twoStageLCA.rank: Two-staged LCA and automatic rank selection

In CHuanSite/SJD: Structured Joint Decomposition (SJD)

Two-staged LCA and automatic rank selection

Description

Two-staged decomposition of several matrices with LCA, the rank selection procedure is automatic based on BEMA

Usage

twoStageLCA.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 seqPCA 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_twoStageLCA.rank = twoStageLCA.rank(
dataset,
group,
threshold = threshold)

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