dataPreprocess: preprocess a omics dataset before running omics SmCCNet

In SmCCNet: Sparse Multiple Canonical Correlation Network Analysis Tool

preprocess a omics dataset before running omics SmCCNet

Description

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)

SmCCNet documentation built on May 29, 2024, 10:49 a.m.