## ----style, echo = FALSE, results = 'asis'------------------------------------ BiocStyle::markdown() ## ----env, message = FALSE, warning = FALSE, echo = FALSE---------------------- library("QFeatures") ## ----featuresplot, fig.cap = "Conceptual representation of a `QFeatures` object and the aggregative relation between different assays.", echo = FALSE---- par(mar = c(0, 0, 0, 0)) plot(NA, xlim = c(0, 12), ylim = c(0, 20), xaxt = "n", yaxt = "n", xlab = "", ylab = "", bty = "n") for (i in 0:7) rect(0, i, 3, i+1, col = "lightgrey", border = "white") for (i in 8:12) rect(0, i, 3, i+1, col = "steelblue", border = "white") for (i in 13:18) rect(0, i, 3, i+1, col = "orange", border = "white") for (i in 19) rect(0, i, 3, i+1, col = "darkgrey", border = "white") for (i in 5:7) rect(5, i, 8, i+1, col = "lightgrey", border = "white") for (i in 8:10) rect(5, i, 8, i+1, col = "steelblue", border = "white") for (i in 11:13) rect(5, i, 8, i+1, col = "orange", border = "white") for (i in 14) rect(5, i, 8, i+1, col = "darkgrey", border = "white") rect(9, 8, 12, 8+1, col = "lightgrey", border = "white") rect(9, 9, 12, 9+1, col = "steelblue", border = "white") rect(9, 10, 12, 10+1, col = "orange", border = "white") rect(9, 11, 12, 11+1, col = "darkgrey", border = "white") segments(3, 8, 5, 8, lty = "dashed") segments(3, 6, 5, 7, lty = "dashed") segments(3, 4, 5, 6, lty = "dashed") segments(3, 0, 5, 5, lty = "dashed") segments(3, 10, 5, 9, lty = "dashed") segments(3, 11, 5, 10, lty = "dashed") segments(3, 13, 5, 11, lty = "dashed") segments(3, 14, 5, 12, lty = "dashed") segments(3, 16, 5, 13, lty = "dashed") segments(3, 19, 5, 14, lty = "dashed") segments(3, 20, 5, 15, lty = "dashed") segments(8, 5, 9, 8, lty = "dashed") segments(8, 8, 9, 9, lty = "dashed") segments(8, 11, 9, 10, lty = "dashed") segments(8, 14, 9, 11, lty = "dashed") segments(8, 15, 9, 12, lty = "dashed") ## ----loadpkg------------------------------------------------------------------ library("QFeatures") ## ----loaddfr, echo = FALSE---------------------------------------------------- data(hlpsms) ## ----readQFeatures------------------------------------------------------------ data(hlpsms) hl <- readQFeatures(hlpsms, ecol = 1:10, name = "psms") hl ## ----subsetassay-------------------------------------------------------------- hl[[1]] hl[["psms"]] head(assay(hl[["psms"]])) head(rowData(hl[["psms"]])) ## ----aggregateFeatures1------------------------------------------------------- hl <- aggregateFeatures(hl, "psms", "Sequence", name = "peptides", fun = colMeans) hl hl[["peptides"]] ## ----aggregateFeatures2------------------------------------------------------- hl <- aggregateFeatures(hl, "peptides", "ProteinGroupAccessions", name = "proteins", fun = colMeans) hl hl[["proteins"]] ## ----------------------------------------------------------------------------- colData(hl) hl$tag <- c("126", "127N", "127C", "128N", "128C", "129N", "129C", "130N", "130C", "131") colData(hl) ## ----rowDataNames------------------------------------------------------------- rowDataNames(hl) ## ----rowData------------------------------------------------------------------ rowData(hl) ## ----rbindRowData------------------------------------------------------------- rbindRowData(hl, i = c("peptides", "proteins")) ## ----------------------------------------------------------------------------- dF <- DataFrame(mean = rowSums(assay(hl[["proteins"]])), sd = rowSds(assay(hl[["proteins"]]))) ## ----------------------------------------------------------------------------- rowData(hl) <- List(proteins = dF) ## ----------------------------------------------------------------------------- rowData(hl)[["proteins"]] ## ----stat3-------------------------------------------------------------------- stat3 <- hl["P42227-2", , ] stat3 ## ----plotstat3---------------------------------------------------------------- stat3_df <- data.frame(longFormat(stat3)) stat3_df$assay <- factor(stat3_df$assay, levels = c("psms", "peptides", "proteins")) library("ggplot2") ggplot(data = stat3_df, aes(x = colname, y = value, group = rowname)) + geom_line() + geom_point() + facet_grid(~ assay) ## ----stat--------------------------------------------------------------------- stat <- hl[c("P42227-2", "P42225"), , ] stat ## ----plotstat----------------------------------------------------------------- stat_df <- data.frame(longFormat(stat)) stat_df$stat3 <- ifelse(stat_df$rowname %in% stat3_df$rowname, "STAT3", "STAT1") stat_df$assay <- factor(stat_df$assay, levels = c("psms", "peptides", "proteins")) ggplot(data = stat_df, aes(x = colname, y = value, group = rowname)) + geom_line() + geom_point() + facet_grid(stat3 ~ assay) ## ----subsetByFeature---------------------------------------------------------- hl |> subsetByFeature("P42227-2") hl |> subsetByFeature(c("P42227-2", "P42225")) ## ----subsetpipe, eval = FALSE------------------------------------------------- # hl |> # subsetByFeature("P42227-2") |> # longFormat() |> # as.data.frame() |> # ggplot(aes(x = colname, # y = value, # group = rowname)) + # geom_line() + # facet_grid(~ assay) ## ----varfilter---------------------------------------------------------------- mito_filter <- VariableFilter(field = "markers", value = "Mitochondrion", condition = "==") mito_filter qval_filter <- VariableFilter(field = "qValue", value = 0.001, condition = "<=") qval_filter ## ----mito_filter-------------------------------------------------------------- filterFeatures(hl, mito_filter) ## ----qval_filter-------------------------------------------------------------- filterFeatures(hl, qval_filter) ## ----formula_filter----------------------------------------------------------- filterFeatures(hl, ~ markers == "Mitochondrion") filterFeatures(hl, ~ qValue <= 0.001) ## ----sessioninfo, echo=FALSE-------------------------------------------------- sessionInfo()