PomaLimma: Differential Expression Analysis Using 'limma'

In pcastellanoescuder/POMA_package: Tools for Omics Data Analysis

Differential Expression Analysis Using limma

Description

PomaLimma uses the classical limma package to compute differential expression analysis.

Usage

PomaLimma(data, contrast = NULL, covs = NULL, adjust = "fdr", weights = FALSE)

Arguments

data	A SummarizedExperiment object.
contrast	Character. Indicates the comparison. For example, "Group1-Group2" or "control-intervention".
covs	Character vector. Indicates the names of colData columns to be included as covariates. Default is NULL (no covariates). If not NULL, a limma model will be fitted using the specified covariates. Note: The order of the covariates is important and should be listed in increasing order of importance in the experimental design.
adjust	Character. Indicates the multiple comparisons correction method. Options are: "fdr", "holm", "hochberg", "hommel", "bonferroni", "BH" and "BY".
weights	Logical. Indicates whether the limma model should estimate the relative quality weights for each group. See ?limma::arrayWeights().

Value

A tibble with the 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")

pcastellanoescuder/POMA_package documentation built on March 15, 2024, 10:09 p.m.