PomaNorm: Normalize Data

In pcastellanoescuder/POMA: Tools for Omics Data Analysis

Normalize Data

Description

PomaNorm performs data normalization using various normalization methods.

Usage

PomaNorm(data, sample_norm = "none", method = "log_pareto")

Arguments

data	A SummarizedExperiment object.
sample_norm	Character. Sample normalization method. Options include "none" (default), "sum", or "quantile".
method	Character. The normalization method to use. Options include "none" (no normalization), "auto_scaling" (autoscaling normalization, i.e., Z-score normalization), "level_scaling" (level scaling normalization), "log_scaling" (log scaling normalization), "log_transform" (log transformation normalization), "vast_scaling" (vast scaling normalization), "log_pareto" (log Pareto scaling normalization), "min_max" (min-max normalization), and "box_cox" (Box-Cox transformation).

Value

A SummarizedExperiment object with normalized data.

Author(s)

Pol Castellano-Escuder

References

Van den Berg, R. A., Hoefsloot, H. C., Westerhuis, J. A., Smilde, A. K., & van der Werf, M. J. (2006). Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC genomics, 7(1), 142.

Examples

data("st000284")

PomaNorm(st000284, method = "log_pareto")

pcastellanoescuder/POMA documentation built on March 15, 2024, 10:08 p.m.