Introduction

Often times, gene expression studies such as microarrays and RNA-Seq result in hundreds to thousands of differentially expressed genes (DEGs). It becomes very difficult to understand the biological significance of such massive data sets, especially when there are multiple conditions and comparisons being analyzed. This package facilitates visualization and downstream analyses of differential gene expression results, using pathway enrichment and protein-protein interaction networks, to aid researchers in uncovering underlying biology and pathophysiology from their gene expression studies.

We have included an example data set of gene expression results in this package as the object exampleDESeqResults. This is a list of 2 data frames, generated using the results() functions from the package DESeq2 (Love et al. 2014). The data is from an RNA-Seq study investigating COVID-19 and non-COVID-19 sepsis patients at admission (T1) compared to approximately1 week later (T2) in the ICU, indexed over time (i.e., T2 vs T1) (An et al. 2023).

Installation

To install and load the package:

# We'll also be using some functions from dplyr
# BiocManager::install("pathlinkR", version="devel")
library(dplyr)
library(pathlinkR)

Visualizing RNA-Seq data with volcano plots

One of the first visualizations commonly performed with gene expression studies is to identify the number of DEGs. These are typically defined using specific cutoffs for both fold change and statistical significance. Thresholds of adjusted p-value <0.05 and absolute fold change >1.5 are used as the default, though any value can be specified. pathlinkR includes the function eruption() to create a volcano plot.

## A quick look at the DESeq2 results table
data("exampleDESeqResults")
knitr::kable(head(exampleDESeqResults[[1]]))

## Generate a volcano plot from the first data frame, with default thresholds
eruption(
    rnaseqResult=exampleDESeqResults[[1]],
    title=names(exampleDESeqResults[1])
)

There are multiple options available for customizing this volcano plot, including:

• Adjust the cutoffs
• Switch the colours of significant and non-significant genes
• Change the number of labelled genes
• Adjust the x- and y-axis limits
• Plot either log2 or non-log2 fold change (for a better sense of magnitude)
• Highlight specific gene sets of interest

Visualizing fold changes across comparisons

In addition to creating volcano plots, we can also visualize our DEGs using heatmaps of genes involved in a specific pathways (e.g. one identified as significant by pathwayEnrichment()). The function plotFoldChange() accomplishes this by taking in an input list of DESeq2::results() data frames, just like pathwayEnrichment(), and creating a heatmap of fold changes for the constituent genes.

plotFoldChange(
    inputList=exampleDESeqResults,
    pathName="Interferon alpha/beta signaling"
)

A number of options are provided for customization, including:

• Shorten the names of each comparison
• Provide a custom list of genes to visualize, and add an informative title
• Use log2 fold changes in the heatmap cells
• Swap the rows and columns from the default, and add a split between the rows (comparisons)

Building and visualizing PPI networks

pathlinkR includes tools for constructing and visualizing Protein-Protein Interaction (PPI) networks. Here we leverage PPI data gathered from InnateDB to generate a list of interactions among DE genes identified in gene expression analyses. These interactions can then be used to build PPI networks within R, with multiple options for controlling the type of network, such as support for first, minimum, or zero order networks. The two main functions used to accomplish this are ppiBuildNetwork() and ppiPlotNetwork().

Let’s continue looking at the DEGs from the COVID positive patients over time, using the significant DEGs to build a PPI network. Since the data frame we’re inputting includes all measured genes (not just the significant ones), we’ll use the filterInput=TRUE option to ensure the network is made only with those genes which pass the standard thresholds (defined above). Since we’re visualizing a network of DEGs, let’s colour the nodes to indicate the direction of their dysregulation (i.e. up- or down-regulated) by specifying fillType="foldChange".

exNetwork <- ppiBuildNetwork(
    rnaseqResult=exampleDESeqResults[[1]],
    filterInput=TRUE,
    order="zero"
)

ppiPlotNetwork(
    network=exNetwork,
    title=names(exampleDESeqResults)[1],
    fillColumn=LogFoldChange,
    fillType="foldChange",
    label=TRUE,
    labelColumn=hgncSymbol,
    legend=TRUE
)

