MMINP.train {MMINP}

Train MMINP model using paired microbial features and metabolites data

Description

This function contains three steps. Step1, Build an O2-PLS model and use it to predict metabolites profile; Step2, Compare predicted and measured metabolites abundances, then filter those metabolites which predicted poorly (i.e. metabolites of which correlation coefficient less than rsignif or adjusted pvalue greater than psignif.); Step3, (iteration) Re-build O2-PLS model until all reserved metabolites are well-fitted.

Usage

MMINP.train(
  metag,
  metab,
  n = 1:3,
  nx = 0:3,
  ny = 0:3,
  seed = 1234,
  compmethod = NULL,
  nr_folds = 3,
  nr_cores = 1,
  rsignif = 0.4,
  psignif = 0.05,
  recomponent = FALSE
)

Arguments

metag

Training data of sequence features' relative abundances. Must have the exact same rows (subjects/samples) as metab.

metab

Training data of metabolite relative abundances. Must have the exact same rows (subjects/samples) as metag.

n

Integer. Number of joint PLS components. Must be positive. More details in crossval_o2m and crossval_o2m_adjR2.

nx

Integer. Number of orthogonal components in metag. Negative values are interpreted as 0. More details in crossval_o2m and crossval_o2m_adjR2.

ny

Integer. Number of orthogonal components in metab. Negative values are interpreted as 0. More details in crossval_o2m and crossval_o2m_adjR2.

seed

a random seed to make the analysis reproducible, default is 1234.

compmethod

A character string indicating which Cross-validate procedure of O2PLS is to be used for estimating components, must be one of "NULL", "cvo2m" or "cvo2m.adj". If set to "NULL", depends on the features number.

nr_folds

Positive integer. Number of folds to consider. Note: kcv=N gives leave-one-out CV. Note that CV with less than two folds does not make sense. More details in crossval_o2m and crossval_o2m_adjR2.

nr_cores

Positive integer. Number of cores to use for CV. You might want to use detectCores(). Defaults to 1. More details in crossval_o2m and crossval_o2m_adjR2.

rsignif

A numeric ranging from 0 to 1, the minimum correlation coefficient of features which considered as well-predicted features.

psignif

A numeric ranging from 0 to 1, the maximum adjusted p value of features which considered as well-predicted features.

recomponent

Logical, whether re-estimate components or not during each iteration.

Value

A list containing

model

O2PLS model

trainres

Final correlation results between predicted and measured metabolites of training samples

components

Components number. If recomponent = TRUE, the components number is the result of last estimation.

re_estimate

Re-estimate information, i.e. whether re-estimate components or not during each iteration

trainnumb

Iteration number

Examples

data(test_metab)
data(test_metag)
a <- MMINP.preprocess(test_metag[, 1:20], normalized = FALSE)
b <- MMINP.preprocess(test_metab[, 1:20], normalized = FALSE)
mminp_model <- MMINP.train(metag = a,
                           metab = b,
                           n = 3:5, nx = 0:3, ny = 0:3,
                           nr_folds = 2, nr_cores = 1)
length(mminp_model$trainres$wellPredicted)