PhosR MaxQuant TMT Analysis

Installation

⬇️ Click here to install in Cauldron (requires Cauldron to be running)

Repository: https://github.com/noatgnu/phosr-maxquant-tmt-plugin

Manual installation:

1. Open Cauldron
2. Go to Plugins → Install from Repository
3. Paste: https://github.com/noatgnu/phosr-maxquant-tmt-plugin
4. Click Install

ID: phosr-maxquant-tmt
Version: 1.0.0
Category: statistics
Author: CauldronGO Team

Description

Comprehensive phosphoproteomic data analysis including normalization, differential analysis, kinase activity prediction, pathway enrichment, PTM-SET analysis, and signaling network visualization using PhosR. Based on: Kim et al. (2021) Cell Reports 34(8):108771. Repository: https://bioconductor.org/packages/PhosR

Workflow Diagram

flowchart TD
    Start([Start]) --> step1
    step1[Reading PTM data...]
    step1 --> step2
    step2[Reading annotation...]
    step2 --> step3
    step3[Processing PTM sites...]
    step3 --> step4
    step4[Processing global protein data...]
    step4 --> step5
    step5[Creating SummarizedExperiment for PTM...]
    step5 --> step6
    step6[Filtering, normalization, and imputation for PTM...]
    step6 --> step7
    step7[Statistical testing for]
    step7 --> step8
    step8[Generating QC and volcano plots...]
    step8 --> step9
    step9[Reading data...]
    step9 --> step10
    step10[Creating QFeatures object...]
    step10 --> step11
    step11[Filtering and aggregating PSMs to Peptidoforms...]
    step11 --> step12
    step12[Processing PTM peptidoform data...]
    step12 --> step13
    step13[Fitting statistical models...]
    step13 --> step14
    step14[Hypothesis testing...]
    step14 --> step15
    step15[Generating quality control and volcano plots...]
    step15 --> End([End])

Runtime

• Environments: r

• Entrypoint: phosr_maxquant_tmt_modular.R

Inputs

Input Details

PTM Evidence File (input_file)

MaxQuant evidence.txt file with PTM data (e.g., from phospho search)

FASTA File (fasta_file)

Protein database FASTA file for site mapping

PTM Annotation File (annotation_file)

Sample annotation with columns: Sample, Condition, BioReplicate

Protein Annotation File (annotation_protein_file)

Sample annotation for protein data (same format as PTM annotation)

PTM Feature ID Column (feature_id_col)

Column name for feature identifiers in PTM file (stripped sequence)

PTM Site Column (site_col)

Column name for PTM site information (modified sequence)

PTM Protein Column (protein_col)

Column name for protein identifiers in PTM file

PTM Probability Column (probability_col)

Column name for site localization probabilities (e.g., Phospho (STY) Probabilities)

Minimum Localization Probability (min_probability)

Minimum probability score for valid PTM sites (0-1)

Protein Evidence File (protein_file)

MaxQuant evidence.txt file for protein-level data (optional)

Protein Feature ID Column (protein_feature_id_col)

Column name for peptide sequence in protein evidence file

Protein ID Column (protein_id_col)

Column name for protein IDs in protein evidence file

Comparison Matrix File (comparison_file)

Pairwise comparisons (columns: comparison_label, condition_A, condition_B)

Apply Log2 Transformation (log2_transform)

Apply log2 transformation to intensities

Exclude Conditions (comma-separated) (exclude_conditions)

Comma-separated list of conditions to exclude from analysis (e.g., 'Norm,Empty')

Remove 'Norm' Channel (remove_norm_channel)

Automatically remove samples with condition 'Norm'

Column Completeness Threshold (col_filter)

Maximum proportion of missing values allowed per sample. Default 0.7 = keep samples with ≥30% valid measurements.

Row Completeness Threshold (row_filter)

Maximum proportion of missing values allowed per feature. Default 0.7 = keep features with ≥30% valid measurements.

Imputation Order (impute_order)

When to impute missing values relative to normalization. 'after': normalize then impute. 'before': impute then normalize.

