In chemokine/OmicsSBGN: Overlay omics data onto SBGN pathway diagram

IFNg dataset

In this example, we analyze a RNA-SEQ dataset of wild type mice and IFNg KO mice. It contains normalized RNA-seq gene expression data described in this study: Greer, Renee L., Xiaoxi Dong, et al. "Akkermansia muciniphila mediates negative effects of IFNg on glucose metabolism." Nature communications 2016.

Load RNA-seq data

The RNA abundance data was quantile normalized and log2 transformed, stored in a "SummarizedExperiment" object

library(SummarizedExperiment)
library(SBGNview)
library(SBGNview.data)
data("IFNg","sbgn.xmls")
count.data <- assays(IFNg)$counts
wt <- colnames(IFNg)[IFNg$group == "wt"]
ko <- colnames(IFNg)[IFNg$group == "ko"]
head(count.data)

Pathway enrichment analysis: run SBGNview gene set

Load gene list: mouse ensemble IDs.

ensemble.to.pathway <- get.mol.list(
    database = "pathwayCommons"
    ,mol.list.ID.type = "ENSEMBL"
    ,org = "mmu"
    ,cpd.or.gene = "gene"
    ,output.pathway.name = TRUE
)
head(ensemble.to.pathway[[2]])

Run gage

gage is an R package for pathway enrichment analysis.

library(gage)
degs <- gage(exprs = count.data
           ,gsets = ensemble.to.pathway
           ,ref = which(colnames(count.data) %in% wt)
           ,samp = which(colnames(count.data) %in% ko)
           ,compare = "as.group"
           )
head(degs$greater)[,c(1,4:5)]
head(degs$less)
down.pathways <- degs$less
head(down.pathways)

Visualize fold change on pathway maps

Generate fold change data for visualization.

The abundance table was log2 transformed. Here we calculate the fold change of KO group v.s. WT group.

mean.wt <- apply(count.data[,wt] ,1 ,"mean")
head(mean.wt)
mean.ko <- apply(count.data[,ko],1,"mean")
head(mean.ko)
ensemble.to.koVsWt <- mean.ko - mean.wt
head(ensemble.to.koVsWt)

Visualize fold change data

down.pathways <- do.call(rbind,strsplit(row.names(down.pathways),"::"))[,1]
head(down.pathways)
sbgnview.obj <- SBGNview(
    gene.data = ensemble.to.koVsWt,
    gene.id.type = "ENSEMBL",
    input.sbgn = down.pathways[1],
    output.file = "ifn.sbgnview.less",
    show.pathway.name = TRUE,
    max.gene.value = 2,
    min.gene.value = -2,
    mid.gene.value = 0,
    node.sum = "mean",
    label.spliting.string = c("-","_"," "), 
    output.format = c("png"),

    inhibition.edge.end.shift = 1,
    font.size = 2,
    logic.node.font.scale = 6,
    font.size.scale.compartment = 1.5,
    org = "mmu",

    text.length.factor.macromolecule = 0.8,
    text.length.factor.compartment = 2,
    text.length.factor.complex = 3,
    if.scale.compartment.font.size = TRUE,
    node.width.adjust.factor.compartment = 0.058 
)
sbgnview.obj

```rSBGNview"} library(knitr) include_graphics("ifn.sbgnview.less_R-HSA-877300_Interferon gamma signaling.svg")

### Visualize RNA abundance data: SummarizedExperiment object as input.
```r
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",
    label.spliting.string = c("-","_"," "), 
    output.format = c("png"),

    inhibition.edge.end.shift = 1,
    font.size = 2,
    logic.node.font.scale = 6,
    font.size.scale.compartment = 1.5,
    org = "hsa",

    text.length.factor.macromolecule = 0.8,
    text.length.factor.compartment = 2,
    text.length.factor.complex = 3,
    if.scale.compartment.font.size = TRUE,
    node.width.adjust.factor.compartment = 0.058 
   )
sbgnview.obj

```rSBGNview of a cancer dataset gse16873"} include_graphics("demo.SummarizedExperiment_R-HSA-877300_Interferon gamma signaling.svg")

``` {r, eval = FALSE, echo=FALSE}
rmarkdown::render("C:/xiaoxi/work/papers/SBGNview/package/SBGNview/vignettes/pathway.enrichment.analysis.Rmd")

chemokine/OmicsSBGN documentation built on June 27, 2019, 7:52 p.m.