The nodes with blue labels (e.g. STAT1, FBXO6, CDH1, etc.) are hubs within the network; i.e. those genes which have a high betweenness score. The statistic used to determine hub nodes can be set in ppiBuildNetwork() with the “hubMeasure” option.

exNetworkPathways <- ppiEnrichNetwork(
    network=exNetwork,
    analysis="hallmark",
    filterResults="default",
    geneUniverse = rownames(exampleDESeqResults[[1]])
)

exSubnetwork <- ppiExtractSubnetwork(
    network=exNetwork,
    pathwayEnrichmentResult=exNetworkPathways,
    pathwayToExtract="INTERFERON GAMMA RESPONSE"
)

ppiPlotNetwork(
    network=exSubnetwork,
    fillType="oneSided",
    fillColumn=degree,
    label=TRUE,
    labelColumn=hgncSymbol,
    legendTitle="Degree"
)

Performing pathway enrichment

The essence of pathway enrichment is the concept of over-representation: that is, are there more genes belonging to a specific pathway present in our DEG list than we would expect to find by chance? To calculate this, the simplest method is to compare the ratio of DEGs in some pathway to all DEGs, and all genes in tat pathway to all genes in all pathways in a database. pathlinkR mainly uses the Reactome database (Fabregat et al. 2017) for this purpose.

One issue that can occur with over-representation analysis is the assumption that each gene in each pathway has “equal” value in belonging to that pathway. In reality, a single protein can have multiple (and sometimes very different) functions, and belong to multiple pathways, like protein kinases. There are also pathways that have substantial overlap with cellular machinery, like the TLR pathways. This can lead to enrichment of multiple similar pathways or even unrelated “false-positives” that make parsing through the results very difficult.

One solution is to use unique gene pairs, as described by the creators of the package sigora (Foroushani et al. 2013). This methodology decreases the number of similar and unrelated pathways from promiscuous genes, focusing more on the pathways that are likely related to the underlying biology. This approach is the default used in the pathlinkR function pathwayEnrichment().

The pathwayEnrichment() function takes as input a list of data frames (each from DESeq2::results()), and by default will split the genes into up- and down-regulated before performing pathway enrichment one each set. The name of the data frames in the list should indicate the comparison that was made in the DESeq2 results, as it will be used to identify the results. For analysis="sigora" we also need to provide a Gene Pair Signature Repository (gpsRepo) which contains the pathways and gene pairs to be tested. Leaving this argument to “default” will use the reaH GPS repository from sigora, containing human Reactome pathways. Alternatively one can supply their own GPS repository; see ??sigora::makeGPS() for details on how to make one.

## Note the structure of `exampleDESeqResults`: a named list of results from
## DESeq2
exampleDESeqResults

$`COVID Pos Over Time`
 
 
DataFrame with 5000 rows and 6 columns
                  baseMean log2FoldChange     lfcSE       stat      pvalue
                 <numeric>      <numeric> <numeric>  <numeric>   <numeric>
ENSG00000000938 16292.6481      -0.562495  0.145827   -3.85727 1.14662e-04
ENSG00000002586  1719.5175       0.450118  0.152012    2.96107 3.06577e-03
ENSG00000002919   870.6417      -0.244573  0.124929   -1.95769 5.02664e-02
ENSG00000002933   266.6548       0.883831  0.209363    4.22153 2.42652e-05
ENSG00000003249    11.4328       1.328713  0.288138    4.61137 4.00026e-06
...                    ...            ...       ...        ...         ...
ENSG00000284882   121.6850     -0.5997942  0.204597 -2.9315844 3.37238e-03
ENSG00000284948    38.7903     -0.1786622  0.179800 -0.9936710 3.20383e-01
ENSG00000284977    73.4571     -0.7469480  0.182492 -4.0930511 4.25734e-05
ENSG00000285417    25.7393     -0.0180603  0.304676 -0.0592769 9.52732e-01
ENSG00000285444    17.3871     -0.3702265  0.264241 -1.4010952 1.61186e-01
                       padj
                  <numeric>
ENSG00000000938 0.001353100
ENSG00000002586 0.015382030
ENSG00000002919 0.123684426
ENSG00000002933 0.000431269
ENSG00000003249 0.000119958
...                     ...
ENSG00000284882 0.016523420
ENSG00000284948 0.465445666
ENSG00000284977 0.000659367
ENSG00000285417 0.970172528
ENSG00000285444 0.286419134

