In tidymass/massstat: Statistical analysis for LC-MS-based metabolomics data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  warning = FALSE,
  message = TRUE,
  out.width = "100%"
)

Data preparation

library(massdataset)
library(tidyverse)
library(massstat)
data("liver_aging_pos")
liver_aging_pos

PLS analysis

PLS and PLS-DA analysis are performed by functions from mixOmics package.

X <- liver_aging_pos[1:100,]
X <-
  X %>%
  activate_mass_dataset(what = "sample_info") %>%
  dplyr::filter(group != "QC")

Y <- X@sample_info$group
Y = convert_dummy_variable(y = Y)

pls_object <-
  massstat::pls(X, Y = Y, ncomp = 2)
library(mixOmics)
plotIndiv(pls_object)
plotVar(pls_object)

PLS-DA analysis

PLS and PLS-DA analysis are performed by functions from mixOmics package.

X <- liver_aging_pos[1:100,]
X <-
  X %>%
  activate_mass_dataset(what = "sample_info") %>%
  dplyr::filter(group != "QC")

Y <- X@sample_info$group

plsda_object <-
  massstat::plsda(X, Y = Y, ncomp = 2)
library(mixOmics)
plotIndiv(plsda_object)
plotVar(plsda_object)