ActivePathways - integrative pathway analysis of multi-omics data

July 11th 2025: ActivePathways version 2.0.6 is now available on CRAN and GitHub that provides functionality to compare directional and non-directional analyses. The major update 2.0 provides the directional p-value merging (DPM) method described in our recent publication.

ActivePathways is a tool for multivariate pathway enrichment analysis that identifies gene sets, such as pathways or Gene Ontology terms, that are over-represented in a list or matrix of genes. ActivePathways uses a data fusion method to combine multiple omics datasets, prioritises genes based on the significance and direction of signals from the omics datasets, and performs pathway enrichment analysis of these prioritised genes. We can find pathways and genes supported by single or multiple omics datasets, as well as additional genes and pathways that are only apparent through data integration and remain undetected in any single dataset alone.

The new version of ActivePathways is described in our recent publication.

Mykhaylo Slobodyanyuk^, Alexander T. Bahcheli^, Zoe P. Klein, Masroor Bayati, Lisa J. Strug, Jüri Reimand. Directional integration and pathway enrichment analysis for multi-omics data. Nature Communications 15, 5690 (2024). (^ - co-first authors) https://www.nature.com/articles/s41467-024-49986-4 https://pubmed.ncbi.nlm.nih.gov/38971800/

The first version of ActivePathways was published in Nature Communications with the PCAWG Pan-Cancer project.

Marta Paczkowska^, Jonathan Barenboim^, Nardnisa Sintupisut, Natalie S. Fox, Helen Zhu, Diala Abd-Rabbo, Miles W. Mee, Paul C. Boutros, PCAWG Drivers and Functional Interpretation Working Group, PCAWG Consortium, Juri Reimand. Integrative pathway enrichment analysis of multivariate omics data. Nature Communications 11, 735 (2020) (^ - co-first authors) https://www.nature.com/articles/s41467-019-13983-9 https://pubmed.ncbi.nlm.nih.gov/32024846/

The package version 2.0.3 used in the DPM preprint and manuscript is archived on Zenodo: https://zenodo.org/records/12118089.

Installation

Package tested with: MacOS 14, Windows 11, Ubuntu 20.04.

Software dependencies: data.table, ggplot2, testthat, knitr, rmarkdown, RColorBrewer.

Installation time: less than 2 minutes.

From CRAN: ActivePathways 2.0.5 is currently the most recent version

Open R and run install.packages('ActivePathways')

Using devtools on our GitHub repository

Using the R package devtools, run devtools::install_github('https://github.com/reimandlab/ActivePathways', build_vignettes = TRUE)

From source on our GitHub repository

Clone the repository, for example using git clone https://github.com/reimandlab/ActivePathways.git.

Open R in the directory where you cloned the package and run install.packages("ActivePathways", repos = NULL, type = "source")

Using ActivePathways

See the vignette for more details. Run browseVignettes(package='ActivePathways') in R.

Examples

The simplest use of ActivePathways requires only a data table and a GMT file. The data table is a matrix of p-values of genes/transcripts/proteins as rows and omics datasets as columns. it also needs a list of gene sets in the form of a GMT (Gene Matrix Transposed) file.

• The data table must be a numerical matrix. For a single gene list, a one-column matrix can be used. The matrix cannot contain any missing values, and one conservative option is to re-assign all missing values as 1s, indicating our confidence that the missing P-values are always insignificant. Alternatively, one may consider removing genes with NA values.

