4   PSMA1   MAPK3   KLRK1   GSK3B   PDGFRA  CAMK2A  VCL GFRA2   ITPR1   KLRC2   LCP2    ERBB2   GRIN1   GRB2    LAT RAF1    SPTA1   RANBP9  PIP4K2B FRS3    INS-IGF2    RASGRP3 SHC3    IER3    INSR    BRAP    PEBP1   CD28    GRIN2D  FYN YWHAB   IL5 AGO4    UBB FGF16   IL2RG   FGF5    PSMC6   IRS1    PPP2R1B ARRB2   MARK3   BTK PLCG2   RAP1A   PEA15   PSMD6   ADCY8   JAK3    SPTBN5  ACTN2   PPP2R5A MAP3K11 DUSP1   DUSP9   RPS27A  CSF2RB  HLA-E   AL672043.1  RASAL2  CALM1   PIK3R3  FRS2    PRKAR1A ADCY9   FGFR2   DLG4    PPP2R5D CNKSR2  NCAM1   RASGRP4 PSMD12  PSMB5   TP53    WDR83   NRG4    PRKACB  MAP2K1  MOV10   PIP5K1C NRG3    CALM2   PTEN    UBA52   PSMD7   RNASE1  PSPN    GFRA3   PSMD11  INS RASGRP1 AGO2    HGF PSME3   

Creating the Enrichment Map

Open Cytoscape and ensure the EnrichmentMap and enchancedGraphics apps are installed. Apps may be installed by clicking Apps -> App Manager in Cytoscape. When the apps are installed, open the Apps menu again and click Enrichment Map. In the window that opens, click the Add Data Set from Files button (The ‘+’) in the top left, change the Analysis Type to ‘Generic/gProfiler’ and upload the pathways.txt and pathways.gmt files.

Click Build to create the network.

P.S. To make network more visually appealing, the Edge Cutoff slider in the EnrichmentMap tab of the Control Panel can be adjusted to determine the similarity coefficient (we recommend 0.5 or 0.6) and reduce the number of edges, the scale in the Tool Panel (View > Show Tool Panel) can be used to resize nodes, and the layout can be changed in the Layout tab (yFilesLayouts can be installed in apps, Layout > yFilesLayouts).

Adjusting the Similarity Coefficient

Adjusting the Node Size

Colouring the Nodes by Subgroup

To upload the subgroups.txt table, go to File > Import > Table > File and import the subgroups.txt file.

Click the Style tab in the Control panel and ensure the Image/Chart1 property is available. Under the Image/Chart 1 property, set the Column to ‘instruct’ and the Mapping Type to ‘passthrough’.

This setting colours the pathway nodes according to the columns (types of evidence) in which the pathway is found to be enriched when considering the initial p-value matrix only one column at a time.

For the sake of convenience, ActivePathways generates the file Legend.pdf which can be added to the enrichment map using an image editing tool.

References

1. Merico, Daniele, et al. “Enrichment map: a network-based method for gene-set enrichment visualization and interpretation.” PloS one 5.11 (2010): e13984.

2. Reimand, Jüri, et al. “Pathway enrichment analysis and visualization of omics data using g: Profiler, GSEA, Cytoscape and EnrichmentMap.” Nature protocols 14.2 (2019): 482.