PECA_analysis: Perform differential expression analysis using peptide-level...

In mathiaskalxdorf/IceR: Requantification of label-free DDA proteomics data pre-analyzed by MaxQuant.

Perform differential expression analysis using peptide-level expression-change averaging

Description

Perform differential expression analysis using peptide-level expression-change averaging

Usage

PECA_analysis(
  peptide_data,
  peptide_data_quant_significance = NULL,
  ids,
  anno = NULL,
  group1_name,
  group2_name,
  TopN_ratio = 5,
  pvalue_cutoff = NA,
  test = c("t", "modt", "rots")
)

Arguments

peptide_data	Table of peptide quantifications with samples in columns and features in rows.
peptide_data_quant_significance	Optional: Ion accumulation significances with samples in columns and features in rows., By default not required and set to NULL.
ids	character vector of same length as rows in peptide_data indicating to which protein ID the corresponding peptide belongs to.
anno	Annotation of grouping per sample
group1_name	Name of group1 in annotation
group2_name	Name of group1 in annotation
TopN_ratio	Number of top abundant peptides on which at maximum protein ratios should be estimated. By default set to 5.
pvalue_cutoff	Optional: Ion accumulation significance cutoff.
test	A character string indicating whether the ordinary t-test ("t"), modified t-test ("modt"), or reproducibility-optimized test statistic ("rots") is performed.

Details

Perform differential expression analysis using the function PECA_df() from the R-package PECA.

Value

Returns a matrix which rows correspond to the genes under analysis and columns indicate the corresponding abundance ratio, t-statistic, p-value and FDR adjusted p-value

mathiaskalxdorf/IceR documentation built on Aug. 1, 2022, 8:03 a.m.