• Gene sets in the form of a GMT file can be acquired from multiple sources such as Gene Ontology, Reactome and others. For better accuracy and statistical power these pathway databases should be combined. Acquiring an up-to-date GMT file is essential to avoid using unreliable outdated annotations (see this paper).

```R

library(ActivePathways)

Run an example using the data files included in the ActivePathways package.

This basic example does not incorporate directionality.

fnamescores <- system.file("extdata", "Adenocarcinomascoressubset.tsv", package = "ActivePathways") fnameGMT <- system.file("extdata", "hsapiensREACsubset.gmt", package = "ActivePathways")

Numeric matrix of p-values is required as input.

NA values are converted to P = 1.

scores <- read.table(fname_scores, header = TRUE, row.names = 'Gene') scores <- as.matrix(scores) scores[is.na(scores)] <- 1

Main call of ActivePathways function:

enrichedpathways <- ActivePathways(scores, fnameGMT)

35 terms were removed from gmt because they did not make the geneset_filter

91 rows were removed from scores because they are not found in the background

list a few first results of enriched pathways identified by ActivePathways

enriched_pathways[1:3,]

termid termname adjustedpval term_size

1: REAC:2424491 DAP12 signaling 4.491268e-05 358

2: REAC:422475 Axon guidance 2.028966e-02 555

3: REAC:177929 Signaling by EGFR 6.245734e-04 366

overlap evidence

1: TP53,PIK3CA,KRAS,PTEN,BRAF,NRAS,... CDS

2: PIK3CA,KRAS,BRAF,NRAS,CALM2,RPS6KA3,... X3UTR,promCore

3: TP53,PIK3CA,KRAS,PTEN,BRAF,NRAS,... CDS

GenesX3UTR GenesX5UTR

1: NA NA

2: CALM2,ARPC2,RHOA,NUMB,CALM1,ACTB,... NA

3: NA NA

Genes_CDS

1: TP53,PTEN,KRAS,PIK3CA,BRAF,NRAS,...

2: NA

3: TP53,PTEN,KRAS,PIK3CA,BRAF,NRAS,...

Genes_promCore

1: NA

2: EFNA1,IQGAP1,COL4A1,SCN2B,RPS6KA3,CALM2,...

3: NA

Show enriched genes of the first pathway 'DAP12 signalling'

the column overlap displays genes of the integrated dataset (from

data fusion, i.e., p-value merging) that occur in the given pathway.

Genes are ranked by joint significance across input omics datasets.

enriched_pathways[["overlap"]][[1]]

[1] "TP53" "PIK3CA" "KRAS" "PTEN" "BRAF" "NRAS" "B2M" "CALM2"

[9] "CDKN1A" "CDKN1B"

Save the resulting pathways as a Comma-Separated Values (CSV) file

for spreadsheets and computational pipelines.

the data.table object cannot be saved directly as text.

exportasCSV(enrichedpathways, "enrichedpathways.csv")

Examine a few lines of the two major types of input

The scores matrix includes p-values for genes (rows)

and evidence of different omics datasets (columns).

This dataset includes predicted cancer driver mutations

in gene coding/CDS, 5'UTR, 3'UTR, and core promoter sequences

head(scores, n = 3)

X3UTR X5UTR CDS promCore

A2M 1.0000000 0.33396764 0.9051708 0.4499201

AAAS 1.0000000 0.42506012 0.7047723 0.7257641

ABAT 0.9664126 0.04202735 0.7600985 0.1903789

GMT files include functional gene sets (pathways, processes).

Each tab-separated line represents a gene set:

gene set ID, description followed by gene symbols.

Gene symbols in the scores table and the GMT file need to match.

NB: this GMT file is a small subset of the real GMT file built for testing.

It should not be used for real analyses.

readLines(fname_GMT)[11:13]

[1] "REAC:3656535\tTGFBR1 LBD Mutants in Cancer\tTGFB1\tFKBP1A\tTGFBR2\tTGFBR1\t"

[2] "REAC:73927\tDepurination\tOGG1\tMPG\tMUTYH\t"

[3] "REAC:5602410\tTLR3 deficiency - HSE\tTLR3\t"

```

Examples - Directional integration of multi-omics data

ActivePathways 2.0 extends our integrative pathway analysis framework significantly. Users can now provide directional assumptions of input omics datasets for more accurate analyses. This allows us to prioritise genes and pathways where certain directional assumptions are met, and penalise those where the assumptions are violated.

For example, fold-change in protein expression would be expected to associate positively with mRNA fold-change of the corresponding gene, while negative associations would be unexpected and indicate more-complex situations or potential false positives. We can instruct the pathway analysis to prioritise positively-associated protein/mRNA pairs and penalise negative associations (or vice versa).

Two additional inputs are included in ActivePathways that allow diverse multi-omics analyses. These inputs are optional.

