# Integrative GSEA Approaches This package provides three approaches, testing based on an fixed-effect (FE) model (iGSEA-FE), testing based on a random-effect (RE) model (iGSEA-RE), and adaptive testing (iGSEA-AT) to integrate multiple gene set enrichment studies. These approaches can be applied to both binary and continuous phenotypes. The output of the function will be the Q-values of gene sets to control the false discovery rate (FDR). We recommend iGSEA-AT due to its stability in performance in various practical situations. package ```r igsea.test(gel, pheno, ssize, gind, gsind, B = 500, vtype = "binary", method = "AT", alpha1 = 0.0253, pihat = 1) ``` ## Arguments - `gel`: a numeric matrix of gene expression levels merged from component studies. Rows represent genes and columns represent samples. - `pheno`: a numeric vector of phenotypes merged from component studies. - `ssize`: a numeric vector indicating the number of samples in each study. - `gind`: a matrix indicating if genes are included in studies. Use 1 as included and 0 as not. Rows represent genes and columns represent studies. - `gsind`: a matrix indicating if genes belongs to gene sets. Use 1 as in a gene set and 0 as not. Rows represent genes and columns represent gene sets. - `B`: an integer indicating the times of shuffling gene labels in order to compute the permuted enrichment scores. It is 500 by default. - `vtype`: a character string specifying the type of phenotypes. It can only be "binary" or "continuous" at this moment. - `method`: a character string specifying the approach you want to use, must be one of "AT" (default), "FE", or "RE". - `alpha1`: a number indicating the first-stage significance level for iGSEA-AT. It is 0.0253 by default. - `pihat`: a number indicating a rough estimate of the proportion of non-enriched sets. It is 1 by default. ## Returns the Q-values of gene sets after false discovery rate (FDR) procedure ## Details ||| |:--|:--| |Package:|iGSEA| |Type:|Package| |Version:|1.2| |Date:|2017-05-07| |License:|GPL-2| The core function in this package is `igsea.test`, which checks whether your input is correct at the very beginning. Specifically, the length of the vector `pheno`, the number of columns of the matrix `gel`, and the sum of the vector `ssize` should be equal, indicating the total number of samples. The number of rows of the matrix `gel`, the matrix `gind`, and the matrix `gsind` should be equal, indicating the total number genes involved in studies. The number of columns of the matrix `gind` and the length of the vector `ssize` should be equal, indicating the number of studies. Please note genes with the same IDs should mean the same genes in reality. In the matrix gind, a gene should be involved in at least one study. The approaches account for different set sizes when testing multiple gene sets. The parameter alpha1 should be greater than 0 and smaller than the overall significance level alpha. The choice of alpha1 could be chosen based on some exploratory data analysis or the prior knowledge about the between-study heterogeneity. It slightly influences the performance of iGSEA-AT. The default value is chosen to be 1-sqrt(1-alpha). When alpha=0.05, the default value is 0.0253. ## Author(s) Wentao Lu, Xinlei Wang, Xiaowei Zhan Maintainer: Wentao Lu ## References Lu, Wentao (2016), Meta-analysis approaches to combine multiple gene set enrichment studies. Unpublished doctoral dissertation, Southern Methodist University. ## Examples ```r #Set seed to make sure the permutaiton test gives the same results set.seed(1234) #In the following binray example, there are 200 genes in total. #Genes 1-40 are set to be up-regulated genes as their gene expression levels are #associated with phenotypes. #The remaining 160 genes are equally expressed genes. #Gene set 1, which contains 40% up-regulated genes, is enriched. #Gene set 2, which contains 20% up-regulated genes, is not enriched. #As there are no RE genes, FE and AT should perform well. G <- matrix(rnorm(200 * 60), c(200, 60)) #200 genes and 60 samples in total P <- c(rep(c(rep(1, 5), rep(0, 5)), 2), rep(c(rep(1, 10), rep(0, 10)), 2)) #phenotypes G[1:40, c(1:5, 11:15, 21:30, 41:50)] <- G[1:40, c(1:5, 11:15, 21:30, 41:50)] + 0.45 S <- c(10, 10, 20, 20) #the number of samples in each study I <- matrix(rep(1, 200*4), 200) #all genes are included in 4 studies GS <- matrix(0, 200, 2) GS[c(1:20, 151:180), 1] <- 1 #gene set 1 is enriched GS[c(31, 80), 2] <- 1 #gene set 2 is non-enriched igsea.test(G, P, S, I, GS) #the output vector consists of two Q-values for the gene sets #A similar normal example is also provided below: set.seed(1234) G <- matrix(rnorm(200 * 60), c(200, 60)) #200 genes and 60 samples in total P <- rnorm(60) #phenotypes S <- c(10, 10, 20, 20) #the number of samples in each study rho_raw <- matrix(0, 200, 4) for (i in 1:40) rho_raw[i, ] <- rnorm(4, mean = 0.3, sd = 0.1) beta <- matrix(0, 200, 60) for (i in 1:200) beta[i, ] <- beta[i, ] + c(rep(rho_raw[i, 1], 10), rep(rho_raw[i, 2], 10), rep(rho_raw[i, 3], 20), rep(rho_raw[i, 4], 20)) for (i in 1:200) { for (j in 1:60){ G[i, j] <- rnorm(1, mean = beta[i, j] * P[j], sd = sqrt(1 - beta[i, j] ^ 2)) } } I <- matrix(rep(1, 200*4), 200) #all genes are included in 4 studies GS <- matrix(0, 200, 2) GS[c(1:20, 151:180), 1] <- 1 #gene set 1 is enriched GS[c(31, 80), 2] <- 1 #gene set 2 is non-enriched igsea.test(G, P, S, I, GS, vtype = "continuous") ```

iGSEA-package