PomaMultivariate: Multivariate Statistical Methods for Mass Spectrometry Data

In POMA: User-friendly Workflow for Pre-processing and Statistical Analysis of Mass Spectrometry Data

Description

PomaMultivariate() allows users to perform different multivariate statistical analysis on MS data.

Usage

PomaMultivariate(
  data,
  method = "pca",
  components = 5,
  center = FALSE,
  scale = FALSE,
  labels = FALSE,
  load_length = 1,
  ellipse = TRUE,
  validation = "Mfold",
  folds = 5,
  nrepeat = 10,
  vip = 1.5,
  num_features = 10
)

Arguments

data	A MSnSet object. First pData column must be the subject group/type.
method	A multivariate method. Options are: "pca", "plsda" and "splsda".
components	Numeric. Number of components to include in the model. Default is 5.
center	Logical that indicates whether the variables should be shifted to be zero centered. Default is FALSE.
scale	Logical that indicates whether the variables should be scaled to have unit variance before the analysis takes place. Default is FALSE.
labels	Logical indicating if sample names should be plotted or not.
load_length	Numeric between 1 and 2. Define the length of biplot loadings. Default is 1.
ellipse	Logical that indicates whether a 95%CI ellipse should be plotted in scores plot. Default is TRUE.
validation	(Only for "plsda" and "splsda" methods) Validation method. Options are "Mfold" and "loo".
folds	(Only for "plsda" and "splsda" methods) Numeric. Number of folds for Mfold validation method (default is 5). If the validation method is loo, this value will become to 1.
nrepeat	(Only for "plsda" and "splsda" methods) Numeric. Number of iterations for the validation method selected.
vip	(Only for "plsda" method) Numeric indicating VIP cutoff to select features that will be displayed in vip plot.
num_features	(Only for "splsda" method) Numeric. Number of variables selected to discriminate groups.

Value

A list with all results for multivariate statistical analysis including plots and data frames.

Author(s)

Pol Castellano-Escuder

Examples

data("st000336")

# PCA
st000336 %>% 
  PomaImpute() %>%
  PomaNorm() %>%
  PomaOutliers() %>%
  PomaMultivariate(method = "pca")

# PLSDA
st000336 %>% 
  PomaImpute() %>%
  PomaNorm() %>%
  PomaOutliers() %>%
  PomaMultivariate(method = "plsda", vip = 1)

# sPLSDA
st000336 %>% 
  PomaImpute() %>%
  PomaNorm() %>%
  PomaOutliers() %>%
  PomaMultivariate(method = "splsda")

POMA documentation built on Nov. 8, 2020, 6:26 p.m.