The scoresdirection and constraintsvector parameters are provided in the mergepvalues() and ActivePathways() functions to incorporate this directional penalty into the data fusion and pathway enrichment analyses.

The parameter constraintsvector is a vector that allows the user to represent the expected relationship between the input omics datasets. The vector size is ndatasets. Values include +1, -1, and 0. The constraintsvector should reflect the expected relative directional relationship between datasets. For example, the constraintsvector values c(-1,1) and c(1,-1) are functionally identical. When combining datasets that contain both directional datatypes (eg gene or protein expression, gene promoter methylation) and non-directional datatypes (eg gene mutational burden, ChIP-seq), we can define the relative relationship between directional datatypes with the values 1 and -1 while setting the value of non-directional datatypes to 0.

The parameter scoresdirection is a matrix that reflects the directions that the genes/transcripts/protein show in the data. The matrix size is ngenes * n_datasets, that is the same size as the P-value matrix. This is a numeric matrix, but only the signs of the values are accounted for.

Directional data integration at the gene level

```R

load a dataset of P-values and fold-changes for mRNA and protein levels

this dataset is embedded in the package

fnamedatamatrix <- system.file('extdata', 'Differentialexpressionrnaprotein.tsv', package = 'ActivePathways') pvalsFCs <- read.table(fnamedatamatrix, header = TRUE, sep = '\t')

examine a few example genes

examplegenes <- c('ACTN4','PIK3R4','PPIL1','NELFE','LUZP1','ITGB2') pvalsFCs[pvalsFCs$gene %in% examplegenes,]

gene rnapval rnalog2fc proteinpval proteinlog2fc

73 PIK3R4 1.266285e-03 1.1557077 2.791135e-03 -0.8344799

74 PPIL1 1.276838e-03 -1.1694221 1.199303e-04 -1.1193605

606 NELFE 1.447553e-02 -0.9120687 1.615592e-05 -1.2630114

4048 LUZP1 3.253382e-05 1.5830796 4.129125e-02 0.5791377

4050 ITGB2 4.584450e-05 1.6472117 1.327997e-01 0.4221579

4052 ACTN4 5.725503e-05 1.5531533 8.238317e-07 1.4279158

create a matrix of gene/protein P-values.

where the columns are different omics datasets (mRNA, protein)

and the rows are genes.

pvalmatrix <- data.frame( row.names = pvalsFCs$gene, rna = pvalsFCs$rnapval, protein = pvalsFCs$proteinpval) pvalmatrix <- as.matrix(pvalmatrix)

examine a few genes in the P-value matrix

pvalmatrix[examplegenes,]

rna protein

ACTN4 5.725503e-05 8.238317e-07

PIK3R4 1.266285e-03 2.791135e-03

PPIL1 1.276838e-03 1.199303e-04

NELFE 1.447553e-02 1.615592e-05

LUZP1 3.253382e-05 4.129125e-02

ITGB2 4.584450e-05 1.327997e-01

convert missing values to P = 1

pvalmatrix[is.na(pvalmatrix)] <- 1

Create a matrix of gene/protein directions

similarly to the P-value matrix (i.e., scores_direction)

dirmatrix <- data.frame( row.names = pvalsFCs$gene, rna = pvalsFCs$rnalog2fc, protein = pvalsFCs$proteinlog2fc) dirmatrix <- as.matrix(dirmatrix)

ActivePathways only uses the signs of the direction values (ie +1 or -1).

dirmatrix <- sign(dirmatrix)

if directions are missing (NA), we recommend setting the values to zero

dirmatrix[is.na(dirmatrix)] <- 0

examine a few genes in the direction matrix

dirmatrix[examplegenes,]

rna protein

ACTN4 1 1

PIK3R4 1 -1

PPIL1 -1 -1

NELFE -1 -1

LUZP1 1 1

ITGB2 1 1

This matrix has to be accompanied by a vector that

provides the expected relationship between the

different datasets. Here, mRNA levels and protein

levels are expected to have consistent directions:

either both positive or both negative (eg log fold-change).

constraints_vector <- c(1,1)

Alternatively, we can use another vector to prioritise

genes/proteins where the directions are the opposite.

constraints_vector <- c(1,-1)

Now we merge the P-values of the two datasets

using directional assumtions and compare these

with the plain non-directional merging.

The top 5 scoring genes differ if we penalise genes

where this directional logic is violated:

While 4 of 5 genes retain significance, the gene PIK3R4 is penalised.

Interestingly, as a consequence of penalising PIK3R4,

other genes such as ITGB2 move up in rank.

directionalmergedpvals <- mergepvalues(pvalmatrix, method = "DPM", dirmatrix, constraints_vector)

mergedpvals <- mergepvalues(pvalmatrix, method = "Brown")

sort(merged_pvals)[1:5]

ACTN4 PPIL1 NELFE LUZP1 PIK3R4

1.168708e-09 2.556067e-06 3.804646e-06 1.950607e-05 4.790125e-05

sort(directionalmergedpvals)[1:5]

ACTN4 PPIL1 NELFE LUZP1 ITGB2

1.168708e-09 2.556067e-06 3.804646e-06 1.950607e-05 7.920157e-05

PIK3R4 is penalised because the fold-changes of its mRNA and

protein levels are significant and have the opposite signs:

pvalsFCs[pvalsFCs$gene == "PIK3R4",]

gene rnapval rnalog2fc proteinpval proteinlog2fc

73 PIK3R4 0.001266285 1.155708 0.002791135 -0.8344799

pval_matrix["PIK3R4",]

rna protein

0.001266285 0.002791135

dir_matrix["PIK3R4",]

rna protein

1 -1

merged_pvals["PIK3R4"]

PIK3R4

4.790125e-05

directionalmergedpvals["PIK3R4"]

PIK3R4

0.8122527

