find_dep: Identify differentially expressed proteins between groups

In promor: Proteomics Data Analysis and Modeling Tools

Identify differentially expressed proteins between groups

Description

This function performs differential expression analysis on protein intensity data with limma.

Usage

find_dep(
  df,
  save_output = FALSE,
  save_tophits = FALSE,
  file_path = NULL,
  adj_method = "BH",
  cutoff = 0.05,
  lfc = 1,
  n_top = 20
)

Arguments

df	A norm_df object or an imp_df object.
save_output	Logical. If TRUE saves results from the differential expression analysis in a text file labeled "limma_output.txt" in the directory specified by file_path.
save_tophits	Logical. If TRUE saves n_top number of top hits from the differential expression analysis in a text file labeled "TopHits.txt" in the directory specified by file_path.
file_path	A string containing the directory path to save the file.
adj_method	Method used for adjusting the p-values for multiple testing. Default is "BH" for "Benjamini-Hochberg" method.
cutoff	Cutoff value for p-values and adjusted p-values. Default is 0.05.
lfc	Minimum absolute log2-fold change to use as threshold for differential expression.
n_top	The number of top differentially expressed proteins to save in the "TopHits.txt" file. Default is 20.

Details

• It is important that the data is first log-transformed, ideally, imputed, and normalized before performing differential expression analysis.

• save_output saves the complete results table from the differential expression analysis.

• save_tophits first subsets the results to those with absolute log fold change of more than 1, performs multiple correction with the method specified in adj_method and outputs the top n_top results based on lowest p-value and adjusted p-value.

• If the number of hits with absolute log fold change of more than 1 is less than n_top, find_dep prints only those with log-fold change > 1 to "TopHits.txt".

• If the file_path is not specified, text files will be saved in a temporary directory.

Value

A fit_df object, which is similar to a limma fit object.

Author(s)

Chathurani Ranathunge

References

Ritchie, Matthew E., et al. "limma powers differential expression analyses for RNA-sequencing and microarray studies." Nucleic acids research 43.7 (2015): e47-e47.

See Also

• normalize_data

• lmFit, eBayes, topTable, and write.fit functions from the limma package.

Examples

## Perform differential expression analysis using default settings
fit_df1 <- find_dep(ecoli_norm_df)

## Change p-value and adjusted p-value cutoff
fit_df2 <- find_dep(ecoli_norm_df, cutoff = 0.1)

promor documentation built on July 26, 2023, 5:39 p.m.