LFQ-Analyst
A tool for analysing label-free quantitative proteomics dataset https://bioinformatics.erc.monash.edu/apps/LFQ-Analyst/
Motivation
- Automate downstream statistical analysis of Label free quantitative proteomics data (generated by MaxQuant)
Input
- MaxQuant proteinGroups.txt file
- An experiment design table (tab separated file) containing three columns ("label", "condition", "replicate")
Data pre-filtering criteria
- Remove potential contaminants
- Remove reverse sequences
- Remove proteins identified only by sites
- Remove proteins identified/quantified by a single Razor or unique peptide
- Remove observation with high proportion of missing values (intensity values must be present at least 2 out of three replicates)
Advanced parameters to choose
- Differencial expression cutoff
- Adjusted p-value cutoff (FDR cutoff on quantitation)
- Log2 fold change cutoff
- Option to choose paired test for matched pair data
- Types of imputation
- A number of missing value imputation options including knn, Minpob etc.
- Type of FDR correction
- Benjamin Hochberg (BH) method
- t-statistics correction: Implemented in fdrtools
- Option to include proteins identified/quantified with a single unique peptide.
- Select how many clusters of differentially expressed proteins needed for the heatmap (default is 6)
Outputs
Result table
- LFQ Results Table: Includes names (Gene names), Protein Ids, Log fold changes/ ratios (each pairwise comparisons), Adjusted p-values (applying FDR corrections), p-values, Boolean values for significance, average protein intensity (log transformed) in each sample.
Result Plots
- Interactive volcano plot for each pairwise comparison.
- Heatmap of differencially expressed proteins
- Protein intensity plots for a single or group of selected proteins from table.
QC Plots
- PCA plot (Could move to QC section)
- Sample Correlation (pearson correlation)
- Sample Coefficient of variations (CVs)
- Number of proteins per sample
- Sample coverage (overlap of identified proteins across every sample)
- Missing value heatmap
- Imputation effect on sample distribution
Download options
Download tables (csv format)
- Results: Same as LFQ Results Table
- Unimputed data matrix: Original protein intensities before imputation in each sample.
- Imputed data matrix: Protein intensities after performing selected imputation method
- Full results: Combined table of all above data outputs i.e. with and without imputation information, along with fold change and p-values.
Download Report
- A summary report for each analysis that includes method, summary statistics and plots.
Local installation
The current version of LFQ-Analyst is hosted on R - 3.4.3. The detailed dependency information can be found in the dependencies.txt file.
Once installed all the dependencies following steps to run the server locally.
- Using git and Rstudio
## Clone the repository
git clone https://github.com/MonashBioinformaticsPlatform/LFQ-Analyst.git
## Move to the folder
cd LFQ-Analyst
## Inside R console or R studio
> library("shiny")
> runApp()
- Using Docker
Install & start Docker demon on your PC
## Clone the repository
git clone https://github.com/MonashBioinformaticsPlatform/LFQ-Analyst.git
## Move to the folder
cd LFQ-Analyst
## Build LFQ-Analyst (Any name after -t)
> docker build -f Dockerfile -t LFQ-Analyst .
## Run LFQ-Analyst (From terminal)
> docker run -p 3838:3838 LFQ-Analyst
## Open local interface
https://localhost:3838/LFQ-Analyst
If you are using more recent versions of R i.e R 3.6.0, please clone the dev branch.
# cloning single branch
git clone https://github.com/MonashBioinformaticsPlatform/LFQ-Analyst.git -b dev --single-branch /your_folder/location/