``` To assess the impact of the directional penalty on gene merged P-value signals we create a plot showing directional results on the y axis and non-directional results on the x. Blue dots are prioritised hits, red dots are penalised.

```R lineplotdf <- data.frame(original = -log10(mergedpvals), modified = -log10(directionalmergedpvals))

ggplot(lineplotdf) + geompoint(size = 2.4, shape = 19, aes(original, modified, color = ifelse(original <= -log10(0.05),"gray", ifelse(modified > -log10(0.05),"#1F449C","#F05039")))) + labs(title = "", x ="Merged -log10(P)", y = "Directional Merged -log10(P)") + geomhline(yintercept = 1.301, linetype = "dashed", col = 'black', size = 0.5) + geomvline(xintercept = 1.301, linetype = "dashed", col = "black", size = 0.5) + geomabline(size = 0.5, slope = 1,intercept = 0) + scalecolor_identity()

```

Pathway-level insight

To explore how changes on the individual gene level impact biological pathways, we can compare results before and after incorporating a directional penalty.

```R

use the example GMT file embedded in the package

fnameGMT2 <- system.file("extdata", "hsapiensREAC_subset2.gmt", package = "ActivePathways")

Integrative pathway enrichment analysis with no directionality

enrichedpathways <- ActivePathways( pvalmatrix, gmt = fnameGMT2, cytoscapefiletag = "Original")

Directional integration and pathway enrichment analysis

this analysis the directional coefficients and constraints_vector from

the gene-based analysis described above

constraints_vector

[1] 1 1

dirmatrix[examplegenes,]

rna protein

ACTN4 1 1

PIK3R4 1 -1

PPIL1 -1 -1

NELFE -1 -1

LUZP1 1 1

ITGB2 1 1

enrichedpathwaysdirectional <- ActivePathways( pvalmatrix, gmt = fnameGMT2, cytoscapefiletag = "Directional", mergemethod = "DPM", scoresdirection = dirmatrix, constraintsvector = constraintsvector)

Examine the pathways that are lost when

directional information is incorporated in the data integration

pathwayslostindirectionalintegration = setdiff(enrichedpathways$termid, enrichedpathwaysdirectional$termid) pathwayslostindirectional_integration

[1] "REAC:R-HSA-3858494" "REAC:R-HSA-69206" "REAC:R-HSA-69242"

[4] "REAC:R-HSA-9013149"

enrichedpathways[enrichedpathways$termid %in% pathwayslostindirectional_integration,]

termid termname adjustedpval

1: REAC:R-HSA-3858494 Beta-catenin independent WNT signaling 0.013437464

