met.normalize: Data normalization

In NicWir/VisomX: User-Friendly and Comprehensive Analysis and Visualization of Omics Data

Data normalization

Description

met.normalize performs row-wise normalization, transformation, and scaling of metabolomics data. This step is performed as part of the met.workflow function. Additionally, the workflow met.test_normalization allows the simultaneous testing of different data processing conditions and helps with finding the most suitable options.

Usage

met.normalize(
  mSetObj = NA,
  rowNorm = NULL,
  transNorm = NULL,
  scaleNorm = NULL,
  ref = NULL,
  norm.vec = NULL,
  ratio = FALSE,
  ratioNum = 20
)

Arguments

mSetObj	Enter the name of the created mSet object (see met.read_data).
rowNorm	(Character) Select the option for row-wise normalization: "GroupPQN" for probabilistic quotient normalization by a reference group "SamplePQN" for probabilistic quotient normalization by a reference sample "QuantileNorm" for Quantile Normalization "CompNorm" for normalization by a reference feature "SumNorm" for normalization to constant sum of intensities "MedianNorm" for normalization to sample median "SpecNorm" for normalization by a sample-specific factor
transNorm	(Character) Select option to transform the data: "LogNorm" for Log10 normalization "CrNorm" Cubic Root Transformation
scaleNorm	(Character) Select option for scaling the data: "MeanCenter" for Mean Centering "AutoNorm" for Autoscaling "ParetoNorm" for Pareto Scaling "RangeNorm" for Range Scaling
ref	(Character) Enter the name of the reference sample or the reference feature (if rowNorm = "GroupPQN", "SamplePQN", or "CompNorm".
norm.vec	(Numeric vector) Vector with sample-specific scaling factors. Only applicable for rowNorm = "SpecNorm".
ratio	This option is only for biomarker analysis.
ratioNum	Relevant only for biomarker analysis.

Value

The input mSet object with normalized data at mSetObj$dataSet$norm.

Author(s)

Nicolas T. Wirth [e-mail] Technical University of Denmark License: GNU GPL (>= 2)

References

adapted from Normalization (https://github.com/xia-lab/MetaboAnalystR).

NicWir/VisomX documentation built on Dec. 8, 2024, 1:27 a.m.