Batch Effect Correction

Version: 1.0.0

Commit Hash: 7afeedc179f8dd2bfa519c63c3291592621e13af

Author: CauldronGO Team

Category: preprocessing

Correct for batch effects in proteomics data

Install with Cauldron get_app
README

Batch Effect Correction

ID: batch-correction
Version: 1.0.0
Category: preprocessing
Author: CauldronGO Team

Description

Correct for batch effects in proteomics data

Runtime

  • Type: r
  • Script: batch_correction.R

Inputs

Name Label Type Required Default Visibility
input_file Input File file Yes - Always visible
annotation_file Annotation File file Yes - Always visible
columns_name Sample Columns column-selector (multiple) Yes - Always visible
method Correction Method select (combat, limma, ruvseq) Yes combat Always visible
preserve_column Preserve Column text No - Always visible
use_log2 Use Log2 boolean No false Always visible

Input Details

Input File (input_file)

Data file with batch effects

Annotation File (annotation_file)

Sample annotation file with 'Sample' and 'Batch' columns

Sample Columns (columns_name)

Select columns containing sample data

  • Column Source: input_file

Correction Method (method)

Method to use for batch correction

  • Options: combat, limma, ruvseq

Preserve Column (preserve_column)

Column name from annotation file to preserve during correction (e.g., Condition, BioReplicate)

Use Log2 (use_log2)

Apply log2 transformation before correction

Outputs

Name File Type Format Description
corrected_data batch_corrected.data.txt data tsv Batch-corrected data matrix
batch_info batch_info.txt data tsv Batch correction summary information

Sample Annotation

This plugin supports sample annotation:

  • Samples From: columns_name
  • Annotation File: annotation_file

Requirements

  • R: >=4.0
  • Packages:
  • sva
  • limma
  • RUVSeq

Example Data

This plugin includes example data for testing:

  columns_name: [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-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_Pepide-CBQCA_LT-IP_01.raw C:\Raja\DIA-NN searches\June 2022\LT-CBQCA-Test_DIA\RN-DS_220106_Pepide-CBQCA_LT-IP_02.raw C:\Raja\DIA-NN searches\June 2022\LT-CBQCA-Test_DIA\RN-DS_220106_Pepide-CBQCA_LT-MockIP_01.raw C:\Raja\DIA-NN searches\June 2022\LT-CBQCA-Test_DIA\RN-DS_220106_Pepide-CBQCA_LT-MockIP_02.raw C:\Raja\DIA-NN searches\June 2022\LT-CBQCA-Test_DIA\RN-DS_220106_BCA_LT-WCL_01.raw C:\Raja\DIA-NN searches\June 2022\LT-CBQCA-Test_DIA\RN-DS_220106_BCA_LT-WCL_02.raw C:\Raja\DIA-NN searches\June 2022\LT-CBQCA-Test_DIA\RN-DS_220106_Pepide-CBQCA_LT-WCL_01.raw C:\Raja\DIA-NN searches\June 2022\LT-CBQCA-Test_DIA\RN-DS_220106_Pepide-CBQCA_LT-WCL_02.raw]
  method: combat
  use_log2: false
  input_file: diann/imputed.data.txt
  annotation_file: differential_analysis/batch_info.txt
  columns_name_source: diann/imputed.data.txt

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

Usage

Via UI

  1. Navigate to preprocessing → Batch Effect Correction
  2. Fill in the required inputs
  3. Click Run Analysis

Via Plugin System

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