2: REAC:R-HSA-69206 G1/S Transition 0.026263457

3: REAC:R-HSA-69242 S Phase 0.009478766

4: REAC:R-HSA-9013149 RAC1 GTPase cycle 0.047568911

term_size overlap evidence

1: 143 PSMA5,PSMB4,PSMC5,PSMD11,PSMA8,GNG13,... rna,protein

2: 130 PSMA5,PSMB4,CDK4,PSMC5,PSMD11,CCNB1,... protein

3: 162 PSMA5,PSMB4,RFC3,CDK4,PSMC5,PSMD11,... combined

4: 184 SRGAP1,TIAM1,BAIAP2,FMNL1,DOCK9,PAK3,... rna

Genes_rna

1: GNG13,PSMC1,PSMA5,PSMB4,ITPR3,DVL1,...

2: NA

3: NA

4: SRGAP1,TIAM1,FMNL1,ARHGAP30,FARP2,DOCK10,...

Genes_protein

1: PSMA8,PSMD11,PSMA5,PRKG1,PSMD10,PSMB4,...

2: PSMD11,PSMA5,PSMD10,PSMB4,CDK7,ORC2,...

3: NA

4: NA

An example of a lost pathway is Beta-catenin independent WNT signaling.

Out of the 32 genes that contribute to this pathway enrichment,

10 genes are in directional conflict. The enrichment is no longer

identified when these genes are penalised due to the conflicting

log2 fold-change directions.

wntpathwayid <- "REAC:R-HSA-3858494" enrichedpathwaygenes <- unlist( enrichedpathways[enrichedpathways$termid == wntpathwayid,]$overlap) enrichedpathway_genes

[1] "PSMA5" "PSMB4" "PSMC5" "PSMD11" "PSMA8" "GNG13" "SMURF1" "PSMC1"

[9] "PSMA4" "PLCB2" "PRKG1" "PSMD4" "PSMD1" "PSMD10" "PSMA6" "PSMA2"

[17] "PSMA1" "PRKCA" "PSMC6" "RHOA" "PSMB3" "PSMB1" "PSME3" "ITPR3"

[25] "AGO4" "DVL3" "PSMA3" "PPP3R1" "DVL1" "CLTA" "PSME2" "CALM1"

[33] "PSMD6" "PSMB6"

examine the pathway genes that have directional disagreement and

contribute to the lack of pathway enrichment in the directional analysis

pathwaygenepvals = pvalmatrix[enrichedpathwaygenes,] pathwaygenedirections = dirmatrix[enrichedpathwaygenes,]

directionalconflictgenes = names(which( pathwaygenedirections[,1] != pathwaygenedirections[,2] & pathwaygenedirections[,1] != 0 & pathwaygenedirections[,2] != 0))

pathwaygenepvals[directionalconflictgenes,]

rna protein

PSMD11 0.34121101 0.002094310

PSMA8 0.55510836 0.001415197

SMURF1 0.03353629 0.042995333

PSMD1 0.04650877 0.100178048

RHOA 0.01786687 0.474628084

PSME3 0.07148904 0.130184883

ITPR3 0.01660850 0.589929787

DVL3 0.46381447 0.022535743

PSME2 0.03274707 0.514351089

PSMB6 0.02863259 0.677224905

pathwaygenedirections[directionalconflictgenes,]

rna protein

PSMD11 1 -1

PSMA8 1 -1

SMURF1 1 -1

PSMD1 1 -1

RHOA -1 1

PSME3 1 -1

ITPR3 1 -1

DVL3 1 -1

PSME2 -1 1

PSMB6 -1 1

length(directionalconflictgenes)

[1] 10

