normalize.features: Perform feature normalization

In SIAMCAT: Statistical Inference of Associations between Microbial Communities And host phenoTypes

Description

This function performs feature normalization according to user- specified parameters.

Usage

normalize.features(siamcat,
    norm.method = c("rank.unit", "rank.std",
        "log.std", "log.unit", "log.clr", "std", "pass"),
    norm.param = list(log.n0 = 1e-06, sd.min.q = 0.1,
        n.p = 2, norm.margin = 1),
    feature.type='filtered',
    verbose = 1)

Arguments

siamcat	an object of class siamcat-class
norm.method	string, normalization method, can be one of these: 'c('rank.unit', 'rank.std', 'log.std', 'log.unit', 'log.clr', 'std', 'pass')
norm.param	list, specifying the parameters of the different normalization methods, see details for more information
feature.type	string, on which type of features should the function work? Can be either "original", "filtered", or "normalized". Please only change this paramter if you know what you are doing!
verbose	integer, control output: 0 for no output at all, 1 for only information about progress and success, 2 for normal level of information and 3 for full debug information, defaults to 1

Details

There are seven different normalization methods available:

• 'rank.unit' - converts features to ranks and normalizes each column (=sample) by the square root of the sum of ranks

• 'rank.std' - converts features to ranks and applies z-score standardization

• 'log.clr' - centered log-ratio transformation (with the addition of pseudocounts)

• 'log.std' - log-transforms features (after addition of pseudocounts) and applies z-score standardization

• 'log.unit' - log-transforms features (after addition of pseudocounts) and normalizes by features or samples with different norms

• 'std' - z-score standardization without any other transformation

• 'pass' - pass-through normalization will not change the features

The list entries in 'norm.param' specify the normalzation parameters, which are dependant on the normalization method of choice:

• 'rank.unit' or 'pass' does not require any other parameters

• 'rank.std' and 'std' requires sd.min.q, quantile of the distribution of standard deviations of all features that will be added to the denominator during standardization in order to avoid underestimation of the standard deviation, defaults to 0.1

• 'log.clr' requires log.n0, which is the pseudocount to be added before log-transformation, defaults to NULL leading to the estimation of log.n0 from the data

• 'log.std' requires both log.n0 and sd.min.q, using the same default values

• 'log.unit' requires next to log.n0 also the parameters n.p and norm.margin. n.p specifies the vector norm to be used, can be either 1 for x/sum(x) or 2 for x/sqrt(sum(x^2)). The parameter norm.margin specifies the margin over which to normalize, similarly to the apply-syntax: Allowed values are 1 for normalization over features, 2 over samples, and 3 for normalization by the global maximum.

The function additionally allows to perform a frozen normalization on a different dataset. After normalizing the first dataset, the norm_feat slot in the siamcat object contains all parameters of the normalization, which you can access via the norm_params accessor.

In order to perform a frozen normalization of a new dataset, you can run the function supplying the normalization parameters as argument to norm.param: norm.param=norm_params(siamcat_reference). See also the example below.

Value

an object of class siamcat-class with normalized features

Examples

# Example data
data(siamcat_example)

# Simple example
siamcat_norm <- normalize.features(siamcat_example,
    norm.method='rank.unit')

# log.unit example
siamcat_norm <- normalize.features(siamcat_example,
    norm.method='log.unit',
    norm.param=list(log.n0=1e-05, n.p=1, norm.margin=1))

# log.std example
siamcat_norm <- normalize.features(siamcat_example,
    norm.method='log.std',
    norm.param=list(log.n0=1e-05, sd.min.q=.1))

# Frozen normalization
siamcat_norm <- normalize.features(siamcat,
    norm.param=norm_params(siamcat_reference))

SIAMCAT documentation built on Nov. 8, 2020, 5:14 p.m.