diff --git a/vignettes/M2Cf/figs/Activity estimations for factor 4-1.png b/vignettes/M2Cf/figs/Activity_estimations_for_factor_4_1.png similarity index 100% rename from vignettes/M2Cf/figs/Activity estimations for factor 4-1.png rename to vignettes/M2Cf/figs/Activity_estimations_for_factor_4_1.png diff --git a/vignettes/M2Cf/figs/Activity estimations for factor 4-2.png b/vignettes/M2Cf/figs/Activity_estimations_for_factor_4_2.png similarity index 100% rename from vignettes/M2Cf/figs/Activity estimations for factor 4-2.png rename to vignettes/M2Cf/figs/Activity_estimations_for_factor_4_2.png diff --git a/vignettes/M2Cf/figs/Chekc cross correlation of omics compared to variance epxlained-1.png b/vignettes/M2Cf/figs/Chekc_cross_correlation_of_omics_compared_to_variance_epxlained_1.png similarity index 100% rename from vignettes/M2Cf/figs/Chekc cross correlation of omics compared to variance epxlained-1.png rename to 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b/vignettes/M2Cf/figs/Compare_MOFA_models_1.png similarity index 100% rename from vignettes/M2Cf/figs/Compare MOFA models-1.png rename to vignettes/M2Cf/figs/Compare_MOFA_models_1.png diff --git a/vignettes/M2Cf/figs/Factor weights per view-1.png b/vignettes/M2Cf/figs/Factor_weights_per_view_1.png similarity index 100% rename from vignettes/M2Cf/figs/Factor weights per view-1.png rename to vignettes/M2Cf/figs/Factor_weights_per_view_1.png diff --git a/vignettes/M2Cf/figs/Plot Ligand-receptor activity estimates-1.png b/vignettes/M2Cf/figs/Plot_Ligand_receptor_activity_estimates_1.png similarity index 100% rename from vignettes/M2Cf/figs/Plot Ligand-receptor activity estimates-1.png rename to vignettes/M2Cf/figs/Plot_Ligand_receptor_activity_estimates_1.png diff --git a/vignettes/M2Cf/figs/Plot Metabolite factor weights-1.png b/vignettes/M2Cf/figs/Plot_Metabolite_factor_weights_1.png similarity index 100% rename from vignettes/M2Cf/figs/Plot Metabolite factor weights-1.png rename to 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input-1.png b/vignettes/M2Cf/figs/Preparing_MOFA_input_1.png similarity index 100% rename from vignettes/M2Cf/figs/Preparing MOFA input-1.png rename to vignettes/M2Cf/figs/Preparing_MOFA_input_1.png diff --git a/vignettes/M2Cf/figs/Preparing MOFA input-2.png b/vignettes/M2Cf/figs/Preparing_MOFA_input_2.png similarity index 100% rename from vignettes/M2Cf/figs/Preparing MOFA input-2.png rename to vignettes/M2Cf/figs/Preparing_MOFA_input_2.png diff --git a/vignettes/M2Cf/figs/Preparing MOFA input-3.png b/vignettes/M2Cf/figs/Preparing_MOFA_input_3.png similarity index 100% rename from vignettes/M2Cf/figs/Preparing MOFA input-3.png rename to vignettes/M2Cf/figs/Preparing_MOFA_input_3.png diff --git a/vignettes/M2Cf/figs/Preparing MOFA input-4.png b/vignettes/M2Cf/figs/Preparing_MOFA_input_4.png similarity index 100% rename from vignettes/M2Cf/figs/Preparing MOFA input-4.png rename to vignettes/M2Cf/figs/Preparing_MOFA_input_4.png diff --git a/vignettes/M2Cf/figs/Preparing MOFA input-5.png b/vignettes/M2Cf/figs/Preparing_MOFA_input_5.png similarity index 100% rename from vignettes/M2Cf/figs/Preparing MOFA input-5.png rename to vignettes/M2Cf/figs/Preparing_MOFA_input_5.png diff --git