geneTestMeta {rqt} | R Documentation |
This function performs a gene-level meta-analysis based on combined effect sizes.
This function performs a gene-level meta-analysis based on combined effect sizes.
geneTestMeta(objects, ...) ## S4 method for signature 'list' geneTestMeta(objects, perm = 0, STT = 0.2, weight = FALSE, cumvar.threshold = 75, out.type = "D", method = "pca", scaleData = FALSE, asym.pval = FALSE, comb.test = "wilkinson", penalty = 0.001, verbose = FALSE)
objects |
List of objects of class rqt |
... |
Additional parameters to pass to the function |
perm |
Integer indicating the number of permutations to compute p-values. Default: 0. |
STT |
Numeric indicating soft truncation threshold (STT) to convert to gamma parameter (must be <= 0.4). Needed for an optimal parameter a in Gamma-distribution. Default: 0.2. See, for example, Fridley, et al 2013: "Soft truncation thresholding for gene set analysis of RNA-seq data: Application to a vaccine study". |
weight |
Logical value. Indicates using weights (see Lee et al 2016). Default: FALSE. |
cumvar.threshold |
Numeric value indicating the explained variance threshold for PCA-like methods. Default: 75 |
out.type |
Character, indicating a type of phenotype.
Possible values: |
method |
Method used to reduce multicollinerity and account for LD.
Default: |
scaleData |
A logic parameter (TRUE/FALSE) indicating scaling of the genotype dataset. |
asym.pval |
Indicates Monte Carlo approximation for p-values. Default: FALSE. |
comb.test |
Statistical test for combining p-values. |
penalty |
Value of |
verbose |
Indicates verbosing output. Default: FALSE. |
A list of two: (i) final.pvalue - a final p-value across all studies; (ii) pvalueList - p-values for each study;
A list of two: (i) final.pvalue - a final p-value across all studies; (ii) pvalueList - p-values for each study;
data1 <- data.matrix(read.table(system.file("extdata/phengen2.dat", package="rqt"), skip=1)) pheno <- data1[,1] geno <- data1[, 2:dim(data1)[2]] colnames(geno) <- paste(seq(1, dim(geno)[2])) geno.obj <- SummarizedExperiment(geno) obj1 <- rqt(phenotype=pheno, genotype=geno.obj) data2 <- data.matrix(read.table(system.file("extdata/phengen3.dat", package="rqt"), skip=1)) pheno <- data2[,1] geno <- data2[, 2:dim(data2)[2]] colnames(geno) <- paste(seq(1, dim(geno)[2])) geno.obj <- SummarizedExperiment(geno) obj2 <- rqt(phenotype=pheno, genotype=geno.obj) data3 <- data.matrix(read.table(system.file("extdata/phengen.dat", package="rqt"), skip=1)) pheno <- data3[,1] geno <- data3[, 2:dim(data3)[2]] colnames(geno) <- paste(seq(1, dim(geno)[2])) geno.obj <- SummarizedExperiment(geno) obj3 <- rqt(phenotype=pheno, genotype=geno.obj) res.meta <- geneTestMeta(list(obj1, obj2, obj3)) print(res.meta)