bootMbpca: Bootstrap mbpca to estimate the coherence of different data...

In mengchen18/mogsa: Multiple omics data integrative clustering and gene set analysis

Description

Bootstrap mbpca to estimate the coherence of different data sets and estimate the number of components should be included in an analysis.

Usage

bootMbpca(
  moa,
  mc.cores = 1,
  B = 100,
  replace = TRUE,
  resample = c("sample", "gene", "total"),
  log = "y",
  ncomp = NULL,
  method = NULL,
  maxiter = 1000,
  svd.solver = c("svd", "fast.svd", "propack"),
  plot = TRUE
)

Arguments

moa	An object of moa returned by mbpca.
mc.cores	Integer; number of cores used in bootstrap. This value is passed to function mclapply
B	Integer; number of bootstrap
replace	Logical; sampling with or without replacement
resample	Could be one of "sample", "gene" or "total". "sample" and "gene" means sample-wise and variable-wise resampling, repectively. "total" means total resampling.
log	Could be "x", "y" or "xy" for plot log axis
ncomp	Passed to function mbpca. In most of cases, user do not need to specify this argument because it could be inferred from moa.
method	Passed to function mbpca.In most of cases, user do not need to specify this argument because it could be inferred from moa.
maxiter	Passed to function mbpca.In most of cases, user do not need to specify this argument because it could be inferred from moa.
svd.solver	Passed to function mbpca.In most of cases, user do not need to specify this argument because it could be inferred from moa.
plot	Logical; whether the result should be plotted.

Details

Bootstrap method were used to determine the components that are presenting significant concordant structure between datasets.

Value

It returns a matrix, columns are eigenvalues for different components. Each rows is a bootstramp sample.

Author(s)

Chen Meng

Examples

# see examples in \code{\link{mbpca}}

mengchen18/mogsa documentation built on June 7, 2020, 6:05 p.m.