PomaUnivariate: Univariate Statistical Methods for Mass Spectrometry Data

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

Description

PomaUnivariate() allows users to perform different univariate statistical analysis on MS data.

Usage

PomaUnivariate(
  data,
  covariates = FALSE,
  method = "ttest",
  paired = FALSE,
  var_equal = FALSE,
  adjust = "fdr"
)

Arguments

data	A MSnSet object. First pData column must be the subject group/type.
covariates	Logical. If it's set to TRUE all metadata variables stored in pData will be used as covariables. Default = FALSE.
method	Univariate statistical method. Options are: "ttest", "anova", "mann" and "kruskal".
paired	Logical that indicates if the data is paired or not.
var_equal	Logical that indicates if the data variance is equal or not.
adjust	Multiple comparisons correction method. Options are: "fdr", "holm", "hochberg", "hommel", "bonferroni", "BH" and "BY".

Value

A data frame with results.

Author(s)

Pol Castellano-Escuder

Examples

data("st000336")
data("st000284")

# ttest
st000336 %>% 
  PomaImpute() %>%
  PomaNorm() %>%
  PomaOutliers() %>%
  PomaUnivariate(method = "ttest")

# ANOVA
st000284 %>% 
  PomaImpute() %>%
  PomaNorm() %>%
  PomaOutliers() %>%
  PomaUnivariate(method = "anova")

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