PomaLimma: Implementation of limma R Package on Mass Spectrometry Data

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

Description

PomaLimma() uses the classical limma package for MS data.

Usage

PomaLimma(
  data,
  contrast = NULL,
  covariates = FALSE,
  adjust = "fdr",
  cutoff = NULL
)

Arguments

data	A MSnSet object. First pData column must be the subject group/type.
contrast	A character with the limma comparison. For example, "Group1-Group2" or "control-intervention".
covariates	Logical. If it's set to TRUE all metadata variables stored in pData will be used as covariables. Default = FALSE.
adjust	Multiple comparisons correction method. Options are: "fdr", "holm", "hochberg", "hommel", "bonferroni", "BH" and "BY".
cutoff	Default is NULL. If this value is replaced for a numeric value, the resultant table will contains only those features with an adjusted p-value below selected value.

Value

A data frame with limma results.

Author(s)

Pol Castellano-Escuder

References

Matthew E. Ritchie, Belinda Phipson, Di Wu, Yifang Hu, Charity W. Law, Wei Shi, Gordon K. Smyth, limma powers differential expression analyses for RNA-sequencing and microarray studies, Nucleic Acids Research, Volume 43, Issue 7, 20 April 2015, Page e47, https://doi.org/10.1093/nar/gkv007

Examples

data("st000284")

st000284 %>%
  PomaNorm() %>%
  PomaLimma(contrast = "Healthy-CRC", adjust = "fdr")

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