# Network Analysis with Differential Species Meta network analysis integrating differential taxon into a network analysis ```r network_withdiff(network_obj, diff_frame, aes_col = NULL, tag_threshold = 5) ``` ## Arguments - `network_obj`: Network analysis results generated from `network_analysis` - `diff_frame`: Differential analysis results generated from `indicator_analysis` or `Deseq_analysis`. - `aes_col`: A named vector of colors to be used to highlight differential taxon vertices - `tag_threshold`: Numeric. A threshold for the minimum number of differential taxon to display. ## Returns A list containing the configured igraph object, vertices coordinates, parameters, and tag statistics. ## Examples ```r { # Data preparation data("Two_group") set.seed(999) # Analysis network_results <- network_analysis( taxobj = Two_group, taxlevel = "Genus", n = 10, threshold = 0.8 ) indicator_results <- indicator_analysis( taxobj = Two_group, taxlevel = "Genus" ) deseq_results <- Deseq_analysis( taxobj = Two_group, taxlevel = "Genus", cutoff = 1, control_name = "Control" ) # Visualize network_diff_obj <- network_withdiff( network_obj = network_results, diff_frame = indicator_results ) # Check contained tags for each model print(network_diff_obj$tag_statistics$sum_of_tags) # Check contained different tags for each model print(network_diff_obj$tag_statistics$detailed_tags) # Re-visualize network_visual_re( network_visual_obj = network_diff_obj, module_paint = TRUE, module_num = c(1, 4) ) # Show module with most Treatment indicators my_module_palette <- color_scheme( c("#83BA9E", "#F49128"), 5 ) network_visual_re( network_visual_obj = network_diff_obj, module_paint = TRUE, module_num = c(1, 4, 6, 3, 8), module_palette = my_module_palette ) # Show module with most Treatment indicators # Available also for DESeq analysis results network_diff_obj <- network_withdiff( network_obj = network_results, diff_frame = deseq_results ) # Parameter adjustment network_diff_obj <- network_withdiff( network_obj = network_results, diff_frame = indicator_results, tag_threshold = 20 ) # The 'tag_threshold' set too high network_diff_obj <- network_withdiff( network_obj = network_results, diff_frame = indicator_results, tag_threshold = 10 ) # Set lower # Check contained tags for each model print(network_diff_obj$tag_statistics$sum_of_tags) # Check contained different tags for each model print(network_diff_obj$tag_statistics$detailed_tags) network_diff_obj <- network_withdiff( network_obj = network_results, diff_frame = indicator_results, tag_threshold = 1 ) # Set too low # Another example data("Three_group") network_results <- network_analysis( taxobj = Three_group, taxlevel = "Genus", n = 15, threshold = 0.9 ) indicator_results <- indicator_analysis( taxobj = Three_group, taxlevel = "Genus" ) tag_color <- c( "CF" = "#F8766D", "CF_OF" = "#FFFF00", "OF" = "#00BA38", "OF_BF" = "#800080", "BF" = "#619CFF", "CF_BF" = "#00FFFF" ) network_diff_obj <- network_withdiff( network_obj = network_results, diff_frame = indicator_results, aes_col = tag_color, tag_threshold = 10 ) # Re-visualize print(network_diff_obj$tag_statistics$detailed_tags) network_visual_re( network_visual_obj = network_diff_obj, module_paint = TRUE, module_num = c(8, 10, 11) ) # Show module with most BF indicators network_visual_re( network_visual_obj = network_diff_obj, module_paint = TRUE, module_num = c(1, 6, 8) ) # Show module with most BF and OF_BF indicators } ```

network_withdiff function