## ----setup, include=FALSE------------------------------------------------ knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE, fig.width = 8) library(lipidr) library(ggplot2) ## ------------------------------------------------------------------------ datadir = system.file("extdata", package="lipidr") filelist = list.files(datadir, "data.csv", full.names = TRUE) # all csv files d = read_skyline(filelist) print(d) ## ------------------------------------------------------------------------ clinical_file = system.file("extdata", "clin.csv", package="lipidr") d = add_sample_annotation(d, clinical_file) colData(d) ## ------------------------------------------------------------------------ d_subset = d[1:10, 1:10] rowData(d_subset) colData(d) ## ------------------------------------------------------------------------ d_qc = d[, d$group == "QC"] rowData(d_qc) colData(d_qc) ## ------------------------------------------------------------------------ pc_lipids = rowData(d)$Class %in% c("PC", "PCO", "PCP") d_pc = d[pc_lipids,] rowData(d_pc) colData(d_pc) ## ------------------------------------------------------------------------ lipid_classes = rowData(d)$Class %in% c("Cer", "PC", "LPC") groups = d$BileAcid != "DCA" d = d[lipid_classes, groups] #QC sample subset d_qc = d[, d$group == "QC"] ## ---- fig.height=6------------------------------------------------------- plot_samples(d, type = "tic", log = TRUE) ## ---- fig.width=10, fig.height=6----------------------------------------- plot_molecules(d_qc, "sd", measure = "Retention.Time", log = FALSE) plot_molecules(d_qc, "cv", measure = "Area") ## ---- fig.height=5------------------------------------------------------- plot_lipidclass(d, "boxplot") ## ------------------------------------------------------------------------ d_summarized = summarize_transitions(d, method = "average") ## ---- fig.height=6------------------------------------------------------- d_normalized = normalize_pqn(d_summarized, measure = "Area", exclude = "blank", log = TRUE) plot_samples(d_normalized, "boxplot") ## ---- fig.height=6, eval=FALSE------------------------------------------- # d_normalized_istd = normalize_istd(d_summarized, measure = "Area", exclude = "blank", log = TRUE) ## ---- fig.width=6, fig.height=5------------------------------------------ mvaresults = mva(d_normalized, measure="Area", method="PCA") plot_mva(mvaresults, color_by="group", components = c(1,2)) ## ------------------------------------------------------------------------ mvaresults = mva(d_normalized, method = "OPLS-DA", group_col = "Diet", groups=c("HighFat", "Normal")) plot_mva(mvaresults, color_by="group") ## ---- eval=FALSE--------------------------------------------------------- # plot_mva_loadings(mvaresults, color_by="Class", top.n=10) ## ------------------------------------------------------------------------ top_lipids(mvaresults, top.n=10) ## ------------------------------------------------------------------------ de_results = de_analysis( data=d_normalized, HighFat_water - NormalDiet_water, measure="Area" ) head(de_results) significant_molecules(de_results) ## ---- fig.height=6------------------------------------------------------- plot_results_volcano(de_results, show.labels = FALSE) ## ---- eval=FALSE--------------------------------------------------------- # # Using formula # de_design( # data=d_normalized, # design = ~ group, # coef="groupHighFat_water", # measure="Area") # # # Using design matrix # design = model.matrix(~ group, data=colData(d_normalized)) # de_design( # data=d_normalized, # design = design, # coef="groupHighFat_water", # measure="Area") ## ------------------------------------------------------------------------ enrich_results = lsea(de_results, rank.by = "logFC") significant_lipidsets(enrich_results) ## ---- fig.width=6, fig.height=5------------------------------------------ plot_class_enrichment(de_results, significant_lipidsets(enrich_results)) ## ---- fig.height=8------------------------------------------------------- plot_chain_distribution(de_results) ## ------------------------------------------------------------------------ sessionInfo()