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 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 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)