• Options: before, after

Imputation Method (impute)

Imputation method for missing values

• Options: none, knn, MinDet, MinProb, min, zero, mixed, nbavg, with, QRILC, MLE, bpca

Normalization Method (normalize_method)

Normalization method to apply

• Options: none, center.median, center.mean, quantiles, quantiles.robust, vsn

Aggregation Order (aggregation_order)

When to aggregate peptides to proteins. 'after': normalize/impute peptides then aggregate. 'before': aggregate peptides then normalize proteins.

• Options: before, after

Protein Summarization Method (summarization_method)

Method for combining peptide intensities to protein level. 'mean': arithmetic mean. 'median': median. 'sum': sum.

• Options: mean, median, sum

P-value Adjustment Method (adjust_method)

Method for multiple testing correction

• Options: BH, bonferroni, holm, hochberg, BY, none

Significance Level (alpha)

Adjusted p-value threshold for significance

Log2 Fold Change Threshold (lfc_threshold)

Minimum absolute log2 fold-change threshold for significance

Organism (organism)

Organism for kinase-substrate database

• Options: human, mouse

Perform Kinase Activity Analysis (perform_kinase_analysis)

Predict kinase activities and kinase-substrate relationships

Top Substrates for Kinase Prediction (kinase_top_substrates)

Number of top substrates to select per kinase

Kinase Score Threshold (kinase_score_threshold)

Minimum score threshold for kinase-substrate predictions

Number of Kinase Motifs (kinase_num_motifs)

Number of motifs to consider in kinase-substrate scoring (higher values increase sensitivity but computation time)

Top Pathways to Plot (top_pathways_plot)

Number of top enriched pathways to show in plots

Top PTM-SETs to Plot (top_ptmset_plot)

Number of top enriched kinase substrate sets to show in plots

Top Differential Sites in Heatmap (top_diff_sites_heatmap)

Number of top differential phosphosites to show in heatmap

Top Phosphosites by Variance in Heatmap (top_phosphosite_heatmap)

Number of phosphosites with highest kinase score variance to show in heatmap

Top Kinase-Substrate Interactions to Plot (network_plot_top_interactions)

Number of top kinase-substrate interactions to show in network plots

Outputs

Requirements

• R Version: >=4.2

R Dependencies (External File)

Dependencies are defined in: r-packages.txt

• QFeatures
• msqrob2
• MsCoreUtils
• PhosR
• impute
• limma
• ggplot2
• pheatmap
• RColorBrewer
• reshape2
• S4Vectors
• Biostrings
• SummarizedExperiment
• reactome.db
• org.Hs.eg.db
• org.Mm.eg.db

Note: When you create a custom environment for this plugin, these dependencies will be automatically installed.

Example Data

This plugin includes example data for testing:

  summarization_method: mean
  adjust_method: BH
  organism: human
  input_file: maxquant-tmt/evidence_ptm.txt
  annotation_protein_file: maxquant-tmt/annotation_protein_msqrob2.txt
  probability_col: Phospho (STY) Probabilities
  log2_transform: true
  normalize_method: center.median
  aggregation_order: after
  perform_kinase_analysis: true
  fasta_file: maxquant-tmt/uniprot_human.fasta
  feature_id_col: Sequence
  protein_file: maxquant-tmt/evidence_protein.txt
  remove_norm_channel: true
  row_filter: 0.7
  impute_order: after
  site_col: Modified sequence
  min_probability: 0.75
  exclude_conditions: Norm
  col_filter: 0.7
  impute: knn
  kinase_top_substrates: 30
  annotation_file: maxquant-tmt/annotation_ptm_msqrob2.txt
  protein_col: Proteins

Load example data by clicking the Load Example button in the UI.

Usage

Via UI

1. Navigate to statistics → PhosR MaxQuant TMT Analysis
2. Fill in the required inputs
3. Click Run Analysis

Via Plugin System

const jobId = await pluginService.executePlugin('phosr-maxquant-tmt', {
  // Add parameters here
});