a/vignettes/M2Cf/figs/Run moon rec_to_TFmetab-1.png b/vignettes/M2Cf/figs/Run_moon_rec_to_TFmetab_1.png similarity index 100% rename from vignettes/M2Cf/figs/Run moon rec_to_TFmetab-1.png rename to vignettes/M2Cf/figs/Run_moon_rec_to_TFmetab_1.png diff --git a/vignettes/M2Cf/figs/Supp figure condition_prot_RNA_correlation-1.png b/vignettes/M2Cf/figs/Supp_figure_condition_prot_RNA_correlation_1.png similarity index 100% rename from vignettes/M2Cf/figs/Supp figure condition_prot_RNA_correlation-1.png rename to vignettes/M2Cf/figs/Supp_figure_condition_prot_RNA_correlation_1.png diff --git a/vignettes/M2Cf/figs/Supp figure single_prot_RNA_correlation-1.png b/vignettes/M2Cf/figs/Supp_figure_single_prot_RNA_correlation_1.png similarity index 100% rename from vignettes/M2Cf/figs/Supp figure single_prot_RNA_correlation-1.png rename to vignettes/M2Cf/figs/Supp_figure_single_prot_RNA_correlation_1.png diff --git a/vignettes/M2Cf/figs/Total variance per factor-1.png b/vignettes/M2Cf/figs/Total_variance_per_factor_1.png similarity index 100% rename from vignettes/M2Cf/figs/Total variance per factor-1.png rename to vignettes/M2Cf/figs/Total_variance_per_factor_1.png diff --git a/vignettes/M2Cf/figs/Variance per view per factor-1.png b/vignettes/M2Cf/figs/Variance_per_view_per_factor_1.png similarity index 100% rename from vignettes/M2Cf/figs/Variance per view per factor-1.png rename to vignettes/M2Cf/figs/Variance_per_view_per_factor_1.png diff --git a/vignettes/M2Cf/figs/correlation RNA/prot-1.png b/vignettes/M2Cf/figs/correlation_RNA/prot-1.png similarity index 100% rename from vignettes/M2Cf/figs/correlation RNA/prot-1.png rename to vignettes/M2Cf/figs/correlation_RNA/prot-1.png diff --git a/vignettes/M2Cf/figs/run moon TF to lig-1.png b/vignettes/M2Cf/figs/run_moon_TF_to_lig_1.png similarity index 100% rename from vignettes/M2Cf/figs/run moon TF to lig-1.png rename to vignettes/M2Cf/figs/run_moon_TF_to_lig_1.png diff --git a/vignettes/MOFA_to_COSMOS.md b/vignettes/MOFA_to_COSMOS.md index e5992e5..1b990f9 100644 --- a/vignettes/MOFA_to_COSMOS.md +++ b/vignettes/MOFA_to_COSMOS.md @@ -103,11 +103,11 @@ this is not necessary since MOFA2 is able to impute data). RNA_sd <- sort(apply(RNA, 1, function(x) sd(x,na.rm = T)), decreasing = T) hist(RNA_sd, breaks = 100) -![](M2Cf/figs/Preparing%20MOFA%20input-1.png) +![](M2Cf/figs/Preparing_MOFA_input_1.png) hist(RNA_sd[1:6000], breaks = 100) -![](M2Cf/figs/Preparing%20MOFA%20input-2.png) +![](M2Cf/figs/Preparing_MOFA_input_2.png) RNA_sd <- RNA_sd[1:6000] RNA <- RNA[names(RNA_sd),] @@ -123,11 +123,11 @@ this is not necessary since MOFA2 is able to impute data). proteo_sd <- sort(apply(proteo, 1, function(x) sd(x,na.rm = T)), decreasing = T) hist(proteo_sd, breaks = 100) -![](M2Cf/figs/Preparing%20MOFA%20input-3.png) +![](M2Cf/figs/Preparing_MOFA_input_3.png) hist(proteo_sd[1:round(dim(proteo)[1]*3/5)], breaks = 100) -![](M2Cf/figs/Preparing%20MOFA%20input-4.png) +![](M2Cf/figs/Preparing_MOFA_input-4.png) proteo_sd <- proteo_sd[1:round(dim(proteo)[1]*3/5)] proteo <- proteo[names(proteo_sd),] @@ -143,7 +143,7 @@ this is not necessary since MOFA2 is able to impute data). metab_sd <- apply(metab, 1, function(x) sd(x,na.rm=T)) hist(metab_sd, breaks = 