## ---- echo = TRUE, results = 'hide', message = FALSE, warning = FALSE--------- 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$group == "ko") ## ---- echo = TRUE , results = 'hide', message = FALSE, warning = FALSE------ ensembl.pathway <- sbgn.gsets(id.type = "ENSEMBL", species = "mmu", mol.type = "gene", output.pathway.name = TRUE ) head(ensembl.pathway[[2]]) ## ---- echo = TRUE, results = 'hide', message = FALSE, warning = FALSE--------- if(!requireNamespace("gage", quietly = TRUE)) { BiocManager::install("gage", update = FALSE) } library(gage) degs <- gage(exprs = count.data, gsets = ensembl.pathway, ref = wt.cols, samp = ko.cols, compare = "paired" #"as.group" ) head(degs$greater)[,3:5] head(degs$less)[,3:5] down.pathways <- row.names(degs$less)[1:10] head(down.pathways) ## ---- echo = TRUE, results = 'hide', message = FALSE, warning = FALSE--------- 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 ## ---- echo = TRUE, results = 'hide', message = FALSE, warning = FALSE--------- #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 ## ----ifng, echo = FALSE,fig.cap="\\label{fig:ifng}SBGNview graph of the most down-regulated pathways in IFNg KO experiment"---- library(knitr) include_graphics("ifn.sbgnview.less_R-HSA-877300_Interferon gamma signaling.svg") ## ----ifna, echo = FALSE,fig.cap="\\label{fig:ifna}SBGNview graph of the second most down-regulated pathways in IFNg KO experiment"---- library(knitr) include_graphics("ifn.sbgnview.less_R-HSA-909733_Interferon alpha_beta signaling.svg") ## ---- echo = TRUE, results = 'hide', message = FALSE, warning = FALSE--------- 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 ## ----cancerds, echo = FALSE,fig.cap="\\label{fig:cancerds}SBGNview of a cancer dataset gse16873"---- include_graphics("demo.SummarizedExperiment_R-HSA-877300_Interferon gamma signaling.svg") ## ----------------------------------------------------------------------------- sessionInfo()