# SBGNview Based Pathway Analysis and Visualization Workflow

#### Xiaoxi Dong

#### Kovidh Vegesna, kvegesna (AT) uncc.edu

#### Weijun Luo, luo_weijun (AT) yahoo.com

#### 01 March, 2021

# 1 Introduction

SBGNview has collected pathway data and gene sets from the following databases: Reactome, PANTHER Pathway, SMPDB, MetaCyc and MetaCrop. These gene sets can be used for pathway enrichment analysis.

In this vignette, we will show you a complete pathway analysis workflow based on GAGE + SBGNview. Similar workflows have been documented in the gage package using GAGE + Pathview.

# 2 Citation

Please cite the following papers when using the open-source SBGNview package. This will help the project and our team:

Luo W, Brouwer C. Pathview: an R/Biocondutor package for pathway-based data integration and visualization. Bioinformatics, 2013, 29(14):1830-1831, doi: 10.1093/bioinformatics/btt285

Please also cite the GAGE paper when using the gage package:

Luo W, Friedman M, etc. GAGE: generally applicable gene set enrichment for pathway analysis. BMC Bioinformatics, 2009, 10, pp. 161, doi: 10.1186/1471-2105-10-161

# 3 Installation and quick start

Please see the Quick Start tutorial for installation instructions and quick start examples.

# 4 Complete pathway analysis + visualization workflow

In this example, we analyze a RNA-Seq dataset of IFNg KO mice vs wild type mice. It contains normalized RNA-seq gene expression data described in Greer, Renee L., Xiaoxi Dong, et al, 2016.

## 4.1 Load the gene (RNA-seq) data

The RNA abundance data was quantile normalized and log2 transformed, stored in a “SummarizedExperiment” object. SBGNview input user data (gene.data or cpd.data) can be either a numeric matrix or a vector, like those in pathview. In addition, it can be a “SummarizedExperiment” object, which is commonly used in BioConductor packages.

    library(SBGNview)
    library(SummarizedExperiment)
    data("IFNg", "pathways.info")
    count.data <- assays(IFNg)$counts
    head(count.data)
    wt.cols <- which(IFNg$group == "wt")
    ko.cols <- which(IFNg$group == "ko")

## 4.2 Gene sets from SBGNview pathway collection

### 4.2.1 Load gene set for mouse with ENSEMBL gene IDs

    ensembl.pathway <- sbgn.gsets(id.type = "ENSEMBL",
                                  species = "mmu",
                                  mol.type = "gene",
                                  output.pathway.name = TRUE
                                  )
    head(ensembl.pathway[[2]])

### 4.2.2 Pathway or gene set analysis using GAGE

    library(gage)
    degs <- gage(exprs = count.data,
               gsets = ensembl.pathway,
               ref = wt.cols,
               samp = ko.cols,
               compare = "paired" #"as.group"
               )
    head(degs$greater)[,3:5]
    head(degs$less)[,3:5]
    down.pathways <- row.names(degs$less)[1:10]
    head(down.pathways)

## 4.3 Visualize perturbations in top SBGN pathways

### 4.3.1 Calculate fold changes or gene perturbations

The abundance values were log2 transformed. Here we calculate the fold change of IFNg KO group v.s. WT group.

    ensembl.koVsWt <- count.data[,ko.cols]-count.data[,wt.cols]
    head(ensembl.koVsWt)

    #alternatively, we can also calculate mean fold changes per gene, which corresponds to gage analysis above with compare="as.group"
    mean.wt <- apply(count.data[,wt.cols] ,1 ,"mean")
    head(mean.wt)
    mean.ko <- apply(count.data[,ko.cols],1,"mean")
    head(mean.ko)
    # The abundance values were on log scale. Hence fold change is their difference.
    ensembl.koVsWt.m <- mean.ko - mean.wt

### 4.3.2 Visualize pathway perturbations by SBNGview

    #load the SBGNview pathway collection, which may takes a few seconds.
    data(sbgn.xmls)
    down.pathways <- sapply(strsplit(down.pathways,"::"), "[", 1)
    head(down.pathways)
    sbgnview.obj <- SBGNview(
        gene.data = ensembl.koVsWt,
        gene.id.type = "ENSEMBL",
        input.sbgn = down.pathways[1:2],#can be more than 2 pathways
        output.file = "ifn.sbgnview.less",
        show.pathway.name = TRUE,
        max.gene.value = 2,
        min.gene.value = -2,
        mid.gene.value = 0,
        node.sum = "mean",
        output.format = c("png"),

        font.size = 2.3,
        org = "mmu",

        text.length.factor.complex = 3,
        if.scale.compartment.font.size = TRUE,
        node.width.adjust.factor.compartment = 0.04
    )
    sbgnview.obj