100) -![](M2Cf/figs/Preparing%20MOFA%20input-5.png) +![](M2Cf/figs/Preparing_MOFA_input-5.png) # Create long data frame ## Only keep samples with each view present @@ -214,7 +214,7 @@ and RNA stability. hist(condition_prot_RNA_correlation, breaks = 20) -![](M2Cf/figs/Supp%20figure%20condition_prot_RNA_correlation-1.png) +![](M2Cf/figs/Supp_figure_condition_prot_RNA_correlation_1.png) overlap_genes <- unique(intersect(proteo$feature,RNA$feature)) single_prot_RNA_correlation <- sapply(overlap_genes, function(gene){ @@ -226,7 +226,7 @@ and RNA stability. hist(single_prot_RNA_correlation, breaks = 100) -![](M2Cf/figs/Supp%20figure%20single_prot_RNA_correlation-1.png) +![](M2Cf/figs/Supp_figure_single_prot_RNA_correlation_1.png) ### MOFA run @@ -280,7 +280,7 @@ consistency (here: model 7-13). compare_factors(list(model7,model8,model9,model10,model11,model12,model13), cluster_rows = F, cluster_cols = F,) -![](M2Cf/figs/Compare%20MOFA%20models-1.png) +![](M2Cf/figs/Compare_MOFA_models_1.png) Here, we can see that the inferred MOFA factor weights do not change drastically around 9. For the sake of this tutorial, we choose the @@ -304,7 +304,7 @@ the model represents the data. # Investigate factors: Explained total variance per view plot_variance_explained(model, x="group", y="factor", plot_total = T)[[2]] -![](M2Cf/figs/Total%20variance%20per%20factor-1.png) +![](M2Cf/figs/Total_variance_per_factor_1.png) calculate_variance_explained(model) @@ -372,7 +372,7 @@ degratation regulatory mechanisms). # plot(calculate_variance_explained(model)$r2_total$single_group, c(mean(RNA_crosscor),mean(metabo_crosscor),mean(prot_crosscor))) plot(calculate_variance_explained(model)$r2_total$single_group, c(dim(RNA_crosscor)[1],dim(metabo_crosscor)[1],dim(prot_crosscor)[1])) -![](M2Cf/figs/Chekc%20cross%20correlation%20of%20omics%20compared%20to%20variance%20epxlained-1.png) +![](M2Cf/figs/Chekc_cross_correlation_of_omics_compared_to_variance_epxlained_1.png) df_variance_crosscor <- as.data.frame(cbind(calculate_variance_explained(model)$r2_total$single_group, c(mean(RNA_crosscor),mean(metabo_crosscor),mean(prot_crosscor)), @@ -391,7 +391,7 @@ degratation regulatory mechanisms). mofa_solved -![](M2Cf/figs/Chekc%20cross%20correlation%20of%20omics%20compared%20to%20variance%20epxlained-2.png) +![](M2Cf/figs/Chekc_cross_correlation_of_omics_compared_to_variance_epxlained_2.png) mofa_nfeatures <- ggplot(df_variance_crosscor, aes(x = n_features, y = var_expl)) + geom_point() + @@ -403,7 +403,7 @@ degratation regulatory mechanisms). mofa_nfeatures -![](M2Cf/figs/Chekc%20cross%20correlation%20of%20omics%20compared%20to%20variance%20epxlained-3.png) +![](M2Cf/figs/Chekc_cross_correlation_of_omics_compared_to_variance_epxlained_3.png) #Get the actual coeficient signficance summary(lm(data= df_variance_crosscor, var_expl~prod)) @@ -454,7 +454,7 @@ the most variance across all views. # Investigate factors: Explained variance per view for each factor pheatmap(model@cache$variance_explained$r2_per_factor[[1]], display_numbers = T, angle_col = "0", legend_labels = c("0","10", "20", "30", "40", "Variance\n\n"), legend = T, main = "", legend_breaks = c(0,10, 20, 30, 40, max(model@cache$variance_explained$r2_per_factor[[1]])), cluster_rows = F, cluster_cols = F, color = colorRampPalette(c("white","red"))(100), fontsize_number = 10) -![](M2Cf/figs/Variance%20per%20view%20per%20factor-1.png) +![](M2Cf/figs/Variance_per_view_per_factor_1.png) pheatmap(model@cache$variance_explained$r2_per_factor[[1]], display_numbers = T, angle_col = "0", legend_labels = c("0","10", "20", "30", "40", "Variance\n\n"), legend = T, main = "", legend_breaks = c(0,10, 20, 30, 40, max(model@cache$variance_explained$r2_per_factor[[1]])), cluster_rows = F, cluster_cols = F, color = colorRampPalette(c("white","red"))(100), fontsize_number = 10,filename = "results/mofa/variance_heatmap.pdf",width = 4, height = 2.5) @@ -576,7 +576,7 @@ top 10 weights per factor and view. ggtitle("Metabolomics"), ncol =3 ) -![](M2Cf/figs/Factor%20weights%20per%20view-1.png) +![](M2Cf/figs/Factor_weights_per_view_1.png) Using this visualization, features with strong association with the factor (large absolute values) can be easily identified and further @@ -686,7 +686,7 @@ downstream analysis as well. ligrec_high_vs_low_top$source <- factor(ligrec_high_vs_low_top$source, levels = ligrec_high_vs_low_top$source) ggplot(ligrec_high_vs_low_top, aes(x=source, y = score)) + geom_bar(stat= "identity",position = "dodge") + theme_minimal() + theme(axis.text.x = element_text(angle = 315, vjust = 0.5, hjust=0)) -![](M2Cf/figs/Activity%20estimations%20for%20factor%204-1.png) +![](M2Cf/figs/Activity_estimations_for_factor_4_1.png) # Calculate regulatory activities from TF network TF_high_vs_low <- run_ulm(mat = as.matrix(RNA), network = dorothea_df, minsize = 10) @@ -702,7 +702,7 @@ downstream analysis as well. TF_high_vs_low_top_10$source <- factor(TF_high_vs_low_top_10$source, levels = TF_high_vs_low_top_10$source) ggplot(TF_high_vs_low_top_10, aes(x=source, y = score)) + geom_bar(stat= "identity",position = "dodge") + theme_minimal() -![](M2Cf/figs/Activity%20estimations%20for%20factor%204-2.png) +![](M2Cf/figs/Activity_estimations_for_factor_4_2.png) # Combine results ligrec_TF_moon_inputs <- list("ligrec" = ligrec_high_vs_low_vector, @@ -778,7 +778,7 @@ factors. ## Saving 7 x 7 in image -![](M2Cf/figs/correlation%20RNA/prot-1.png) +![](M2Cf/figs/correlation_RNA/prot_1.png) Coherently, only the factors where there is variance explained for both RNA and proteins are correlated. @@ -796,13 +796,13 @@ the protein factor weights. abline(v = -0.2) abline(v = 0.2)} -![](M2Cf/figs/Plot%20RNA%20weights%20and%20protein%20weights-1.png) +![](M2Cf/figs/Plot_RNA_weights_and_protein_weights_1.png) {plot(density(prot_input)) abline(v = -0.05) abline(v = 0.05)} -![](M2Cf/figs/Plot%20RNA%20weights%20and%20protein%20weights-2.png) +![](M2Cf/figs/Plot_RNA_weights_and_protein_weights_2.png) Based on the plots and indicated by the straight lines, the RNA weights threshold is set to -0.2 and 0.2, and the protein weights threshold to @@ -844,7 +844,7 @@ The same procedure is repeated for the transcription factor activities. abline(v = -0.5) abline(v = 3.5)} -![](M2Cf/figs/Plot%20TF%20activity%20estimates-1.png) +![](M2Cf/figs/Plot_TF_activity_estimates_1.png) The threshold is set to -0.5 and 3.5 respectively. Further, the TF\_to\_remove variable is later used to