In VoisinneG/InteRact: Analysis of affinity purification/tandem MS data

knitr::opts_chunk$set(echo = TRUE)

Input data format

The input file can be of many types as long as it can be converted to a data.frame containing a number of columns with protein intensities and one column with protein identifiers. A protein identifier correspond to a unique entry in the UniProt database. Multiple protein identifiers can be associated with a single protein group.

Load data

Load an example dataset containing protein group intensities:

library(InteRact)
data("proteinGroups_Cbl")
names(proteinGroups_Cbl)[1:20]

Map conditions

Protein intensity column names start with "Intensity" :

idx_intensity_columns <- grep("^Intensity.", names(proteinGroups_Cbl))
print(names(proteinGroups_Cbl)[idx_intensity_columns][1:10])

Use the function identify_conditions() to map conditions from intensity column names:

condition <- identify_conditions(proteinGroups_Cbl,
                                  Column_intensity_pattern = "^Intensity.",
                                  split = "_", 
                                  bckg_pos = 1,
                                  time_pos = 2,
                                  bio_pos = 3,
                                  tech_pos = 4)
print(condition)

Preprocess data

preprocessed_data <- preprocess_data(proteinGroups_Cbl,
                            Column_gene_name = "Gene.names",
                            Column_score = "Score",
                            Column_ID = "Protein.IDs",
                            Column_Npep = NULL,
                            Column_intensity_pattern = "^Intensity.",
                            bait_gene_name = "Cbl",
                            condition = condition,
                            bckg_bait = "Cbl",
                            bckg_ctrl = "WT"
                            )

Run InteRact

Replace missing values with methode method, compare protein intensities between bait and control backgrounds (bckg_bait and bckg_ctrl respectively) across experimental conditions:

res <- InteRact(preprocess_df = preprocessed_data, 
                method = "none",
                pool_background = TRUE)

Identify specific interactions

Identify prey proteins specifically enriched in the bait background :

res <- identify_interactors(res, 
                            p_val_thresh = 0.001, 
                            fold_change_thresh = 3, 
                            n_success_min = 2, 
                            consecutive_success = TRUE)
names(res)

print(res$interactor)

Summarize results

Create a summary data.frame :

#Create a summary data frame
sum_tbl <- summary_table(res)
head(sum_tbl[, 1:10])

Volcano plots

Generate volcano plots :

plot_volcanos(res, 
              conditions = "030", 
              p_val_thresh = 0.005, fold_change_thresh = 3)

Recruitment kinetics

Plot kinetics of recruitment:

plot_per_condition(res)

Focus on a protein

Compare protein intensities across condition for a given protein

plot_comparison(res, names = "Crkl")

VoisinneG/InteRact documentation built on May 17, 2022, 11:40 p.m.