Figure 4.1: SBGNview graph of the most down-regulated pathways in IFNg KO experiment

Figure 4.2: SBGNview graph of the second most down-regulated pathways in IFNg KO experiment

## 4.4 SBGNview with SummarizedExperiment object

The ‘cancer.ds’ is a microarray dataset from a breast cancer study. The dataset was adopted from gage package and processed into a SummarizedExperiment object. It is used to demo SBGNview’s visualization ability.

    data("cancer.ds")
    sbgnview.obj <- SBGNview(
        gene.data = cancer.ds,
        gene.id.type = "ENTREZID",
        input.sbgn = "R-HSA-877300",
        output.file = "demo.SummarizedExperiment",
        show.pathway.name = TRUE,
        max.gene.value = 1,
        min.gene.value = -1,
        mid.gene.value = 0,
        node.sum = "mean",
        output.format = c("png"),

        font.size = 2.3,
        org = "hsa",

        text.length.factor.complex = 3,
        if.scale.compartment.font.size = TRUE,
        node.width.adjust.factor.compartment = 0.04
       )
    sbgnview.obj

Figure 4.3: SBGNview of a cancer dataset gse16873

# 5 Session Info

    sessionInfo()

    ## R version 4.0.4 (2021-02-15)
    ## Platform: x86_64-w64-mingw32/x64 (64-bit)
    ## Running under: Windows Server 2012 R2 x64 (build 9600)
    ##
    ## Matrix products: default
    ##
    ## locale:
    ## [1] LC_COLLATE=C
    ## [2] LC_CTYPE=English_United States.1252
    ## [3] LC_MONETARY=English_United States.1252
    ## [4] LC_NUMERIC=C
    ## [5] LC_TIME=English_United States.1252
    ##
    ## attached base packages:
    ## [1] parallel  stats4    stats     graphics  grDevices utils     datasets
    ## [8] methods   base
    ##
    ## other attached packages:
    ##  [1] gage_2.40.1                 SummarizedExperiment_1.20.0
    ##  [3] Biobase_2.50.0              GenomicRanges_1.42.0
    ##  [5] GenomeInfoDb_1.26.3         IRanges_2.24.1
    ##  [7] S4Vectors_0.28.1            BiocGenerics_0.36.0
    ##  [9] MatrixGenerics_1.2.1        matrixStats_0.58.0
    ## [11] SBGNview_1.4.1              SBGNview.data_1.4.1
    ## [13] pathview_1.30.1             knitr_1.31
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] KEGGgraph_1.50.0       Rcpp_1.0.6             lattice_0.20-41
    ##  [4] GO.db_3.12.1           png_0.1-7              rsvg_2.1
    ##  [7] Biostrings_2.58.0      digest_0.6.27          R6_2.5.0
    ## [10] RSQLite_2.2.3          evaluate_0.14          httr_1.4.2
    ## [13] highr_0.8              Rdpack_2.1.1           zlibbioc_1.36.0
    ## [16] rlang_0.4.10           Rgraphviz_2.34.0       jquerylib_0.1.3
    ## [19] blob_1.2.1             Matrix_1.3-2           rmarkdown_2.7
    ## [22] stringr_1.4.0          igraph_1.2.6           RCurl_1.98-1.2
    ## [25] bit_4.0.4              DelayedArray_0.16.2    compiler_4.0.4
    ## [28] xfun_0.21              pkgconfig_2.0.3        htmltools_0.5.1.1
    ## [31] KEGGREST_1.30.1        GenomeInfoDbData_1.2.4 bookdown_0.21
    ## [34] XML_3.99-0.5           crayon_1.4.1           bitops_1.0-6
    ## [37] rbibutils_2.0          grid_4.0.4             jsonlite_1.7.2
    ## [40] DBI_1.1.1              magrittr_2.0.1         graph_1.68.0
    ## [43] stringi_1.5.3          cachem_1.0.4           XVector_0.30.0
    ## [46] xml2_1.3.2             bslib_0.2.4            vctrs_0.3.6
    ## [49] org.Hs.eg.db_3.12.0    tools_4.0.4            bit64_4.0.5
    ## [52] fastmap_1.1.0          yaml_2.2.1             AnnotationDbi_1.52.0
    ## [55] memoise_2.0.0          sass_0.3.1