## ----loadData, echo = TRUE, message = FALSE, warning = FALSE------------- library("pRoloc") library("pRolocdata") ## Subset data for markers for example data("hyperLOPIT2015") hyperLOPIT2015 <- markerMSnSet(hyperLOPIT2015) ## ----queryparams--------------------------------------------------------- params <- setAnnotationParams(inputs = c("Mus musculus", "UniProt/Swissprot")) ## ----addGO, echo = TRUE, message = FALSE, warning = FALSE, eval = TRUE---- cc <- addGoAnnotations(hyperLOPIT2015, params, namespace = "cellular_component") fvarLabels(cc) ## ----filterGO, echo = TRUE, message = FALSE, warning = FALSE, eval = TRUE---- ## Next we filter the GO term matrix removing any terms that have ## have less than `n` proteins or greater than `p` % of total proteins ## in the dataset (this removes terms that only have very few proteins ## and very general terms) cc <- filterMinMarkers(cc) cc <- filterMaxMarkers(cc) ## ----orderMarkers, eval = TRUE, verbose = FALSE-------------------------- ## Extract markers can use n to specify to select top n terms res <- orderGoAnnotations(cc, k = 1:3, p = 1/3, verbose = FALSE) ## ----viewGO, eval=FALSE-------------------------------------------------- ## library("pRolocGUI") ## pRolocVis(res, fcol = "GOAnnotations") ## ----clusterDist, eval = TRUE, verbose = FALSE--------------------------- ## Now calculate distances dd <- clustDist(cc, fcol = "GOAnnotations", k = 1:3, verbose = FALSE) dd[[1]] ## ----visualiseRes, fig.width=12, eval = TRUE----------------------------- ## Plot normalised distances plot(dd, p = 1/3) ## Examine kmeans clustering plot(dd[[1]], cc) ## ----minRank, eval = FALSE----------------------------------------------- ## ## Normalise by n^1/3 ## minDist <- getNormDist(dd, p = 1/3) ## ## ## Get new order according to lowest distance ## o <- order(minDist) ## ## ## Re-order `GOAnnotations` matrix in `fData` ## fData(cc)$GOAnnotations <- fData(cc)$GOAnnotations[, o] ## ----visAgain, eval=FALSE------------------------------------------------ ## pRolocVis(cc, fcol = "GOAnnotations")