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.

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")

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))

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