maxabpp

R package for augmented visualization of MaxQuant identification and label-free quantitation data in peptide-centric competitive activity-based protein profiling or other untargeted proteomics platforms.

Visualizing ABPP Data

Getting Started

How to install this package?

1. You need to install the devtools package. You can do this from CRAN. Invoke R and then type

install.packages("devtools")

2. Load the devtools package.

library(devtools)

3. Install this package directly from github.

install_github("devradiumking/maxabpp")

Usage

1. Install dependent R packages.

install.packages("tidyverse")
install.packages("stringdist")
install.packages("stringr")
install.packages("ggplot2")
install.packages("ggrepel")
install.packages("RColorBrewer")
install.packages("grid")

2. Load dependent R packages.

library(tidyverse)
library(stringdist)
library(stringr)
library(ggplot2)
library(ggforce)
library(ggrepel)
library(grid)
library(RColorBrewer)
library(plyr)

3. Call function pairwise_LFQ() on raw MaxQuant output ("modificationSpecificPeptides.txt" and a customized metadata file "metadata.txt" must be put in the folder set as the working directory) to obtain output1. Example metadata.txt: Raw data file name Replicate group Intensity A0288_klc_20140708m_HEK293_Redoxome_H2O2_BR1_C1 H2O2_20 Intensity A0288_klc_20140708m_HEK293_Redoxome_H2O2_BR1_C5 H2O2_2000 Intensity A0288_klc_20140708m_HEK293_Redoxome_H2O2_BR2_C1 H2O2_20 Intensity A0288_klc_20140708m_HEK293_Redoxome_H2O2_BR2_C5 H2O2_2000 Intensity A0288_klc_20140708m_HEK293_Redoxome_H2O2_BR3_C1 H2O2_20 Intensity A0288_klc_20140708m_HEK293_Redoxome_H2O2_BR3_C5 H2O2_2000

output1 <- pairwise_LFQ(
raw = read.delim("modificationSpecificPeptides.txt", header = TRUE, sep = "\t"), 
metadata = read.delim("metadata.txt", header=TRUE, sep = "\t"), 
name_probe_mod = c("Mod"), 
max_each_mod = 1, 
max_total_mods = 1, 
quantitation_level = "peptide", 
background_check = FALSE,
normalize_to = "sum_all")

Note: Multiple modification forms of a single chemical probe can be used as name_probe_mod = c("Mod1", "Mod2"). For instance, original (+ probe mass) and hydrolyzed (+ probe mass + 18 Da). These should be previously set on MaxQuant.

4. Call function append_ec_sites() on output1 to obtain output2, for example:

output2 <- append_ec_sites(output1, quantitation_level = "peptide")

5. Call function plot_volcano(), on output2 to obtain a volcano plot, for example:

plot_volcano(output2, "InhibitorHigh _vs_ InhibitorLow _log2fold_change", "InhibitorHigh _vs_ InhibitorLow _-log10p-value", xlim = c(-6, 2), ylim = c(0, 5), "Gene Names", 1, 0, "InhibitorName/ProbeName")

6. New feature of v1.1, you can plot all volcano plots by calling multi_volcano_plots() functions

multi_volcano_plots(raw = raw, meta = meta, name_probe_mod = c("Mod"),
                    max_each_mod = 1, max_total_mods = 1, quantitation_level = "peptide" , background_check = FALSE,
                    xlim = c(-10, 3), ylim = c(0, 5), label_col_name = "Gene Names", pCutoff = 0.05, FCcutoff = 0)

7. New features of v2.3: visualization of identified proteins groups from MaxQuant proteinGroups.txt with Venn Diagram and Target Diagram of tiered intersection. User-renamed proteinGroups.txt files must be put in the designated folder (default folder name is "proteinGroups"). Create one if needed. Call the functions below to make the plots.

setList <- make_proteinGroups_setList(folderName = "proteinGroups")
plot_Max_Venn(Max_Venn(setList, IndividualAnalysis = FALSE))
plot_target(make_tiers(setList))

Citation

maxabpp was developed at the Yao Lab at Chemistry Department, University of Connecticut

If you use this package please cite as:

Lei Wang and Xudong Yao (2020). maxabpp: R package for augmented visualization of peptide-centric competitive activity-based protein profiling data from MaxQuant protein identification and label-free quantitation output. package version 2.5. https://github.com/devradiumking/maxabpp