## ----setup, include = FALSE--------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>", crop = NULL ## cf https://stat.ethz.ch/pipermail/bioc-devel/2020-April/016656.html ) ## ----hierarchy, message = FALSE, out.width = "600px"-------------------------- library(scp) data("scp1") plot(scp1) ## ----assay_data--------------------------------------------------------------- assay(scp1, "190321S_LCA10_X_FP97AG")[1:5, ] ## ----names-------------------------------------------------------------------- names(scp1) ## ----rowData------------------------------------------------------------------ rowData(scp1) rowData(scp1)[["proteins"]] ## ----rowDataNames------------------------------------------------------------- rowDataNames(scp1) ## ----rbindRowData------------------------------------------------------------- rbindRowData(scp1, i = 1:5) ## ----colData------------------------------------------------------------------ colData(scp1) ## ----colData_dollar----------------------------------------------------------- scp1$SampleType ## ----subset_assay------------------------------------------------------------- scp1[, , "190321S_LCA10_X_FP97AG"] ## ----subsetByAssay------------------------------------------------------------ subsetByAssay(scp1, "190321S_LCA10_X_FP97AG") ## ----subset_samples----------------------------------------------------------- scp1[, scp1$SampleType == "Macrophage", ] ## ----subsetByColData---------------------------------------------------------- subsetByColData(scp1, scp1$SampleType == "Macrophage") ## ----subset_features---------------------------------------------------------- scp1["Q02878", , ] ## ----subsetByFeature---------------------------------------------------------- subsetByFeature(scp1, "Q02878") ## ----filterFeatures----------------------------------------------------------- filterFeatures(scp1, ~ Reverse != "+") ## ----filterNA----------------------------------------------------------------- filterNA(scp1, i = "proteins", pNA = 0.7) ## ----zeroIsNA----------------------------------------------------------------- table(assay(scp1, "peptides") == 0) scp1 <-zeroIsNA(scp1, "peptides") table(assay(scp1, "peptides") == 0) ## ----aggregateFeatures-------------------------------------------------------- aggregateFeatures(scp1, i = "190321S_LCA10_X_FP97AG", fcol = "protein", name = "190321S_LCA10_X_FP97AG_aggr", fun = MsCoreUtils::robustSummary) ## ----normalize---------------------------------------------------------------- normalize(scp1, "proteins", method = "center.mean", name = "proteins_mcenter") ## ----sweep-------------------------------------------------------------------- sf <- colSums(assay(scp1, "proteins"), na.rm = TRUE) / 1E4 sweep(scp1, i = "proteins", MARGIN = 2, ## 1 = by feature; 2 = by sample STATS = sf, FUN = "/", name = "proteins_sf") ## ----logTransform------------------------------------------------------------- logTransform(scp1, i = "proteins", base = 2, pc = 1, name = "proteins_log") ## ----impute------------------------------------------------------------------- anyNA(assay(scp1, "proteins")) scp1 <- impute(scp1, i = "proteins", method ="knn", k = 3) anyNA(assay(scp1, "proteins")) ## ----vis1, message = FALSE, fig.width = 6.5----------------------------------- rd <- rbindRowData(scp1, i = 1:3) library("ggplot2") ggplot(data.frame(rd)) + aes(y = PIF, x = assay) + geom_boxplot() ## ----longFormat--------------------------------------------------------------- lf <- longFormat(scp1[, , 1], colvars = c("SampleType", "Channel")) ggplot(data.frame(lf)) + aes(x = Channel, y = value, colour = SampleType) + geom_boxplot() ## ----setup2, include = FALSE-------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "", crop = NULL ) ## ----sessioninfo, echo=FALSE-------------------------------------------------- sessionInfo()