meta.spca {iSFun}

Meta-analytic sparse principal component analysis method in integrative study

Description

This function provides penalty-based sparse principal component meta-analytic method to handle the multiple datasets with high dimensions generated under similar protocols, which is based on the principle of maximizing the summary statistics S.

Usage

meta.spca(x, L, mu1, eps = 1e-04, scale.x = TRUE, maxstep = 50,
  trace = FALSE)

Arguments

x

list of data matrices, L datasets of explanatory variables.

L

numeric, number of datasets.

mu1

numeric, sparsity penalty parameter.

eps

numeric, the threshold at which the algorithm terminates.

scale.x

character, "TRUE" or "FALSE", whether or not to scale the variables x. The default is TRUE.

maxstep

numeric, maximum iteration steps. The default value is 50.

trace

character, "TRUE" or "FALSE". If TRUE, prints out its screening results of variables.

Value

A 'meta.spca' object that contains the list of the following items.

• x: list of data matrices, L datasets of explanatory variables with centered columns. If scale.x is TRUE, the columns of L datasets are standardized to have mean 0 and standard deviation 1.

• eigenvalue: the estimated first eigenvalue.

• eigenvector: the estimated first eigenvector.

• component: the estimated first component.

• variable: the screening results of variables.

• meanx: list of numeric vectors, column mean of the original datasets x.

• normx: list of numeric vectors, column standard deviation of the original datasets x.

References

• Kim S H, Kang D, Huo Z, et al. Meta-analytic principal component analysis in integrative omics application[J]. Bioinformatics, 2018, 34(8): 1321-1328.

See Also

See Also as ispca, spca.

Examples

library(iSFun)
data("simData.pca")
x <- simData.pca$x
L <- length(x)

res <- meta.spca(x = x, L = L, mu1 = 0.5, trace = TRUE)