$`COVID Neg Over Time`
 
 
DataFrame with 5000 rows and 6 columns
                  baseMean log2FoldChange     lfcSE      stat      pvalue
                 <numeric>      <numeric> <numeric> <numeric>   <numeric>
ENSG00000000938 16292.6481      -0.967706  0.188265 -5.140117 2.74567e-07
ENSG00000002586  1719.5175       0.724006  0.196562  3.683353 2.30186e-04
ENSG00000002919   870.6417      -0.101169  0.161486 -0.626486 5.30996e-01
ENSG00000002933   266.6548       1.010147  0.274661  3.677795 2.35259e-04
ENSG00000003249    11.4328       0.647755  0.412732  1.569433 1.16547e-01
...                    ...            ...       ...       ...         ...
ENSG00000284882   121.6850      -0.427594  0.267027  -1.60132 1.09307e-01
ENSG00000284948    38.7903      -0.291125  0.234652  -1.24067 2.14728e-01
ENSG00000284977    73.4571      -1.049690  0.235115  -4.46458 8.02262e-06
ENSG00000285417    25.7393       0.490122  0.420899   1.16446 2.44236e-01
ENSG00000285444    17.3871       0.363210  0.351279   1.03397 3.01152e-01
                       padj
                  <numeric>
ENSG00000000938 2.39613e-05
ENSG00000002586 2.63737e-03
ENSG00000002919 6.61318e-01
ENSG00000002933 2.68493e-03
ENSG00000003249 2.29349e-01
...                     ...
ENSG00000284882 0.218554310
ENSG00000284948 0.353434564
ENSG00000284977 0.000245775
ENSG00000285417 0.386061932
ENSG00000285444 0.446699110

enrichedResultsSigora <- pathwayEnrichment(
    inputList=exampleDESeqResults,
    analysis="sigora",
    filterInput=TRUE,
    gpsRepo="default"
)

head(enrichedResultsSigora)

# A tibble: 6 × 12
  comparison       direction pathwayId pathwayName   pValue pValueAdjusted genes
  <chr>            <chr>     <chr>     <fct>          <dbl>          <dbl> <chr>
1 COVID Pos Over … Up        R-HSA-38… Chemokine … 7.04e-46       7.05e-43 CCR3…
2 COVID Pos Over … Up        R-HSA-19… Immunoregu… 5.46e-45       5.46e-42 CD1A…
3 COVID Pos Over … Up        R-HSA-38… Costimulat… 2.56e-40       2.56e-37 CD28…
4 COVID Pos Over … Up        R-HSA-67… Neutrophil… 7.88e-31       7.89e-28 ABCA…
5 COVID Pos Over … Up        R-HSA-20… Cell surfa… 3.55e-21       3.55e-18 ATP1…
6 COVID Pos Over … Up        R-HSA-14… Alpha-defe… 1.02e-20       1.02e-17 CD4;…
# ℹ 5 more variables: numCandidateGenes <dbl>, numBgGenes <int>,
#   geneRatio <dbl>, totalGenes <int>, topLevelPathway <chr>

For those who still prefer traditional over-representation analysis, we include the option of doing so by setting analysis="reactome", which uses ReactomePA (Yu et al. 2016). When using this method, we recommend providing a gene universe to serve as a background for the enrichment test; here we’ll use all the genes which were tested for significance by DESeq2 (i.e. all genes from the count matrix), converting them to Entrez gene IDs before running the test. See the full vignette at our Github pages for details.

