MMINP.preprocess: Data Preprocessing function for MMINP

In MMINP: Microbe-Metabolite Interactions-Based Metabolic Profiles Predictor

Data Preprocessing function for MMINP

Description

Before doing MMINP analysis, abundances of both microbial features and metabolites should be preprocessed. Both measurements are expected to be transformed to relative abundance (i.e. proportion) and be log-transformed. To meet the need of O2-PLS method, data must be scaled.

Usage

MMINP.preprocess(
  data,
  normalized = TRUE,
  prev = NA,
  abund = NA,
  logtransformed = TRUE,
  scaled = TRUE
)

Arguments

data	A numeric matrix or data frame containing measurements of metabolites or microbial features.
normalized	Logical, whether to transform measurements into relative abundance or not.
prev	A numeric ranging from 0 to 1, the minimum prevalence of features to be retained. If set to NA, means no need to filter prevalence.
abund	A numeric greater than 0, the minimum abundance (mean) of features to be retained. If set to NA, means no need to filter abundance.
logtransformed	Logical, whether do log transformation or not.
scaled	Logical, whether scale the columns of data or not.

Details

The rows of data must be samples and columns of data must be metabolites or microbial features. The filtering process (prev and abund) is before log transformation and scale transformation.

Value

A preprocessed numeric matrix for analysis of MMINP.

Examples

data(train_metag)
d <- MMINP.preprocess(train_metag)
d <- MMINP.preprocess(train_metag, prev = 0.3, abund = 0.001)
d[1:5, 1:5]

MMINP documentation built on July 5, 2022, 1:08 a.m.