# False Discovery Rate (FDR) Control Integrating Prior Tree Structure for Microbiome Data Based on F-test and Residual Permutation. The function is a wrapper of `TreeFDR` with a pre-defined test based on F-statistic and residual permutation. ```r MicrobiomeSeqTreeFDR (otu.tab, tree, meta.dat, grp.name, adj.name = NULL, raw.count = FALSE, B = 100, ...) ``` ## Arguments - `otu.tab`: a data matrix, rows are the features and columns are the samples. - `tree`: an object of "phylo" class. The tree of OTUs. - `meta.dat`: a data frame containing the variables specified by `grp.name` and `adj.name`. - `grp.name`: a character string indicating the variable of major interest. Can be categorical or numerical. - `adj.name`: a character vector indicating the variables to be adjusted. Can be categorical or numerical. - `raw.count`: a logical value indicating whether `X` are raw counts. If raw counts are supplied, internal normalization/transformation (GMPR/sqrt) will be performed. The default is FALSE. The user should be responsible for selecting the appropriate normalization/transformation methods. - `B`: the number of permutations. The default is 100 since the permutation test is very fast. - `...`: further arguments such as covariates to be passed to `TreeFDR` ## Returns - **p.adj**: TreeFDR adjusted p-values. - **p.unadj**: raw p-values. - **z.adj**: moderated z-values. The scale may be different from the raw z-values. - **z.unadj**: raw z-values. - **k, rho**: the estimates of the hyperparameters. The values indicate the informativeness of the prior structure. ## References Xiao, J., Cao, H., Chen, J. (2017). False discovery rate control incorporating phylogenetic tree increases detection power in microbiome-wide multiple testing. Bioinformatics, 33(18), 2873-2881. ## Author(s) Jun Chen ## See Also `TreeFDR` ## Examples ```r require(StructFDR) # Generate data data(throat.parameter) data.obj <- SimulateData(nCases = 50, nControls = 50, nOTU = 400, nCluster = 20, depth = 10000, p.est = throat.parameter$p.est, theta = throat.parameter$theta, scene = 'S2', signal.strength = 4) meta.dat <- data.frame(group = factor(data.obj$y), sex = sample(gl(2, 50))) beta.true <- data.obj$beta.true # Call TreeFDR tree.fdr.obj <- MicrobiomeSeqTreeFDR(data.obj$X, data.obj$tree, meta.dat, 'group', 'sex', B = 20) tree.p.adj <- tree.fdr.obj$p.adj # Empirical power and FDR (tree.emp.pwr <- sum(tree.p.adj <= 0.05 & beta.true != 0) / sum(beta.true != 0)) (tree.emp.fdr <- sum(tree.p.adj <= 0.05 & beta.true == 0) / sum(tree.p.adj <= 0.05)) ```

MicrobiomeSeqTreeFDR function