```

ActivePathways 2.0 allows you to incorporate directional information in your pathway analysis. You can compare results with and without directional penalties to gain deeper insights into your data.

```R

Run standard pathway analysis

enrichedpathways <- ActivePathways( pvalmatrix, gmt = fname_GMT2)

Run directional pathway analysis

enrichedpathwaysdirectional <- ActivePathways( pvalmatrix, gmt = fnameGMT2, mergemethod = "DPM", scoresdirection = dirmatrix, constraintsvector = constraints_vector)

Examine pathways lost due to directional conflicts

pathwayslost <- setdiff(enrichedpathways$termid, enrichedpathwaysdirectional$termid) enrichedpathways[enrichedpathways$termid %in% pathwayslost,] ```

Merging results for visualization in Cytoscape

ActivePathways provides functions to merge results from multiple analyses for visualization in Cytoscape:

```R

Merge results from standard and directional analyses

mergedresults <- mergeresults( enrichedpathways, enrichedpathwaysdirectional, gmtfile = fnameGMT2, outputprefix = "Aggregated", col_colors = c("#FF0000", "#00FF00", "#FFFFF0") ) ```

This creates files for Cytoscape visualization that show both analyses in a single enrichment map, with node borders indicating which pathways were: - Shared between both analyses - Lost when using directional analysis - Gained when using directional analysis

Follow the same steps for creating an enrichment map in Cytoscape, but use the "Aggregated_" files instead.

To visualise differences in biological pathways between ActivePathways analyses with or without a directional penalty, we combine both outputs into a single enrichment map for plotting.

More thorough documentation of the ActivePathways function can be found in R with ?ActivePathways, and complete tutorials can be found with browseVignettes(package='ActivePathways').

Visualising pathway enrichment results using enrichment maps in Cytoscape

Cytoscape provides powerful tools to visualise the enriched pathways from ActivePathways as a network (i.e., an enrichment map). ActivePathways provides the files needed for building enrichment maps in Cytoscape. To create these files, supply a file prefix to the argument cytoscape_file_tag in the ActivePathways() function. The prefix can be a path to an existing writable directory.

{r} res <- ActivePathways(scores, fname_GMT, cytoscape_file_tag = "enrichmentMap__") Four files are written using the prefix:

• enrichmentMap__pathways.txt: a table of significant terms and the associated adjusted P-values. Terms include molecular pathways, biological processes, and other gene sets. Note that only terms with adjusted_p_val <= significant are written.

• enrichmentMap__subgroups.txt: a matrix indicating which columns of the input matrix (i.e., which omics datasets) contributed to the discovery of each pathway. These values correspond to the evidence evaluation of input omics datasets discussed above. A value of one indicates the pathway was also detectable using a specific input omics dataset. A value of zero indicates otherwise. This file will be not generated if the input matrix is a single-column matrix of scores (just one omics dataset).

• enrichmentMap__pathways.gmt: a shortened version of the supplied GMT file, containing only the significant pathways detected by ActivePathways.

• enrichmentMap__legend.pdf: a pdf file containing a legend, with colors corresponding to the different omics datasets visualised in the enrichment map. This can be used as a reference to the generated enrichment map.

Creating enrichment maps using results of ActivePathways

Pathway enrichment analysis often leads to complex and redundant results. Enrichment maps are network-based visualisations of pathway enrichment analyses. Enrichment maps can be generated in the Cytoscape software using the EnrichmentMap app. The enhancedGraphics app is also required. See the vignette for details: browseVignettes(package='ActivePathways').

Required software

1. Cytoscape, see https://cytoscape.org/download.html
2. EnrichmentMap app of Cytoscape, see menu Apps>App manager or https://apps.cytoscape.org/apps/enrichmentmap
3. EnhancedGraphics app of Cytoscape, see menu Apps>App manager or https://apps.cytoscape.org/apps/enhancedGraphics

Creating the enrichment map

• Open the Cytoscape software.
• Select Apps -> EnrichmentMap.
• In the following dialogue, click the button + Add Data Set from Files in the top left corner of the dialogue.
• Change the Analysis Type to Generic/gProfiler/Enrichr.
• Upload the files enrichmentMap__pathways.txt and enrichmentMap__pathways.gmt in the Enrichments and GMT fields, respectively.
• Click the checkbox Show Advanced Options and set Cutoff to 0.6.
• Then click Build in the bottom-right corner to create the enrichment map.