In addition to the Reactome database used when setting analysis to “sigora” or “reactome”, we also provide over-representation analysis using the Hallmark gene sets from the Molecular Signatures Database (MSigDb). These are 50 gene sets that represent “specific, well-defined biological states or processes with coherent expression” (Liberzon et al. 2015). This database provides a more high-level summary of key biological processes compared to the more granular Reactome pathways.

enrichedResultsHm <- pathwayEnrichment(
    inputList=exampleDESeqResults,
    analysis="hallmark",
    filterInput=TRUE,
    split=TRUE
)

head(enrichedResultsHm)

# A tibble: 6 × 12
  comparison       direction pathwayId pathwayName   pValue pValueAdjusted genes
  <chr>            <chr>     <chr>     <chr>          <dbl>          <dbl> <chr>
1 COVID Pos Over … Up        HEME MET… HEME METAB… 2.69e-32       1.34e-30 SLC4…
2 COVID Pos Over … Up        IL2 STAT… IL2 STAT5 … 5.89e- 4       1.47e- 2 PLPP…
3 COVID Pos Over … Down      INTERFER… INTERFERON… 3.14e-30       1.48e-28 EIF2…
4 COVID Pos Over … Down      INTERFER… INTERFERON… 2.37e-28       5.58e-27 EIF2…
5 COVID Pos Over … Down      INFLAMMA… INFLAMMATO… 6.83e- 9       1.07e- 7 KIF1…
6 COVID Pos Over … Down      TNFA SIG… TNFA SIGNA… 1.75e- 7       2.06e- 6 MXD1…
# ℹ 5 more variables: numCandidateGenes <dbl>, numBgGenes <dbl>,
#   geneRatio <dbl>, totalGenes <int>, topLevelPathway <chr>

Finally, users may also use data from KEGG when performing enrichment analyses. This database is supported with traditional over-representation analysis by setting analysis="kegg", or with a gene pair-based approach by specifying analysis="sigora" and gpsRepo="kegH".

Plotting pathway enrichment results

Now that we have (a lot of) pathway enrichment results from multiple comparisons, its time to visualize them. The function plotPathways() does this by grouping Reactome pathways (or Hallmark gene sets) under parent groups, and indicates if each pathway is up- or down-regulated in each comparison, making it easy to identify which pathways are shared or unique to different DEG lists. Because there are often many pathways, you can split the plot into multiple columns (up to 3), and truncate the pathway names to make the results fit more easily.

Sometimes a pathway may be enriched in both up- and down-regulated genes from the same DEG list (these usually occur with larger pathways). Such occurrences are indicated by a white asterisk where the more significant (lower adjusted p-value) direction is displayed. You can also change the angle/labels of the comparisons, or add the number of DEGs in each comparison below the labels. Lastly, you can specify which pathways or top pathway groups to include for visualization.

pathwayPlots(
    pathwayEnrichmentResults=enrichedResultsSigora, 
    columns=2
)

A variety of tweaks can be applied to these plot as well:

• Only show immune-related pathways based on the top pathway grouping
• Change the colour scaling used for the adjusted p-values
• Condense the comparison names to better fit onto the plot, and make them display horizontally
• Add the number of DEGs used for enrichment below the comparison name
• Increase the truncation cutoff value of pathway names for more words

From these results, you can see that while many of the immune system pathways change in the same direction over time in COVID-19 and non-COVID-19 sepsis patients, a few unique ones stand out, mostly related to interferon signaling (“Interferon Signaling”, “Interferon gamma signaling”, “Interferon alpha/beta signaling”, “ISG15 antiviral mechanism”). This likely reflects an elevated early antiviral response in COVID-19 patients that decreased over time, compared to no change in non-COVID-19 sepsis patients.

