# The function pathway_errorbar() is used to visualize the results of functional pathway differential abundance analysis as error bar plots. ## Arguments - `abundance`: A data frame with row names representing pathways and column names representing samples. Each element represents the relative abundance of the corresponding pathway in the corresponding sample. - `daa_results_df`: A data frame containing the results of the differential abundance analysis of the pathways, generated by the pathway_daa function. x_lab should be a column name of daa_results_df. - `Group`: A data frame or a vector that assigns each sample to a group. The groups are used to color the samples in the figure. - `ko_to_kegg`: A logical parameter indicating whether there was a convertion that convert ko abundance to kegg abundance. - `p_values_threshold`: A numeric parameter specifying the threshold for statistical significance of differential abundance. Pathways with p-values below this threshold will be considered significant. - `order`: A parameter controlling the ordering of the rows in the figure. The options are: "p_values" (order by p-values), "name" (order by pathway name), "group" (order by the group with the highest mean relative abundance), or "pathway_class" (order by the pathway category). - `select`: A vector of pathway names to be included in the figure. This can be used to limit the number of pathways displayed. If NULL, all pathways will be displayed. - `p_value_bar`: A logical parameter indicating whether to display a bar showing the p-value threshold for significance. If TRUE, the bar will be displayed. - `colors`: A vector of colors to be used to represent the groups in the figure. Each color corresponds to a group. - `x_lab`: A character string to be used as the x-axis label in the figure. The default value is "description" for KOs'descriptions and "pathway_name" for KEGG pathway names. ## Returns A figure showing the error bar plot of the differential abundance analysis results for the functional pathways. ## Examples ```r ## Not run: # Example 1: Analyzing KEGG pathway abundance metadata <- read_delim( "path/to/your/metadata.txt", delim = "\t", escape_double = FALSE, trim_ws = TRUE ) # data(metadata) kegg_abundance <- ko2kegg_abundance( "path/to/your/pred_metagenome_unstrat.tsv" ) # data(kegg_abundance) # Please change group to "your_group_column" if you are not using example dataset group <- "Environment" daa_results_df <- pathway_daa( abundance = kegg_abundance, metadata = metadata, group = group, daa_method = "ALDEx2", select = NULL, reference = NULL ) # Please check the unique(daa_results_df$method) and choose one daa_sub_method_results_df <- daa_results_df[daa_results_df$method == "ALDEx2_Welch's t test", ] daa_annotated_sub_method_results_df <- pathway_annotation( pathway = "KO", daa_results_df = daa_sub_method_results_df, ko_to_kegg = TRUE ) # Please change Group to metadata$your_group_column if you are not using example dataset Group <- metadata$Environment p <- pathway_errorbar( abundance = kegg_abundance, daa_results_df = daa_annotated_sub_method_results_df, Group = Group, p_values_threshold = 0.05, order = "pathway_class", select = daa_annotated_sub_method_results_df %>% arrange(p_adjust) %>% slice(1:20) %>% select("feature") %>% pull(), ko_to_kegg = TRUE, p_value_bar = TRUE, colors = NULL, x_lab = "pathway_name" ) # Example 2: Analyzing EC, MetaCyc, KO without conversions metadata <- read_delim( "path/to/your/metadata.txt", delim = "\t", escape_double = FALSE, trim_ws = TRUE ) # data(metadata) ko_abundance <- read.delim("path/to/your/metacyc_abundance.tsv") # data(metacyc_abundance) group <- "Environment" daa_results_df <- pathway_daa( abundance = metacyc_abundance %>% column_to_rownames("pathway"), metadata = metadata, group = group, daa_method = "LinDA", select = NULL, reference = NULL ) daa_annotated_results_df <- pathway_annotation( pathway = "MetaCyc", daa_results_df = daa_results_df, ko_to_kegg = FALSE ) Group <- metadata$Environment p <- pathway_errorbar( abundance = metacyc_abundance %>% column_to_rownames("pathway"), daa_results_df = daa_annotated_results_df, Group = Group, p_values_threshold = 0.05, order = "group", select = NULL, ko_to_kegg = FALSE, p_value_bar = TRUE, colors = NULL, x_lab = "description" ) ## End(Not run) ```

pathway_errorbar function