meta.spca | R Documentation |
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.
meta.spca(x, L, mu1, eps = 1e-04, scale.x = TRUE, maxstep = 50, trace = FALSE)
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. |
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.
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 as ispca
, spca
.
library(iSFun) data("simData.pca") x <- simData.pca$x L <- length(x) res <- meta.spca(x = x, L = L, mu1 = 0.5, trace = TRUE)
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