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". Quantile is often used when >100 samples.
method	Character. The normalization method to use. Options include "none" (no normalization), "auto_scaling" (autoscaling, i.e., Z-score normalization), "level_scaling" (level scaling), "log_scaling" (log scaling), "log" (log transformation), "vast_scaling" (vast scaling), "log_pareto" (log Pareto scaling), "min_max" (min-max), 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

# Output is a normalized SummarizedExperiment object
data <- POMA::st000284 # Example SummarizedExperiment object included in POMA

# No sample normalization
data %>% 
  PomaNorm(sample_norm = "none", 
           method = "log_pareto")

# Sum sample normalization
data %>% 
  PomaNorm(sample_norm = "sum", 
           method = "log_pareto")

pcastellanoescuder/POMA documentation built on Nov. 28, 2024, 1:21 p.m.