twoStageiLCA.rank: Two-staged Independent LCA and automatic rank selection

In CHuanSite/SJD: Structured Joint Decomposition (SJD)

Two-staged Independent LCA and automatic rank selection

Description

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 dataset(s) to be projected on.
proj_group	A listed of boolean combinations indicating which groupings should be used for each projected dataset.The length of proj_group should match the length of proj_dataset, and the length of each concatenated boolean combination should match the length of the parameter group.
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)

CHuanSite/SJD documentation built on Nov. 29, 2024, 5:52 a.m.