Generating networks from enriched pathways

pathlinkR includes functions for turning the pathway enrichment results from either Reactome-based method (“sigora” or “reactomepa”) into networks, using the overlap of the genes assigned to each pathway to determine their similarity to one other. In these networks, each pathway is a node, with connections or edges between them determined via a distance measure. A threshold can be set, where two pathways with a minimum similarity measure are considered connected, and would have an edge drawn between their nodes.

We provide a pre-computed distance matrix of Reactome pathways, generated using Jaccard distance, but there is support for multiple distance measures to be used. Once this “foundation” of pathway interactions is created, a pathway network can be built using the createPathnet() function:

data("sigoraDatabase")

pathwayDistancesJaccard <- getPathwayDistances(pathwayData = sigoraDatabase)

startingPathways <- pathnetFoundation(
    mat=pathwayDistancesJaccard,
    maxDistance=0.8
)

# Get the enriched pathways from the "COVID Pos Over Time" comparison
exPathwayNetworkInput <- enrichedResultsSigora %>% 
    filter(comparison == "COVID Pos Over Time")

myPathwayNetwork <- pathnetCreate(
    pathwayEnrichmentResult=exPathwayNetworkInput,
    foundation=startingPathways
)

There are two options for visualization, the first being a static network:

pathnetGGraph(
    myPathwayNetwork,
    labelProp=0.1,
    nodeLabelSize=3,
    nodeLabelOverlaps=8,
    segColour="red",
    themeBaseSize = 12
)

Nodes (pathways) which are filled in are enriched pathways (i.e. those output by pathwayEnrichment()). Size of nodes is correlated with statistical significance, while edge thickness relates to the similarity of two connected pathways.

Though this type of visualization is useful, we can also display this network using an alternate method that creates an interactive display, with the function pathnetVisNetwork(); see our Github pages for these details.

Supplemental materials

Citations

An AY, Baghela AS, Falsafi R, Lee AH, Trahtemberg U, Baker AJ, dos Santos CC, Hancock REW. Severe COVID-19 and non-COVID-19 severe sepsis converge transcriptionally after a week in the intensive care unit, indicating common disease mechanisms. Front Immunol. 2023;6(14):1167917.

Fabregat A, Sidiropoulos K, Viteri G, Forner O, Marin-Garcia P, Arnau V, D’Eustachio P, Stein L, Hermjakob H. Reactome pathway analysis: a high-performance in-memory approach. BMC Bioinform. 2017;18:142.

Foroushani ABK, Brinkman FSL, Lynn DJ. Pathway-GPS and sigora: identifying relevant pathways based on the over-representation of their gene-pair signatures. PeerJ. 2013;1:e229.

Liberzon A, Birger C, Thorvaldsdóttir H, Ghandi M, Mesirov JP, Tamayo P. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 2015;1(6):417–25.

Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550.

Yu G, He QY. ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization. Mol Biosyst. 2016;12(2):477-9.

Session information

R version 4.4.2 (2024-10-31)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.1 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] DESeq2_1.47.1               SummarizedExperiment_1.37.0
 [3] Biobase_2.67.0              MatrixGenerics_1.19.0      
 [5] matrixStats_1.4.1           GenomicRanges_1.59.1       
 [7] GenomeInfoDb_1.43.1         IRanges_2.41.1             
 [9] S4Vectors_0.45.2            BiocGenerics_0.53.3        
[11] generics_0.1.3              pathlinkR_1.3.3            
[13] dplyr_1.1.4                 BiocStyle_2.35.0           

