InteRact: Analysis of AP-MS data

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

Analysis of AP-MS data

Description

Analysis of AP-MS data

Usage

InteRact(
  df,
  id = NULL,
  updateProgress = NULL,
  N_rep = 1,
  method = "default",
  quantile_rep = 0.05,
  scale_background = TRUE,
  pool_background = FALSE,
  log_test = TRUE,
  log_stoichio = TRUE,
  log_mean = TRUE,
  by_conditions = TRUE,
  substract_ctrl = TRUE,
  use_mean_for_bait = FALSE,
  preprocess_df = NULL,
  ...
)

Arguments

df	A dataframe containing protein intensities. By default, protein intensity column names start by "Intensity." (use parameter Column_intensity_pattern to change)
id	The id of the InteRactome. 1 by default. Useful to distinguish InteRactomes with the same bait.
updateProgress	function to show progress bar in shiny app
N_rep	Number of iterations for the replacement of missing values
method	Method to replace missing values. Methods from the "mice" package are supported. Use "none" if you do not want to replace missing values. By default, missing values are sampled from a normal distribution centered on the quantile of ctrl intensities defined by parameter quantile_rep with the standard deviation set to the mean SD of ctrl intensities across all proteins.
quantile_rep	Numeric value between 0 and 1. Quantile of the distribution of mean intensities in the control background used to replace missing values.
scale_background	logical. Scale imputed values according to protein intensities in the control background?
pool_background	option to use all control background conditions as one control group for all conditions
log_test	logical, perform t-test on log transform intensities
log_stoichio	logical, use the geometric mean instead of the arithmetic mean to compute stoichiometries
log_mean	logical, use the geometric mean instead of the arithmetic mean to compute the mean InteRactome
by_conditions	option to perform the comparison between bait and control group for each condition
substract_ctrl	logical, substract ctrl intensities in the calculation of stoichiometries
use_mean_for_bait	logical, average bait intensities across all conditions to compute interaction stoichiometries
preprocess_df	list obtained by the function preprocess_data()
...	Additional parameters passed to function preprocess_data() and identify_conditions

Value

a list containing the preprocessed data and on object of class InteRactome, i.e a list including the following elements :

conditions : a vector of experimental conditions.

names : a vector of names (by default gene names are used).

p_val : a list of vectors containing the p values associated to each experimental condition.

fold_change : a list of vectors containing the fold change associated to each experimental condition.

... : other variables.

Author(s)

Guillaume Voisinne

Examples

#load data :
data("proteinGroups_Cbl")
#Run InteRact with default parameters
res <- InteRact(proteinGroups_Cbl, bait_gene_name = "Cbl")

#You now have an `InteRactome`. See its elements.
class(res)
names(res)
#Generate volcano plots
plot_volcanos(res)

#Identify specific interactors
res <- identify_interactors(res, p_val_thresh = 0.05, fold_change_thresh = 2)

#Visualize interaction kinetics
plot_per_condition(res)

# Append annotations
# res <- append_annotations(res)
#Create a summary data frame
sum_tbl <- summary_table(res)

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