# Select transcripts/genes with significant p-values. Select transcripts with global p-value lower than an user defined threshold and provide a summary of over- or under-expression according to local p-values. ```r RN_select(Results, gpv_t = 0.01, lpv_t = 0.01, method = "BH") ``` ## Arguments - `Results`: The output of RNentropy or RN_calc. - `gpv_t`: Threshold for global p-value. (Default: 0.01) - `lpv_t`: Threshold for local p-value. (Default: 0.01) - `method`: Multiple test correction method. Available methods are the ones of p.adjust. Type p.adjust.methods to see the list. Default: BH (Benjamini & Hochberg) ## Returns The original input containing - **gpv**: -log10 of the global p-values - **lpv**: -log10 of the local p-values - **c_like**: results formatted as in the output of the C++ implementation of RNentropy. - **res**: The results data.frame containing the original expression values together with the -log10 of global and local p-values. - **design**: The experimental design matrix. and a new dataframe - **selected**: Transcripts/genes with a corrected global p-value lower than gpv_t. For each condition it will contain a column where values can be -1,0,1 or NA. 1 means that all the replicates of this condition have expression value higher than the average and local p-value <= lpv_t (thus the corresponding gene will be over-expressed in this condition). -1 means that all the replicates of this condition have expression value lower than the average and local p-value <= lpv_t (thus the corresponding gene will be under-expressed in this condition). 0 means that at least one of the replicates has a local p-value > lpv_t. NA means that the local p-values of the replicates are not consistent for this condition, that is, at least one replicate results to be over-expressed and at least one results to be under-expressed. ## Author(s) Giulio Pavesi - Dep. of Biosciences, University of Milan Federico Zambelli - Dep. of Biosciences, University of Milan ## Examples ```r data("RN_Brain_Example_tpm", "RN_Brain_Example_design") #compute statistics and p-values (considering only a subset of genes due to #examples running time limit of CRAN) Results <- RN_calc(RN_Brain_Example_tpm[1:10000,], RN_Brain_Example_design) Results <- RN_select(Results) ## The function is currently defined as function (Results, gpv_t = 0.01, lpv_t = 0.01, method = "BH") { lpv_t <- -log10(lpv_t) gpv_t <- -log10(gpv_t) Results$gpv_bh <- -log10(p.adjust(10^-Results$gpv, method = method)) true_rows <- (Results$gpv_bh >= gpv_t) design_b <- t(Results$design > 0) Results$lpv_sel <- data.frame(row.names = rownames(Results$lpv)[true_rows]) for (d in seq_along(design_b[, 1])) { col <- apply(Results$lpv[true_rows, ], 1, ".RN_select_lpv_row", design_b[d, ], lpv_t) Results$lpv_sel <- cbind(Results$lpv_sel, col) colnames(Results$lpv_sel)[length(Results$lpv_sel)] <- paste("condition", d, sep = "_") } lbl <- Results$res[, !sapply(Results$res, is.numeric)] Results$selected <- cbind(lbl[true_rows], Results$gpv[true_rows], Results$gpv_bh[true_rows], Results$lpv_sel) colnames(Results$selected) <- c(names(which(!sapply(Results$res, is.numeric))), "GL_LPV", "Corr. GL_LPV", colnames(Results$lpv_sel)) Results$selected <- Results$selected[order(Results$selected[,3], decreasing=TRUE),] Results$lpv_sel <- NULL return(Results) } ```

RN_select function