## ----setup, include = FALSE----------------------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>", error = FALSE, warning = FALSE, eval = TRUE, message = FALSE, fig.width = 10 ) options(width = 100) stopifnot(requireNamespace("htmltools")) htmltools::tagList(rmarkdown::html_dependency_font_awesome()) ## ----install, eval=FALSE-------------------------------------------------------------------------- # if (!requireNamespace("BiocManager", quietly = TRUE)) # install.packages("BiocManager") # # BiocManager::install("GeneTonic") ## ----loadlib, eval = TRUE------------------------------------------------------------------------- library("GeneTonic") ## ----launchapp, eval=FALSE------------------------------------------------------------------------ # GeneTonic(dds = dds_object, # res_de = res_de_object, # res_enrich = res_enrich_object, # annotation_obj = annotation_object, # project_id = "myFirstGeneTonic") ## ----examplerun, eval=FALSE----------------------------------------------------------------------- # example("GeneTonic", ask = FALSE) ## ----create_dds, eval=TRUE------------------------------------------------------------------------ library("macrophage") library("DESeq2") data("gse", package = "macrophage") dds_macrophage <- DESeqDataSet(gse, design = ~line + condition) # changing the ids to Ensembl instead of the Gencode used in the object rownames(dds_macrophage) <- substr(rownames(dds_macrophage), 1, 15) dds_macrophage ## ----create_resde1, eval = TRUE------------------------------------------------------------------- keep <- rowSums(counts(dds_macrophage) >= 10) >= 6 dds_macrophage <- dds_macrophage[keep, ] dds_macrophage ## ----create_resde2, eval = FALSE------------------------------------------------------------------ # dds_macrophage <- DESeq(dds_macrophage) # # vst_macrophage <- vst(dds_macrophage) # res_macrophage_IFNg_vs_naive <- results(dds_macrophage, # contrast = c("condition", "IFNg", "naive"), # lfcThreshold = 1, alpha = 0.05) # res_macrophage_IFNg_vs_naive$SYMBOL <- rowData(dds_macrophage)$SYMBOL ## ----load_resde, eval=TRUE------------------------------------------------------------------------ ## To speed up the operations in the vignette, we can also load this object directly data("res_de_macrophage") head(res_macrophage_IFNg_vs_naive) ## ----create_resenrich1, eval=TRUE----------------------------------------------------------------- library("AnnotationDbi") de_symbols_IFNg_vs_naive <- deseqresult2df(res_macrophage_IFNg_vs_naive, FDR = 0.05)$SYMBOL bg_ids <- rowData(dds_macrophage)$SYMBOL[rowSums(counts(dds_macrophage)) > 0] ## ----create_resenrich2, eval=FALSE---------------------------------------------------------------- # library("topGO") # topgoDE_macrophage_IFNg_vs_naive <- # pcaExplorer::topGOtable(de_symbols_IFNg_vs_naive, # bg_ids, # ontology = "BP", # mapping = "org.Hs.eg.db", # geneID = "symbol", # topTablerows = 500) ## ----load_resenrich, eval=TRUE-------------------------------------------------------------------- ## To speed up the operations in the vignette, we also load this object directly data("res_enrich_macrophage") head(topgoDE_macrophage_IFNg_vs_naive, 2) ## ----convert_resenrich, eval=TRUE----------------------------------------------------------------- res_enrich_macrophage <- shake_topGOtableResult(topgoDE_macrophage_IFNg_vs_naive) colnames(res_enrich_macrophage) ## ----create_anno, eval=TRUE----------------------------------------------------------------------- library("org.Hs.eg.db") anno_df <- data.frame( gene_id = rownames(dds_macrophage), gene_name = mapIds(org.Hs.eg.db, keys = rownames(dds_macrophage), column = "SYMBOL", keytype = "ENSEMBL"), stringsAsFactors = FALSE, row.names = rownames(dds_macrophage) ) ## alternatively: # anno_df <- pcaExplorer::get_annotation_orgdb(dds_macrophage, "org.Hs.eg.db", "ENSEMBL") ## ----aggr_enrich, eval=TRUE----------------------------------------------------------------------- res_enrich_macrophage <- get_aggrscores(res_enrich = res_enrich_macrophage, res_de = res_macrophage_IFNg_vs_naive, annotation_obj = anno_df, aggrfun = mean) ## ----dryrun, eval=FALSE--------------------------------------------------------------------------- # GeneTonic(dds = dds_macrophage, # res_de = res_macrophage_IFNg_vs_naive, # res_enrich = res_enrich_macrophage, # annotation_obj = anno_df, # project_id = "GT1") ## ------------------------------------------------------------------------------------------------- gtl <- GeneTonicList( dds = DESeq2::estimateSizeFactors(dds_macrophage), res_de = res_macrophage_IFNg_vs_naive, res_enrich = res_enrich_macrophage, annotation_obj = anno_df ) # if nothing is returned, the object is ready to be provided to GeneTonic checkup_gtl(gtl) if (interactive()) { GeneTonic(gtl = gtl) } ## ----fig.cap="Screenshot of the `GeneTonic` application, when launched as a server where users can directly upload `GeneTonicList` objects. Information on the format and content for this object type are provided in the collapsible element on the right side of the dashboard body.", echo=FALSE---- knitr::include_graphics("upload_gtl.png") ## ----starthappyhour, eval = FALSE----------------------------------------------------------------- # happy_hour(dds = dds_macrophage, # res_de = res_de, # res_enrich = res_enrich, # annotation_obj = anno_df, # project_id = "examplerun", # mygenesets = res_enrich$gs_id[c(1:5,11,31)], # mygenes = c("ENSG00000125347", # "ENSG00000172399", # "ENSG00000137496") # ) ## ----enhancetable--------------------------------------------------------------------------------- p <- enhance_table(res_enrich_macrophage, res_macrophage_IFNg_vs_naive, n_gs = 30, annotation_obj = anno_df, chars_limit = 60) p library("plotly") ggplotly(p) ## ----alluvial------------------------------------------------------------------------------------- gs_alluvial(res_enrich = res_enrich_macrophage, res_de = res_macrophage_IFNg_vs_naive, annotation_obj = anno_df, n_gs = 4) ## ----ggs------------------------------------------------------------------------------------------ ggs <- ggs_graph(res_enrich_macrophage, res_de = res_macrophage_IFNg_vs_naive, anno_df, n_gs = 20) ggs # could be viewed interactively with library(visNetwork) library(magrittr) ggs %>% visIgraph() %>% visOptions(highlightNearest = list(enabled = TRUE, degree = 1, hover = TRUE), nodesIdSelection = TRUE) ## ----summaryrep----------------------------------------------------------------------------------- em <- enrichment_map(res_enrich_macrophage, res_macrophage_IFNg_vs_naive, n_gs = 30, color_by = "z_score", anno_df) library("igraph") library("visNetwork") library("magrittr") em %>% visIgraph() %>% visOptions(highlightNearest = list(enabled = TRUE, degree = 1, hover = TRUE), nodesIdSelection = TRUE) ## ------------------------------------------------------------------------------------------------- distilled <- distill_enrichment(res_enrich_macrophage, res_macrophage_IFNg_vs_naive, anno_df, n_gs = 60, cluster_fun = "cluster_markov") DT::datatable(distilled$distilled_table[,1:4]) dim(distilled$distilled_table) DT::datatable(distilled$res_enrich[,]) dg <- distilled$distilled_em library("igraph") library("visNetwork") library("magrittr") # defining a color palette for nicer display colpal <- colorspace::rainbow_hcl(length(unique(V(dg)$color)))[V(dg)$color] V(dg)$color.background <- scales::alpha(colpal, alpha = 0.8) V(dg)$color.highlight <- scales::alpha(colpal, alpha = 1) V(dg)$color.hover <- scales::alpha(colpal, alpha = 0.5) V(dg)$color.border <- "black" visNetwork::visIgraph(dg) %>% visOptions(highlightNearest = list(enabled = TRUE, degree = 1, hover = TRUE), nodesIdSelection = TRUE, selectedBy = "membership") ## ------------------------------------------------------------------------------------------------- res_enrich_subset <- res_enrich_macrophage[1:100, ] fuzzy_subset <- gs_fuzzyclustering( res_enrich = res_enrich_subset, n_gs = nrow(res_enrich_subset), gs_ids = NULL, similarity_matrix = NULL, similarity_threshold = 0.35, fuzzy_seeding_initial_neighbors = 3, fuzzy_multilinkage_rule = 0.5) # show all genesets members of the first cluster fuzzy_subset[fuzzy_subset$gs_fuzzycluster == "1", ] # list only the representative clusters DT::datatable( fuzzy_subset[fuzzy_subset$gs_cluster_status == "Representative", ] ) ## ----volcano-------------------------------------------------------------------------------------- gs_volcano(res_enrich_macrophage, p_threshold = 0.05, color_by = "aggr_score", volcano_labels = 10, gs_ids = NULL, plot_title = "my volcano") res_enrich_simplified <- gs_simplify(res_enrich_macrophage, gs_overlap = 0.7) dim(res_enrich_macrophage) dim(res_enrich_simplified) gs_volcano(res_enrich_simplified, color_by = "aggr_score") ## ----dendro--------------------------------------------------------------------------------------- gs_dendro(res_enrich_macrophage, n_gs = 50, gs_dist_type = "kappa", clust_method = "ward.D2", color_leaves_by = "z_score", size_leaves_by = "gs_pvalue", color_branches_by = "clusters", create_plot = TRUE) ## ----mds------------------------------------------------------------------------------------------ gs_mds(res_enrich_macrophage, res_macrophage_IFNg_vs_naive, anno_df, n_gs = 200, gs_ids = NULL, similarity_measure = "kappa_matrix", mds_k = 2, mds_labels = 5, mds_colorby = "z_score", gs_labels = NULL, plot_title = NULL) ## ----overview------------------------------------------------------------------------------------- gs_summary_overview(res_enrich_macrophage, n_gs = 30, p_value_column = "gs_pvalue", color_by = "z_score") ## ----sumheat-------------------------------------------------------------------------------------- gs_summary_heat(res_enrich_macrophage, res_macrophage_IFNg_vs_naive, anno_df, n_gs = 15) ## ------------------------------------------------------------------------------------------------- gs_summary_heat(gtl = gtl, n_gs = 10) ## ----scoresheat----------------------------------------------------------------------------------- vst_macrophage <- vst(dds_macrophage) scores_mat <- gs_scores( se = vst_macrophage, res_de = res_macrophage_IFNg_vs_naive, res_enrich = res_enrich_macrophage, annotation_obj = anno_df ) gs_scoresheat(scores_mat, n_gs = 30) ## ----happyhour, eval=FALSE------------------------------------------------------------------------ # happy_hour(dds = dds_macrophage, # res_de = res_de, # res_enrich = res_enrich, # annotation_obj = anno_df, # project_id = "examplerun", # mygenesets = res_enrich$gs_id[c(1:5,11,31)], # mygenes = c("ENSG00000125347", # "ENSG00000172399", # "ENSG00000137496") # ) ## ----happyhour2, eval=FALSE----------------------------------------------------------------------- # happy_hour(gtl = gtl, # project_id = "examplerun", # mygenesets = gtl$res_enrich$gs_id[c(1:5,11,31)], # mygenes = c("ENSG00000125347", # "ENSG00000172399", # "ENSG00000137496"), # open_after_creating = TRUE # ) ## ----template------------------------------------------------------------------------------------- template_rmd <- system.file("extdata", "cocktail_recipe.Rmd", package = "GeneTonic") template_rmd ## ----comparepair---------------------------------------------------------------------------------- # generating some shuffled gene sets res_enrich2 <- res_enrich_macrophage[1:50, ] set.seed(42) shuffled_ones <- sample(seq_len(50)) # to generate permuted p-values res_enrich2$gs_pvalue <- res_enrich2$gs_pvalue[shuffled_ones] res_enrich2$z_score <- res_enrich2$z_score[shuffled_ones] res_enrich2$aggr_score <- res_enrich2$aggr_score[shuffled_ones] gs_summary_overview_pair(res_enrich = res_enrich_macrophage, res_enrich2 = res_enrich2, n_gs = 25) ## ----compare4------------------------------------------------------------------------------------- res_enrich2 <- res_enrich_macrophage[1:42, ] res_enrich3 <- res_enrich_macrophage[1:42, ] res_enrich4 <- res_enrich_macrophage[1:42, ] set.seed(2*42) shuffled_ones_2 <- sample(seq_len(42)) # to generate permuted p-values res_enrich2$gs_pvalue <- res_enrich2$gs_pvalue[shuffled_ones_2] res_enrich2$z_score <- res_enrich2$z_score[shuffled_ones_2] res_enrich2$aggr_score <- res_enrich2$aggr_score[shuffled_ones_2] set.seed(3*42) shuffled_ones_3 <- sample(seq_len(42)) # to generate permuted p-values res_enrich3$gs_pvalue <- res_enrich3$gs_pvalue[shuffled_ones_3] res_enrich3$z_score <- res_enrich3$z_score[shuffled_ones_3] res_enrich3$aggr_score <- res_enrich3$aggr_score[shuffled_ones_3] set.seed(4*42) shuffled_ones_4 <- sample(seq_len(42)) # to generate permuted p-values res_enrich4$gs_pvalue <- res_enrich4$gs_pvalue[shuffled_ones_4] res_enrich4$z_score <- res_enrich4$z_score[shuffled_ones_4] res_enrich4$aggr_score <- res_enrich4$aggr_score[shuffled_ones_4] compa_list <- list( scenario2 = res_enrich2, scenario3 = res_enrich3, scenario4 = res_enrich4 ) gs_horizon(res_enrich_macrophage, compared_res_enrich_list = compa_list, n_gs = 20, sort_by = "clustered") ## ----compareradar--------------------------------------------------------------------------------- # with only one set gs_radar(res_enrich = res_enrich_macrophage) # with a dataset to compare against gs_radar(res_enrich = res_enrich_macrophage, res_enrich2 = res_enrich2) ## ----misc----------------------------------------------------------------------------------------- head(deseqresult2df(res_macrophage_IFNg_vs_naive)) # to make sure normalized values are available... dds_macrophage <- estimateSizeFactors(dds_macrophage) gene_plot(dds_macrophage, gene = "ENSG00000125347", intgroup = "condition", annotation_obj = anno_df, plot_type = "auto") gene_plot(dds_macrophage, gene = "ENSG00000174944", intgroup = "condition", assay = "abundance", annotation_obj = anno_df, plot_type = "auto") geneinfo_2_html("IRF1") ## ----gsheatmap------------------------------------------------------------------------------------ gs_heatmap(se = vst_macrophage, res_de = res_macrophage_IFNg_vs_naive, res_enrich = res_enrich_macrophage, annotation_obj = anno_df, geneset_id = "GO:0060337" , cluster_columns = TRUE, anno_col_info = "condition" ) go_2_html("GO:0060337", res_enrich = res_enrich_macrophage) ## ----signaturevolcano----------------------------------------------------------------------------- signature_volcano(res_de = res_macrophage_IFNg_vs_naive, res_enrich = res_enrich_macrophage, annotation_obj = anno_df, geneset_id = "GO:0060337", FDR = 0.05, color = "#1a81c2" ) ## ----shakers, eval=FALSE-------------------------------------------------------------------------- # res_enrich <- shake_enrichResult(enrichment_results_from_clusterProfiler) # res_enrich <- shake_topGOtableResult(enrichment_results_from_topGOtable) ## ----checkup-------------------------------------------------------------------------------------- checkup_GeneTonic(dds = dds_macrophage, res_de = res_macrophage_IFNg_vs_naive, res_enrich = res_enrich_macrophage, annotation_obj = anno_df) # if all is fine, it should return an invisible NULL and a simple message ## ----cite----------------------------------------------------------------------------------------- citation("GeneTonic") ## ----sessioninfo---------------------------------------------------------------------------------- sessionInfo()