Colour the nodes of the network to visualise supporting omics datasets

The third file enrichmentMap__subgroups.txt needs to be imported to Cytoscape directly in order to color nodes (i.e. terms) according to their source omics datasets. To import the file, select the menu option File -> Import -> Table from File and select the file enrichmentMap__subgroups.txt. In the following dialogue, select To a Network Collection in the dropdown menu Where to Import Table Data. Click OK to proceed.

Cytoscape uses the imported information to color nodes like a pie chart. To enable this click the Style tab in the left control panel and select the Image/Chart1 Property in a series of dropdown menus (Properties -> Paint -> Custom Paint 1 -> Image/Chart 1).

The Image/Chart 1 property now appears in the Style control panel. Click the triangle on the right, then set the Column to instruct and the Mapping Type to Passthrough Mapping.

This step colours the nodes corresponding to the enriched pathways according to the supporting omics datasets, based on the scores matrix initially analysed in ActivePathways.

ActivePathways generates a color legend in the file enrichmentMap__legend.pdf that shows which colors correspond to which omics datasets.

Note that one of the colors corresponds to a subset of enriched pathways with combined evidence. These terms were only detected through data fusion and P-value merging, and not with any of the input datasets individually. This exemplifies the added value of integrative multi-omics pathway enrichment analysis.

Visualising directional impact with node borders

From the drop-down Properties menu, select Border Line Type.

Set Column to directional impact and Mapping Type to Discrete Mapping. Now we can compare findings between a non-directional and a directional method. We highlight pathways that were shared (0), lost (1), and gained (2) between the approaches. Here, we have solid lines for the shared pathways, dots for the lost pathways, and vertical lines for the gained pathways. Border widths can be adjusted in the Border Width property, again with discrete mapping.

This step changes node borders in the aggregated enrichment map, depicting the additional information provided by directional impact.

Alternative node coloring

For a more diverse range of colors, ActivePathways supports any color palette from RColorBrewer. The colorpalette parameter must be provided. ```{r} res <- ActivePathways(scores, gmtfile, cytoscapefiletag = "enrichmentMap_", colorpalette = "Pastel1") ```

Alternatively, the customcolors parameter can be specified as a vector to manually input the color of each dataset. This vector should contain the same number of colors as columns in the scores matrix. ```{r} res <- ActivePathways(scores, gmtfile, cytoscapefiletag = "enrichmentMap_", customcolors = c("violet","green","orange","red")) ```

To change the color of the combined contribution, a color must be provided to the colorintegratedonly parameter.

If the coloring of nodes did not work in Cytoscape after setting the options in the Style panel, check that the EnhancedGraphics Cytoscape app is installed.

References

• See the vignette for more details: browseVignettes(package='ActivePathways').

• Mykhaylo Slobodyanyuk^, Alexander T. Bahcheli^, Zoe P. Klein, Masroor Bayati, Lisa J. Strug, Jüri Reimand. Directional integration and pathway enrichment analysis for multi-omics data. Nature Communications (2024) (^ - co-first authors) https://www.nature.com/articles/s41467-024-49986-4 https://pubmed.ncbi.nlm.nih.gov/38971800/.

• Integrative Pathway Enrichment Analysis of Multivariate Omics Data. Paczkowska M^, Barenboim J^, Sintupisut N, Fox NS, Zhu H, Abd-Rabbo D, Mee MW, Boutros PC, PCAWG Drivers and Functional Interpretation Working Group; Reimand J, PCAWG Consortium. Nature Communications (2020) (^ - co-first authors) https://pubmed.ncbi.nlm.nih.gov/32024846/ https://doi.org/10.1038/s41467-019-13983-9.

• Pathway Enrichment Analysis and Visualization of Omics Data Using g:Profiler, GSEA, Cytoscape and EnrichmentMap. Reimand J^, Isserlin R^, Voisin V, Kucera M, Tannus-Lopes C, Rostamianfar A, Wadi L, Meyer M, Wong J, Xu C, Merico D, Bader GD. Nature Protocols (2019) (^ - co-first authors) https://pubmed.ncbi.nlm.nih.gov/30664679/ https://doi.org/10.1038/s41596-018-0103-9.