The LFQProcessing R package enables users to load and process the output of the MaxQuant Computational Platform for processing LC-MS/MS proteomics data. This package contains utilities for performing differential expression DE statistics and Quality Control reports.
A sister R package MassExpression, provides the same utilities for a generic format, and will be the focus of future development.
Both LFQProcessing and MassExpression were developed by MassDynamics to enable greater accessibility, reproducibility and insight into proteomics datasets and insights. All feedback is welcome.
For more details please see our biorXiv paper: Mass Dynamics 1.0: A streamlined, web-based environment for analyzing, sharing and integrating Label-Free Data.
The latest release of LFQProcessing is installable using
install_github("MassDynamics/lfq_processing")
For further reproducibility. The package repository contains an renv.lock file that
can be used to restore a working R 4.1.0 environment for running LFQProccesing by running
renv::restore()
The first step is to assign variables that point to the location of the maxquant output files and to the cloned git repository.
mq_folder = "/home/ubuntu/data/mq-test/"
app_folder = "/home/peppe/maxquant-quant-analysis-public/"The mq_folder has to contain these files:
- evidence.txt
- modificationSpecificPeptides.txt
- msms.txt
- parameters.txt
- peptides.txt
- proteinGroups.txt
- summary.txt
Experimental dessign needs to be written in a tab separated file formatted like the example below:
| file_name | replicate | experiment | mqExperiment |
|---|---|---|---|
| JD_06232014_sample1_A | 1 | sample1 | 1_A |
| JD_06232014_sample1_B | 2 | sample1 | 1_B |
| JD_06232014_sample1_C | 3 | sample1 | 1_C |
| JD_06232014_sample2_A | 1 | sample2 | 2_A |
| JD_06232014_sample2_B | 2 | sample2 | 2_B |
| JD_06232014_sample2_C | 3 | sample2 | 2_C |
| JD_06232014_sample3_A | 1 | sample3 | 3_A |
| JD_06232014_sample3_B | 2 | sample3 | 3_B |
| JD_06232014_sample3_C | 3 | sample3 | 3_C |
| JD_06232014_sample4_A | 1 | sample4 | 4_A |
| JD_06232014_sample4_B | 2 | sample4 | 4_B |
| JD_06232014_sample4_C | 3 | sample4 | 4_C |
Where the column file_name contains the names of the LC-MS/MS runs in the data, the column experiment contains the experimental conditions, the column replicate is a unique number for each of the biological or technical replicate in one experimental contition, the column mqExperiment contains the values in the "Experiment" column in the Maxquant "Raw data" tab when setting up the Maxquant analysis (figure below).
Maxquant experimental set up. Each "File" has to be associated to a unique value in the "Experiment" column
Save this file with the name "experimentDesign_original.txt" in your Maxquant .txt folder.
To run the quantitative analysis end-to end, all you need to do is write paths to your Maxquant .txt and to an output folder where you would like to store the output.
protein_quant_runner(maxquant_folder, output_folder)Example data is provided with the package. To run this data you can use the following code:
data("example_lfq_her2_targetted_therapy")
# Run Code
output = lfq_transformer(ma_tables = example_lfq_her2_targetted_therapy_tables,
output_folder = "./sample_data_her/",
imputeStDev=0.3,
imputePosition=1.8)
# output[[1]] is the protein level DE statistics.
# Make a Volcano Plot
plot(output[[1]]$`logFC AZD8931_resistant_SKBR3_AZDRc - Parental_SKBR3`,
-log10(output[[1]]$`P.Value AZD8931_resistant_SKBR3_AZDRc - Parental_SKBR3`))