## ---- fig.show='hold', message=FALSE, warning=FALSE, eval=FALSE------------ # ## try http:// if https:// URLs are not supported # source("https://bioconductor.org/biocLite.R") # biocLite("hipathia") ## ---- fig.show='hold', message=FALSE, warning=FALSE------------------------ library(hipathia) data("brca") brca ## ---- fig.show='hold', message=FALSE, warning=FALSE------------------------ hhead(assay(brca), 4) ## ---- fig.show='hold', message=FALSE, warning=FALSE------------------------ colData(brca) ## ---- fig.show='hold', message=FALSE, eval=FALSE--------------------------- # brca <- SummarizedExperiment(assays=SimpleList(raw=brca_data), # colData=brca_design) ## ---- fig.show='hold'------------------------------------------------------ data(brca_data) trans_data <- translate_data(brca_data, "hsa") ## ---- fig.show='hold'------------------------------------------------------ exp_data <- normalize_data(trans_data) ## ---- fig.show='hold', fig.cap="BRCA data before scaling"------------------ boxplot(trans_data) ## ---- fig.show='hold', fig.cap="BRCA data after scaling"------------------- boxplot(exp_data) ## ---- fig.show='hold', fig.cap="BRCA data after a Quantiles normalization"---- exp_data <- normalize_data(trans_data, by_quantiles = TRUE) boxplot(exp_data) ## ---- fig.show='hold', fig.cap="BRCA data after normalizing by percentil"---- exp_data <- normalize_data(trans_data, percentil = TRUE) boxplot(exp_data) ## ---- fig.show='hold', fig.cap="BRCA data after truncating by percentil 0.95"---- exp_data <- normalize_data(trans_data, truncation_percentil = 0.95) boxplot(exp_data) ## ---- fig.show='hold'------------------------------------------------------ pathways <- load_pathways(species = "hsa") ## ---- fig.show='hold'------------------------------------------------------ pathways_only2 <- load_pathways(species = "hsa", pathways_list = c("hsa03320", "hsa04014")) ## ---- fig.show='hold'------------------------------------------------------ length(get_pathways_list(pathways)) get_pathways_list(pathways)[1:10] ## ---- fig.show='hold'------------------------------------------------------ length(get_pathways_list(pathways_only2)) get_pathways_list(pathways_only2) ## ---- fig.show='hold'------------------------------------------------------ results <- hipathia(exp_data, pathways, decompose = FALSE, verbose=FALSE) ## ---- fig.show='hold'------------------------------------------------------ results ## ---- fig.show='hold'------------------------------------------------------ path_vals <- get_paths_data(results, matrix = TRUE) path_vals <- get_paths_data(results) hhead(path_vals, 4) ## ---- echo=FALSE, results='asis'------------------------------------------- tab <- t(sapply(c("hsa", "mmu", "rno"), function(species){ p <- suppressMessages(load_pathways(species)) effs <- sum(sapply(p$pathigraphs, function(pathi) length( pathi$effector.subgraphs))) decs <- sum(sapply(p$pathigraphs, function(pathi) length(pathi$subgraphs))) n <- length(p$pathigraphs) c(n, effs, decs) })) colnames(tab) <- c("Pathways", "Effector subpathways", "Decomposed subpathways") knitr::kable(tab) ## ---- fig.show='hold'------------------------------------------------------ uniprot_vals <- quantify_terms(results, pathways, dbannot = "uniprot") go_vals <- quantify_terms(results, pathways, dbannot = "GO") ## ---- fig.show='hold'------------------------------------------------------ sample_group <- brca_design[colnames(path_vals),"group"] comp <- do_wilcoxon(path_vals, sample_group, g1 = "Tumor", g2 = "Normal") hhead(comp) ## ---- fig.show='hold'------------------------------------------------------ pathways_summary <- get_pathways_summary(comp, pathways) head(pathways_summary, 4) ## ---- fig.show='hold'------------------------------------------------------ ranked_path_vals <- path_vals[order(comp$p.value, decreasing = FALSE),] pca_model <- do_pca(ranked_path_vals[1:ncol(ranked_path_vals),]) ## ---- fig.show='hold', fig.cap="Heatmap plot", fig.small=TRUE-------------- heatmap_plot(path_vals, group = sample_group) ## ---- fig.show='hold', fig.cap="Heatmap plots with variable clustering", fig.small=TRUE---- heatmap_plot(uniprot_vals, group = sample_group, colors="hipathia", variable_clust = TRUE) ## ---- fig.show='hold', fig.cap="Different colors of heatmaps: `redgreen`", fig.small=TRUE---- heatmap_plot(go_vals, group = sample_group, colors="redgreen", variable_clust = TRUE) ## ---- fig.show='hold', fig.cap="PCA plot"---------------------------------- pca_plot(pca_model, sample_group, legend = TRUE) ## ---- fig.show='hold', fig.cap="PCA plot with 5 random colors"------------- pca_plot(pca_model, group = rep(1:5, 8), main = "Random types", legend = TRUE) ## ---- fig.show='hold', fig.cap="Multiple PCA plot with acumulated explained variance"---- multiple_pca_plot(pca_model, sample_group, cex=3, plot_variance = TRUE) ## ---- fig.show='hold', fig.cap= "Pathway comparison plot without node colors"---- pathway_comparison_plot(comp, metaginfo = pathways, pathway = "hsa03320") ## ---- fig.show='hold', fig.cap="Pathway comparison plot with node colors: `classic`"---- colors_de <- node_color_per_de(results, pathways, sample_group, "Tumor", "Normal") pathway_comparison_plot(comp, metaginfo = pathways, pathway = "hsa03320", node_colors = colors_de) ## ---- fig.show='hold', fig.cap="Pathway comparison plot with node colors: `hipathia`"---- colors_de_hipathia <- node_color_per_de(results, pathways, sample_group, "Tumor", "Normal", colors = "hipathia") pathway_comparison_plot(comp, metaginfo = pathways, pathway = "hsa03320", node_colors = colors_de_hipathia, colors = "hipathia") ## ---- fig.show='hold'------------------------------------------------------ report <- create_report(comp, pathways, "save_noColors") report_colors <- create_report(comp, pathways, "save_colors", node_colors = colors_de) ## ---- fig.show='hold'------------------------------------------------------ visualize_report(report_colors) ## ---- fig.show='hold'------------------------------------------------------ visualize_report(report, port = 4001) ## ---- fig.show='hold'------------------------------------------------------ servr::daemon_stop() ## ---- fig.show='hold', eval=FALSE------------------------------------------ # colors_de_uni <- node_color_per_de(results, pathways, sample_group, "Tumor", # "Normal", group_by = "uniprot") # create_report(comp, pathways, "save_colors_uniprot", # node_colors = colors_de_uni, group_by = "uniprot") # visualize_report("save_colors_uniprot", port = 4002) ## ---- fig.show='hold'------------------------------------------------------ servr::daemon_stop() ## ---- fig.show='hold', message=FALSE, warning=FALSE------------------------ class(brca) hhead(brca, 4) ## ---- fig.show='hold', message=FALSE, warning=FALSE------------------------ class(assay(brca)) hhead(assay(brca), 4) ## ---- fig.show='hold'------------------------------------------------------ get_path_names(pathways, c("P-hsa03320-37", "P-hsa04010-15"))