## ----include=FALSE, eval=FALSE------------------------------------------------ # knitr::opts_chunk$set(comment = "#", message = FALSE) # # if (!requireNamespace("devtools", quietly = TRUE)) { # install.packages("devtools") # } # # library(devtools) # # devtools::load_all(".") # library(SummarizedExperiment) ## ----get_package, eval=FALSE-------------------------------------------------- # if (!requireNamespace("BiocManager", quietly = TRUE)) { # install.packages("BiocManager") # } # BiocManager::install("compbiomed/animalcules") ## ----eval=FALSE--------------------------------------------------------------- # if (!requireNamespace("devtools", quietly = TRUE)) { # install.packages("devtools") # } # devtools::install_github("compbiomed/animalcules") ## ----load--------------------------------------------------------------------- library(animalcules) library(SummarizedExperiment) ## ----eval=FALSE--------------------------------------------------------------- # run_animalcules() ## ----------------------------------------------------------------------------- data_dir <- system.file("extdata/TB_example_dataset.rds", package = "animalcules") MAE <- readRDS(data_dir) ## ----eval=FALSE--------------------------------------------------------------- # data_dir <- "PATH_TO_THE_ANIMALCULES_FILE" # MAE <- readRDS(data_dir) ## ----------------------------------------------------------------------------- p <- animalcules::filter_summary_pie_box(MAE, samples_discard = c("SRR1204622"), filter_type = "By Metadata", sample_condition = "age_s" ) p ## ----------------------------------------------------------------------------- p <- filter_summary_bar_density(MAE, samples_discard = c("SRR1204622"), filter_type = "By Metadata", sample_condition = "sex_s" ) p ## ----------------------------------------------------------------------------- microbe <- MAE[["MicrobeGenetics"]] samples <- as.data.frame(colData(microbe)) result <- filter_categorize(samples, sample_condition = "age_s", new_label = "AGE_GROUP", bin_breaks = c(0, 30, 40, 100), bin_labels = c("a", "b", "c") ) head(result$sam_table) result$plot.unbinned result$plot.binned ## ----------------------------------------------------------------------------- p <- relabu_barplot(MAE, tax_level = "genus", sort_by = "conditions", sample_conditions = c("Disease"), show_legend = TRUE ) p ## ----------------------------------------------------------------------------- p <- relabu_heatmap(MAE, tax_level = "genus", sort_by = "conditions", sample_conditions = c("sex_s", "age_s") ) p ## ----------------------------------------------------------------------------- p <- relabu_boxplot(MAE, tax_level = "genus", organisms = c("Streptococcus", "Staphylococcus"), condition = "sex_s", datatype = "logcpm" ) p ## ----------------------------------------------------------------------------- alpha_div_boxplot( MAE = MAE, tax_level = "genus", condition = "Disease", alpha_metric = "shannon" ) ## ----------------------------------------------------------------------------- do_alpha_div_test( MAE = MAE, tax_level = "genus", condition = "Disease", alpha_metric = "shannon", alpha_stat = "T-test" ) ## ----------------------------------------------------------------------------- diversity_beta_heatmap( MAE = MAE, tax_level = "genus", input_beta_method = "bray", input_bdhm_select_conditions = "Disease", input_bdhm_sort_by = "condition" ) ## ----------------------------------------------------------------------------- diversity_beta_boxplot( MAE = MAE, tax_level = "genus", input_beta_method = "bray", input_select_beta_condition = "Disease" ) ## ----------------------------------------------------------------------------- diversity_beta_test( MAE = MAE, tax_level = "genus", input_beta_method = "bray", input_select_beta_condition = "Disease", input_select_beta_stat_method = "PERMANOVA", input_num_permutation_permanova = 999 ) ## ----------------------------------------------------------------------------- result <- dimred_pca(MAE, tax_level = "genus", color = "age_s", shape = "Disease", pcx = 1, pcy = 2, datatype = "logcpm" ) result$plot head(result$table) ## ----------------------------------------------------------------------------- result <- dimred_pcoa(MAE, tax_level = "genus", color = "age_s", shape = "Disease", axx = 1, axy = 2, method = "bray" ) result$plot head(result$table) ## ----------------------------------------------------------------------------- result <- dimred_umap(MAE, tax_level = "genus", color = "age_s", shape = "Disease", cx = 1, cy = 2, n_neighbors = 15, metric = "euclidean", datatype = "logcpm" ) result$plot ## ----------------------------------------------------------------------------- # result <- dimred_tsne(MAE, # tax_level="phylum", # color="age_s", # shape="Disease", # k="3D", # initial_dims=30, # perplexity=10, # datatype="logcpm") # result$plot ## ----------------------------------------------------------------------------- p <- differential_abundance(MAE, tax_level = "phylum", input_da_condition = c("Disease"), min_num_filter = 2, input_da_padj_cutoff = 0.5 ) p ## ----------------------------------------------------------------------------- p <- find_biomarker(MAE, tax_level = "genus", input_select_target_biomarker = c("Disease"), nfolds = 3, nrepeats = 3, seed = 99, percent_top_biomarker = 0.2, model_name = "logistic regression" ) # biomarker p$biomarker # importance plot p$importance_plot # ROC plot p$roc_plot ## ----------------------------------------------------------------------------- sessionInfo()