remove transcription factors @@ -860,7 +860,7 @@ The same procedure is repeated for the ligand-receptor activities. abline(v = -0.5) abline(v = 2.5)} -![](M2Cf/figs/Plot%20Ligand-receptor%20activity%20estimates-1.png) +![](M2Cf/figs/Plot_Ligand-receptor_activity_estimates_1.png) Here, only activities higher than 2.5 or lower than -0.5 are kept (see straight lines in plot). This is an arbitrary theshold aimed at keeping @@ -887,7 +887,7 @@ weights. abline(v = -0.2) abline(v = 0.2)} -![](M2Cf/figs/Plot%20Metabolite%20factor%20weights-1.png) +![](M2Cf/figs/Plot_Metabolite_factor_weights_1.png) The threshold is set to 0.2 and -0.2 respectively. Further, the metab\_to\_exclude variable is later used to remove metabolites with @@ -1245,7 +1245,7 @@ generated with the reduce\_solution\_network function. abline(v = 1) abline(v = -1) -![](M2Cf/figs/Run%20moon%20rec_to_TFmetab-1.png) +![](M2Cf/figs/Run_moon_rec_to_TFmetab_1.png) solution_network <- reduce_solution_network(decoupleRnival_res = moon_res, meta_network = meta_network_filtered, @@ -1394,7 +1394,7 @@ downstream input definition is changing. abline(v = 1) abline(v = -1) -![](M2Cf/figs/run%20moon%20TF%20to%20lig-1.png) +![](M2Cf/figs/run_moon_TF_to_lig_1.png) solution_network <- reduce_solution_network(decoupleRnival_res = moon_res, meta_network = as.data.frame(dorothea_PKN_filtered[,c(1,3,2)]), diff --git a/vignettes/net_compr_MOON.md b/vignettes/net_compr_MOON.md index 6b3393c..d19a8fe 100644 --- a/vignettes/net_compr_MOON.md +++ b/vignettes/net_compr_MOON.md @@ -1,6 +1,5 @@ -## Tutorial with NCI60 cohort -# libraries, data loading and feature selection +## libraries, data loading and feature selection # We advise to instal from github to get the latest version of the tool. # if (!requireNamespace("devtools", quietly = TRUE)) @@ -71,7 +70,7 @@ sig_input <- sig_input[abs(sig_input) > 2] # metab_input <- metab_input[abs(metab_input) > 2] -# Filter inputs and prior knowledge network +## Filter inputs and prior knowledge network #Remove genes that are not expressed from the meta_network meta_network <- cosmosR:::filter_pkn_expressed_genes(names(RNA_input), meta_pkn = meta_network) @@ -118,7 +117,7 @@ meta_network_compressed <- meta_network_cleanup(meta_network_compressed) -# run MOON ot score the and contextualise the PKN +## run MOON ot score the and contextualise the PKN load("support/dorothea_reg.RData") @@ -176,7 +175,7 @@ abline(v = 1) abline(v = -1) -![](net_compr_MOON_files/figure-markdown_strict/extract%20subnetwork%20from%20scored%20MOON%20network-1.png) +![](net_compr_MOON_files/figure-markdown_strict/extract_subnetwork_from_scored_MOON_network_1.png) solution_network <- reduce_solution_network(decoupleRnival_res = moon_res, meta_network = meta_network, diff --git a/vignettes/net_compr_MOON_files/figure-markdown_strict/extract subnetwork from scored MOON network-1.png b/vignettes/net_compr_MOON_files/figure-markdown_strict/extract_subnetwork_from_scored_MOON_network_1.png similarity index 100% rename from vignettes/net_compr_MOON_files/figure-markdown_strict/extract subnetwork from scored MOON network-1.png rename to vignettes/net_compr_MOON_files/figure-markdown_strict/extract_subnetwork_from_scored_MOON_network_1.png