loaded via a namespace (and not attached):
  [1] RColorBrewer_1.1-3      sys_3.4.3               jsonlite_1.8.9         
  [4] shape_1.4.6.1           magrittr_2.0.3          ggtangle_0.0.4         
  [7] farver_2.1.2            rmarkdown_2.29          GlobalOptions_0.1.2    
 [10] fs_1.6.5                zlibbioc_1.52.0         vctrs_0.6.5            
 [13] memoise_2.0.1           ggtree_3.15.0           rstatix_0.7.2          
 [16] htmltools_0.5.8.1       S4Arrays_1.7.1          broom_1.0.7            
 [19] Formula_1.2-5           gridGraphics_0.5-1      SparseArray_1.7.2      
 [22] sass_0.4.9              bslib_0.8.0             htmlwidgets_1.6.4      
 [25] plyr_1.8.9              cachem_1.1.0            buildtools_1.0.0       
 [28] igraph_2.1.1            lifecycle_1.0.4         iterators_1.0.14       
 [31] pkgconfig_2.0.3         gson_0.1.0              Matrix_1.7-1           
 [34] R6_2.5.1                fastmap_1.2.0           GenomeInfoDbData_1.2.13
 [37] clue_0.3-66             aplot_0.2.3             enrichplot_1.27.1      
 [40] digest_0.6.37           colorspace_2.1-1        patchwork_1.3.0        
 [43] AnnotationDbi_1.69.0    RSQLite_2.3.8           ggpubr_0.6.0           
 [46] vegan_2.6-8             labeling_0.4.3          fansi_1.0.6            
 [49] httr_1.4.7              polyclip_1.10-7         abind_1.4-8            
 [52] mgcv_1.9-1              compiler_4.4.2          bit64_4.5.2            
 [55] withr_3.0.2             doParallel_1.0.17       backports_1.5.0        
 [58] BiocParallel_1.41.0     carData_3.0-5           viridis_0.6.5          
 [61] DBI_1.2.3               ggforce_0.4.2           R.utils_2.12.3         
 [64] ggsignif_0.6.4          MASS_7.3-61             DelayedArray_0.33.2    
 [67] rjson_0.2.23            permute_0.9-7           tools_4.4.2            
 [70] ape_5.8                 R.oo_1.27.0             glue_1.8.0             
 [73] nlme_3.1-166            GOSemSim_2.33.0         grid_4.4.2             
 [76] cluster_2.1.6           reshape2_1.4.4          fgsea_1.33.0           
 [79] gtable_0.3.6            R.methodsS3_1.8.2       tidyr_1.3.1            
 [82] data.table_1.16.2       car_3.1-3               tidygraph_1.3.1        
 [85] utf8_1.2.4              XVector_0.47.0          ggrepel_0.9.6          
 [88] foreach_1.5.2           pillar_1.9.0            stringr_1.5.1          
 [91] yulab.utils_0.1.8       circlize_0.4.16         splines_4.4.2          
 [94] tweenr_2.0.3            treeio_1.31.0           lattice_0.22-6         
 [97] bit_4.5.0               tidyselect_1.2.1        GO.db_3.20.0           
[100] ComplexHeatmap_2.23.0   locfit_1.5-9.10         maketools_1.3.1        
[103] Biostrings_2.75.1       knitr_1.49              gridExtra_2.3          
[106] xfun_0.49               graphlayouts_1.2.1      visNetwork_2.1.2       
[109] stringi_1.8.4           UCSC.utils_1.3.0        lazyeval_0.2.2         
[112] ggfun_0.1.7             yaml_2.3.10             evaluate_1.0.1         
[115] codetools_0.2-20        ggraph_2.2.1            tibble_3.2.1           
[118] qvalue_2.39.0           BiocManager_1.30.25     ggplotify_0.1.2        
[121] cli_3.6.3               munsell_0.5.1           jquerylib_0.1.4        
[124] Rcpp_1.0.13-1           png_0.1-8               parallel_4.4.2         
[127] ggplot2_3.5.1           blob_1.2.4              clusterProfiler_4.15.0 
[130] DOSE_4.1.0              tidytree_0.4.6          viridisLite_0.4.2      
[133] sigora_3.1.1            scales_1.3.0            purrr_1.0.2            
[136] crayon_1.5.3            GetoptLong_1.0.5        rlang_1.1.4            
[139] cowplot_1.1.3           fastmatch_1.1-4         KEGGREST_1.47.0