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,
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.

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.

## Value

A fit_df object, which is similar to a limma

fit object.

## Details

• 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.

## 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.

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

## Author

Chathurani Ranathunge

## Examples


## Perform differential expression analysis using default settings
fit_df1 <- find_dep(ecoli_norm_df)
#> Warning: 3 very small variances detected, have been offset away from zero
#> 1186 siginificantly differentially expressed proteins found.

## Change p-value and adjusted p-value cutoff
fit_df2 <- find_dep(ecoli_norm_df, cutoff = 0.1)
#> Warning: 3 very small variances detected, have been offset away from zero
#> 1227 siginificantly differentially expressed proteins found.