## ---- eval=FALSE-------------------------------------------------------------- # if(!requireNamespace("BiocManager", quietly = TRUE)) # install.packages("BiocManager") # BiocManager::install("Macarron") ## ----------------------------------------------------------------------------- library(Macarron) prism_abundances <- system.file( 'extdata','demo_abundances.csv', package="Macarron") prism_annotations <-system.file( 'extdata','demo_annotations.csv', package="Macarron") prism_metadata <-system.file( 'extdata','demo_metadata.csv', package="Macarron") mets_taxonomy <-system.file( 'extdata','demo_taxonomy.csv', package="Macarron") prism_prioritized <- Macarron::Macarron(input_abundances = prism_abundances, input_annotations = prism_annotations, input_metadata = prism_metadata, input_taxonomy = mets_taxonomy) ## ---- eval=FALSE-------------------------------------------------------------- # abundances_df = read.csv(file = prism_abundances, row.names = 1) # setting features as rownames # annotations_df = read.csv(file = prism_annotations, row.names = 1) # setting features as rownames # metadata_df = read.csv(file = prism_metadata, row.names = 1) # setting samples as rownames # taxonomy_df = read.csv(file = mets_taxonomy) # # # Running Macarron # prism_prioritized <- Macarron::Macarron(input_abundances = abundances_df, # input_annotations = annotations_df, # input_metadata = metadata_df, # input_taxonomy = taxonomy_df) ## ----eval = FALSE------------------------------------------------------------- # # Step 1: Storing input data in a summarized experiment object # prism_mbx <- prepInput(input_abundances = abundances_df, # input_annotations = annotations_df, # input_metadata = metadata_df) # # # Step 2: Creating a distance matrix from pairwise correlations in abundances of metabolic features # prism_w <- makeDisMat(se = prism_mbx) # # # Step 3: Finding covariance modules # prism_modules <- findMacMod(se = prism_mbx, # w = prism_w, # input_taxonomy = taxonomy_df) # # The output is a list containing two dataframes- module assignments and measures of success # # if evaluateMOS=TRUE. To write modules to a separate dataframe, do: # prism_module_assignments <- prism_modules[[1]] # prism_modules_mos <- prism_modules[[2]] # # # Step 4: Calculating AVA # prism_ava <- calAVA(se = prism_mbx, # mod.assn = prism_modules) # # # Step 5: Calculating q-value # prism_qval <- calQval(se = prism_mbx, # mod.assn = prism_modules) # # # Step 6: Calculating effect size # prism_es <- calES(se = prism_mbx, # mac.qval = prism_qval) # # # Step 7: Prioritizing metabolic features # prism_prioritized <- prioritize(se = prism_mbx, # mod.assn = prism_modules, # mac.ava = prism_ava, # mac.qval = prism_qval, # mac.es = prism_es) # # The output is a list containing two dataframes- all prioritized metabolic features and # # only characterizable metabolic features. # all_prioritized <- prism_prioritized[[1]] # char_prioritized <- prism_prioritized[[2]] # # # Step 8 (optional): View only the highly prioritized metabolic features in each module # prism_highly_prioritized <- showBest(prism_prioritized) ## ----------------------------------------------------------------------------- sessionInfo() ## ---- eval=FALSE-------------------------------------------------------------- # taxonomy_df <- decorateID(input_annotations = annotations_df) # write.csv(taxonomy_df, file="demo_taxonomy.csv", row.names = FALSE) ## ---- eval=FALSE-------------------------------------------------------------- # prism_prioritized <- Macarron::Macarron(input_abundances = abundances_df, # input_annotations = annotations_df, # input_metadata = metadata_df, # input_taxonomy = taxonomy_df, # min_prevalence = 0.5) # # or # prism_w <- makeDisMat(se = prism_mbx, # min_prevalence = 0.5) ## ---- eval=FALSE-------------------------------------------------------------- # # See MOS of modules generated using default # prism_modules <- findMacMod(se = prism_mbx, # w = prism_w, # input_taxonomy = taxonomy_df) # prism_modules_mos <- prism_modules[[2]] # View(prism_modules_mos) # # # Change MMS # prism_modules <- findMacMod(se = prism_mbx, # w = prism_w, # input_taxonomy = taxonomy_df, # min_module_size = 10) ## ---- eval=FALSE-------------------------------------------------------------- # prism_qval <- calQval(se = prism_mbx, # mod.assn = prism_modules, # metadata_variable = "diagnosis", # fixed_effects = c("diagnosis","age","antibiotics"), # reference = c("diagnosis,Control";"antibiotics,No"))