preprocess a omics dataset before running omics SmCCNet — dataPreprocess • SmCCNet

Data preprocess pipeline to: (1) filter by coefficient of variation (cv), (2) center or scale data and (3) adjust for clinical covariates.

Usage

dataPreprocess(
  X,
  covariates = NULL,
  is_cv = FALSE,
  cv_quantile = 0,
  center = TRUE,
  scale = TRUE
)

Arguments

X: dataframe with the size of \(n\) by \(p\), where \(n\) is the sample size and \(p\) is the feature size.
covariates: dataframe with covariates to be adjusted for.
is_cv: Whether to use coefficient of variation filter (small cv filter out).
cv_quantile: CV filtering quantile.
center: Whether to center the dataset X.
scale: Whether to scale the dataset X.

Value

Processed omics data with the size of nxp.

Examples


X1 <- as.data.frame(matrix(rnorm(600, 0, 1), nrow = 60))
covar <- as.data.frame(matrix(rnorm(120, 0, 1), nrow = 60))
processed_data <- dataPreprocess(X = X1, covariates = covar, is_cv = TRUE, 
cv_quantile = 0.5, center = TRUE, scale = TRUE)