Two-staged Independent LCA and automatic rank selection

Two-staged decomposition of several matrices with Independent LCA, twoStageLCA is first performed on the data, the rank selection procedure is automatic based on BEMA. Then, fastICA is implemented on the score to extract the independent components.

Usage

twoStageiLCA.rank(
  dataset,
  group,
  weighting = NULL,
  total_number = NULL,
  threshold,
  backup = 0,
  plotting = FALSE,
  proj_dataset = NULL,
  proj_group = NULL,
  enable_normalization = TRUE,
  column_sum_normalization = FALSE,
  screen_prob = NULL
)

Arguments

dataset

A list of dataset to be analyzed

group

A list of grouping of the datasets, indicating the relationship between datasets

weighting

Weighting of each dataset, initialized to be NULL

total_number

Total number of components will be extracted, if default value is set to NA, then BEMA will be used.

threshold

The threshold used to cutoff the eigenvalues

backup

A backup variable, which permits the overselection of the components by BEMA

plotting

A boolean value to determine whether to plot the scree plot or not, default to be False

proj_dataset

The datasets to be projected on

proj_group

The grouping of projected data sets

enable_normalization

An argument to decide whether to use normalizaiton or not, default is TRUE

column_sum_normalization

An argument to decide whether to use column sum normalization or not, default it FALSE

screen_prob

A vector of probabilies for genes to be chosen

Value

A list contains the component and the score of each dataset on every component after twoStageiLCA.rank algorithm

Examples

dataset = list(matrix(runif(5000, 1, 2), nrow = 100, ncol = 50),
matrix(runif(5000, 1, 2), nrow = 100, ncol = 50),
matrix(runif(5000, 1, 2), nrow = 100, ncol = 50),
matrix(runif(5000, 1, 2), nrow = 100, ncol = 50))
group = list(c(1, 2, 3, 4), c(1, 2), c(3, 4), c(1, 3), c(2, 4), c(1), c(2), c(3), c(4))
threshold = c(3, 1.5, 1.5, 1.5, 1.5, 0.5, 0.5, 0.5, 0.5)
res_twoStageiLCA.rank = twoStageiLCA.rank(
dataset,
group,
threshold = threshold)