Single Data Set Decomposition with Principal Component Analysis — sepPCA • SJD

Apply PCA (Principal Component Analysis) to a single data set

Usage

sepPCA(
  dataset,
  comp_num,
  weighting = NULL,
  enable_normalization = TRUE,
  column_sum_normalization = FALSE,
  screen_prob = NULL
)

Arguments

dataset: A dataframe/matrix to be decomposed
comp_num: Number of PCs to be extracted
weighting: Weighting of each dataset, initialized to be NULL
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 of scores and component

Examples

dataset = list(matrix(runif(5000, 1, 2), nrow = 100, ncol = 50))
comp_num = 2
res_sepPCA = sepPCA(dataset, comp_num)