imprints_read_peptides: imprints_read_peptides

In mgerault/mineCETSAapp: A shiny app for IMPRINTS.CETSA package

imprints_read_peptides

Description

Function to read your peptides files and concatenate them in one data frame.

Usage

imprints_read_peptides(
  peptides_files,
  treatment,
  temperatures,
  prefixcontaminant = "",
  averagecount = TRUE,
  countthreshold = 2,
  proteins = NULL,
  modification_torm = NULL,
  dataset_name = "imprints"
)

Arguments

peptides_files	The path to the PeptidesGroups file from PD. Can be one or more files.
treatment	A character vector that contains the treatment applied for each channel like 'B1_Vehicle' for example.
temperatures	A character vector that contains the temperatures used for each peptides file like '37C' for example.
prefixcontaminant	Character corresponding to the prefix used to identify contaminants.
averagecount	Whether to take the median of the abundance count numbers across the measured temperature range and then use this value for filtering. Default set to TRUE. Otherwise, filter the peptides according to the associated count numbers at each temperature.
countthreshold	The minimal threshold number of associated abundance count of peptides, default is 2.
proteins	Either a data frame or a file that has the 2 columns 'id' and 'description' corresponding to the proteins you want to keep (usually proteins after imprints_rearrange or imprints_caldiff). If not specified, no filtering is applied and you must have the column 'Master Protein Descriptions' in your PeptideGroup file(s).
modification_torm	A character vector that contains the peptides modifications you want to remove. Default is NULL
dataset_name	The name of your dataset

Value

The peptides data frame

mgerault/mineCETSAapp documentation built on April 17, 2025, 7:24 p.m.