Total Proteomics QC & Normalisation

Version: 1.0.0

Commit Hash: cff55d3556642469be6ea14a0759da941044815c

Author: CauldronGO Team

Category: preprocessing

QC filtering and PRONE normalisation method comparison for DIA-NN total proteomics data

Install with Cauldron get_app
README

Total Proteomics QC & Normalisation

Installation

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

Repository: https://github.com/noatgnu/total-proteomics-qc-normalisation-plugin

Manual installation:

  1. Open Cauldron
  2. Go to Plugins → Install from Repository
  3. Paste: https://github.com/noatgnu/total-proteomics-qc-normalisation-plugin
  4. Click Install

ID: total-proteomics-qc-normalisation
Version: 1.0.0
Category: preprocessing
Author: CauldronGO Team

Description

QC filtering and PRONE normalisation method comparison for DIA-NN total proteomics data

Workflow Diagram

flowchart TD
    Start([Start]) --> step1
    step1["Loading pg_matrix..."]
    step1 --> step2
    step2["Loading annotation file..."]
    step2 --> step3
    step3["Filtering proteins (contaminants and minimum peptides)"]
    step3 --> step4
    step4{"Loading stats file for QC plots"}
    step4 --> step5
    step5["Running PRONE normalisation..."]
    step5 --> step6
    step6["Computing CV comparison across normalisation methods..."]
    step6 --> step7
    step7["Writing normalised output matrices..."]
    step7 --> step8
    step8["QC and normalisation complete."]
    step8 --> End([End])

Runtime

  • Environments: r

  • Entrypoint: total_proteomics_qc_normalisation.R

Inputs

Name Label Type Required Default Visibility
pg_matrix_file Protein Group Matrix File file Yes - Always visible
stats_file Stats File file No - Always visible
annotation_file Sample Annotation File file Yes - Always visible
min_unique_peptides Minimum Proteotypic Peptides number (min: 0, step: 1) No 2 Always visible
contaminant_column Contaminant Column text No Contaminant Always visible
cc_mapped_column Curated Category Column text No - Always visible
q_value_cutoff Report Q-Value Cutoff number (min: 0, max: 1, step: 0.001) No 0.01 Always visible

Input Details

Protein Group Matrix File (pg_matrix_file)

DIA-NN report.pg_matrix.tsv (protein group intensities, one column per raw file), the same matrix as .parquet, or a DIA-NN 2.x report.parquet

Stats File (stats_file)

DIA-NN report.stats.tsv (per-run QC metrics). Optional; omit to skip run-level QC plots.

Sample Annotation File (annotation_file)

Cauldron sample annotation file. Sample is matched against pg_matrix column names by exact string, falling back to basename.

  • Table Editor: Enabled with 3 columns
  • Columns:
    • Sample: Sample (required)
    • Sample identifier matching a pg_matrix column (full raw-file path or basename)
    • Condition: Condition (required)
    • Experimental group label
    • BioReplicate: BioReplicate
    • Biological replicate identifier (optional)

Minimum Proteotypic Peptides (min_unique_peptides)

Minimum N.Proteotypic.Sequences required to keep a protein group

Contaminant Column (contaminant_column)

Column flagging contaminants with '+'; matching rows are dropped. Skipped automatically if pg_matrix has no such column.

Curated Category Column (cc_mapped_column)

Optional column whose value equal to the primary gene name flags a 'CC.mapped' category (annotation only, never filtered on). Leave empty to skip.

Report Q-Value Cutoff (q_value_cutoff)

Only used when a DIA-NN report.parquet is given as the protein group matrix: Q.Value, Global.Q.Value and Global.PG.Q.Value cutoff (run-specific PG.Q.Value is fixed at 0.05)

Outputs

Name File Type Format Description
sample_annotation sample_annotation.tsv data tsv Resolved sample-to-group mapping used for the run
log2_matrix log2_matrix.tsv data tsv Filtered protein groups as a log2 intensity matrix, before normalisation
normalized_median normalized_Median.tsv data tsv PRONE Median-normalised matrix
normalized_quantile normalized_Quantile.tsv data tsv PRONE Quantile-normalised matrix
normalized_vsn normalized_VSN.tsv data tsv PRONE VSN-normalised matrix
normalized_normicsvsn normalized_NormicsVSN.tsv data tsv PRONE NormicsVSN-normalised matrix (skipped automatically if PRONE cannot fit it on this dataset)
cv_comparison_plot normalisation/CV_comparison_all_methods.png plot png Per-group CV across normalisation methods; the deciding plot for choosing a method for the differential-expression plugin
qc_run_summary_plot QC/QC_run_summary.png plot png Combined per-run QC panel (proteins, precursors, missed cleavages identified). Present only when a stats file was given.

Requirements

  • R Version: >=4.0

R Dependencies (External File)

Dependencies are defined in: r-packages.txt

  • SummarizedExperiment
  • PRONE
  • tidyverse
  • ggplot2
  • RColorBrewer
  • scales
  • svglite
  • patchwork
  • nanoparquet

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:

  pg_matrix_file: examples/pg_matrix.tsv
  stats_file: examples/stats.tsv
  annotation_file: examples/annotation.txt
  min_unique_peptides: 2

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

Usage

Via UI

  1. Navigate to preprocessing → Total Proteomics QC & Normalisation
  2. Fill in the required inputs
  3. Click Run Analysis

Via Plugin System

const jobId = await pluginService.executePlugin('total-proteomics-qc-normalisation', {
  // Add parameters here
});