[database] # The path of uniref databases folder. uniref_db = # The path of domain databases folder. [data_path] provide the absolute path of the domain databases folder; [none] use the default domain databases installed in the metawibele package. [ Default: none ] domain_db = none [basic] # Study name. [ Default: MGX ] study = MGX # The prefix name for output results. [ Default: metawibele ] basename = metawibele [computation] # The number of cores that you are requesting. [ Default: 1 ] threads = 1 # The amount of memory (in MB) that you will be using for your job. [ Default: 20000 ] memory = 20000 # The amount of time (in minute) that you will be using for your job. [ Default: 60 ] time = 60 [abundance] # The method for normalization [Choices: cpm, relab]. [cpm] copies per million units (sum to 1 million); [relab] relative abundance (sum to 1). [ Default: cpm ] normalize = cpm # The minimum abundance for each feature. [ Default: 0 ] abundance_detection_level = 0 [msp] # The minimum fraction of taxonomy classified genes in each MSP [0-1]. [Default: 0.10] tshld_classified = 0.10 # The minimum percent differences between the most and second dominant taxon for each MSP [0-1]. [Default: 0.50] tshld_diff = 0.50 [interproscan] # Interproscan executable file, e.g. /my/path/interproscan/interproscan.sh [ Default: interproscan.sh ] interproscan_cmmd = interproscan.sh # The appls used by interproscan: [appls] comma separated list of analyses, [ Choices: CDD,COILS,Gene3D,HAMAP,MobiDBLite,PANTHER,Pfam,PIRSF,PRINTS,PROSITEPATTERNS,PROSITEPROFILES,SFLD,SMART,SUPERFAMILY,TIGRFAM,Phobius,SignalP,TMHMM ]; [all] use all analyses for running. [ Default: all ] interproscan_appl = all # The number of splitting files which can be annotated in parallel. [ Default: 1 ] split_number = 1 [maaslin2] # Maaslin2 executable file, e.g. /my/path/Maaslin2/R/Maaslin2.R, [ Default: Maaslin2.R ] maaslin2_cmmd = Maaslin2.R # The minimum abundance for each feature. [ Default: 0 ] min_abundance = 0 # The minimum percent of samples for which a feature is detected at minimum abundance. [ Default: 0.1 ] min_prevalence = 0.1 # Keep features with variance greater than. [Default: 0.0] min_variance = 0 # The q-value threshold for significance. [ Default: 0.25 ] max_significance = 0.25 # The normalization method to apply [ Choices: TSS, CLR, CSS, NONE, TMM ]. [ Default: TSS ] normalization = NONE # The transform to apply [ Choices: LOG, LOGIT, AST, NONE ]. [ Default: LOG ] transform = LOG # The analysis method to apply [ Choices: LM, CPLM, ZICP, NEGBIN, ZINB ]. [ Default: LM ] analysis_method = LM # The fixed effects for the model, comma-delimited for multiple effects. [ Default: all ] fixed_effects = all # The random effects for the model, comma-delimited for multiple effects. [ Default: none ] random_effects = none # The correction method for computing the q-value. [ Default: BH ] correction = BH # Apply z-score so continuous metadata are on the same scale. [ Default: TRUE ] standardize = TRUE # Generate a heatmap for the significant associations. [ Default: FALSE ] plot_heatmap = FALSE # In heatmap, plot top N features with significant associations. [ Default: FALSE ] heatmap_first_n = FALSE # Generate scatter plots for the significant associations. [ Default: FALSE ] plot_scatter = FALSE # The number of R processes to run in parallel. [ Default: 1 ] maaslin2_cores = 1 # The factor to use as a reference for a variable with more than two levels provided as a string of 'variable,reference' semi-colon delimited for multiple variables. NOTE: A space between the variable and reference will not error but will cause an inaccurate result. [ Default: NA ] reference = NA # The minimum percent of case-control samples used for comparison in which a feature is detected. [ Default: 0.1 ] tshld_prevalence = 0.10 # The q-value threshold for significance used as DA annotations. [ Default: 0.05 ] tshld_qvalue = 0.05 # The statistic used as effect size [ Choices: coef, mean(log) ]. [coef] represents the coefficient from the model; [mean(log)] represents differences of mean log-scaled abundances between case and control conditions. [ Default: mean(log) ] effect_size = mean(log) # The main phenotype metadata used for prioritization, e.g. variable. [ Default: none ]: skip the association with environmental/phenotypic parameters phenotype = none