MaxLFQ Normalization

Installation

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

Repository: https://github.com/noatgnu/maxlfq-plugin

Manual installation:

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

ID: maxlfq
Version: 1.0.0
Category: preprocessing
Author: CauldronGO Team

Description

Label-free quantification using MaxLFQ algorithm for protein-level quantification from peptide intensities. Based on: Cox et al. (2014) Mol Cell Proteomics 13:2513-2526. Implementation: iq R package (https://github.com/tvpham/iq)

Runtime

• Environments: r

• Entrypoint: maxlfq.R

Inputs

Input Details

Input File (input_file)

Peptide intensity data file

Protein Column (protein_col)

Column name containing protein identifiers

Peptide Column (peptide_col)

Column name containing peptide identifiers

Sample Columns (sample_cols)

Columns containing sample intensities

• Column Source: input_file

Sample Annotation File (annotation_file)

Optional annotation file with Sample and Condition columns for grouping in plots

Data Completeness (data_completeness)

Minimum fraction of samples with valid values (0-1). Higher values = less memory usage. Use 0.7-0.9 for large datasets.

Use Log2 Transform (use_log2)

Apply log2 transformation to peptide intensities before MaxLFQ. When enabled, MaxLFQ works in log2 space and outputs log2 values. When disabled, works in raw intensity space.

Maximum Cores (max_cores)

Maximum number of CPU cores to use (-1 = use all available cores minus 1, 0 = use all cores)

Outputs

Sample Annotation

This plugin supports sample annotation:

• Samples From: sample_cols
• Annotation File: annotation_file

Visualizations

This plugin generates 2 plot(s):

Before Normalization (boxplot_before)

Protein intensities before normalization grouped by condition

• Type: image-grid
• Data Source: maxlfq_results
• Default: Yes
• Image Pattern: boxplot_before_normalization.svg
• Pattern Type: exact

After Normalization (boxplot_after)

Protein intensities after normalization grouped by condition

• Type: image-grid
• Data Source: maxlfq_results
• Image Pattern: boxplot_after_normalization.svg
• Pattern Type: exact

Requirements

• R Version: >=4.0

R Dependencies (External File)

Dependencies are defined in: r-packages.txt

• iq
• tidyverse
• data.table
• reshape2
• ggplot2

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:

  use_log2: true
  input_file: diann/Reports.pr_matrix.tsv
  protein_col: Protein.Group
  peptide_col: Precursor.Id
  sample_cols_source: diann/Reports.pr_matrix.tsv
  sample_cols: [C:\Raja\DIA-NN searches\June 2022\LT-CBQCA-Test_DIA\RN-DS_220106_BCA_LT-IP_01.raw C:\Raja\DIA-NN searches\June 2022\LT-CBQCA-Test_DIA\RN-DS_220106_BCA_LT-IP_02.raw C:\Raja\DIA-NN searches\June 2022\LT-CBQCA-Test_DIA\RN-DS_220106_BCA_LT-IP_03.raw C:\Raja\DIA-NN searches\June 2022\LT-CBQCA-Test_DIA\RN-DS_220106_BCA_LT-MockIP_01.raw C:\Raja\DIA-NN searches\June 2022\LT-CBQCA-Test_DIA\RN-DS_220106_BCA_LT-MockIP_02.raw C:\Raja\DIA-NN searches\June 2022\LT-CBQCA-Test_DIA\RN-DS_220106_BCA_LT-MockIP_03.raw]
  annotation_file: diann/annotation.txt
  data_completeness: 0.7

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

Usage

Via UI

1. Navigate to preprocessing → MaxLFQ Normalization
2. Fill in the required inputs
3. Click Run Analysis

Via Plugin System

const jobId = await pluginService.executePlugin('maxlfq', {
  // Add parameters here
});