Limma Differential Expression

Version: 1.0.0

Commit Hash: e1dfa8958ee50e3f87dfdd452fbf7741ebb91251

Author: CauldronGO Team

Category: analysis

Linear models for differential expression analysis using limma

Install with Cauldron get_app
README

Limma Differential Expression

Installation

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

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

Manual installation:

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

ID: limma
Version: 1.0.0
Category: analysis
Author: CauldronGO Team

Description

Linear models for differential expression analysis using limma

Runtime

  • Environments: r

  • Entrypoint: limma.R

Inputs

Name Label Type Required Default Visibility
input_file Input Data File file Yes - Always visible
annotation_file Annotation File file Yes - Always visible
index_col Index Column text No - Always visible
log2 Apply Log2 Transformation boolean No false Always visible
comparisons Comparisons file No - Always visible
impute_order Imputation Order select (before, after) No before Always visible
impute Imputation Method select (none, knn, MinDet, MinProb, min, zero, mixed, nbavg, with, QRILC, MLE, bpca) No none Always visible
normalize Normalization Method select (none, quantiles, quantiles.robust, vsn, center.median, center.mean) No none Always visible
aggregate_column Aggregate By Column text No - Always visible
aggregate_method Aggregation Method text No MsCoreUtils::robustSummary Always visible

Input Details

Input Data File (input_file)

Proteomics or expression data file

Annotation File (annotation_file)

Sample annotation file with conditions

Index Column (index_col)

Column name to use as feature identifier

Apply Log2 Transformation (log2)

Apply log2 transformation before analysis

Comparisons (comparisons)

Comparison groups for differential analysis

Imputation Order (impute_order)

When to perform imputation relative to normalization (before or after normalization)

  • Options: before, after

Imputation Method (impute)

Imputation method for missing values (select 'none' to skip imputation)

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

Normalization Method (normalize)

Normalization method to apply (select 'none' to skip normalization)

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

Aggregate By Column (aggregate_column)

Column name to aggregate features (e.g., aggregate peptides to proteins). Leave empty to skip aggregation.

Aggregation Method (aggregate_method)

Method to use for feature aggregation (e.g., MsCoreUtils::robustSummary, median, mean)

Outputs

Name File Type Format Description
differential_results differential_analysis.txt data tsv Differential expression analysis results
contrast_matrix_info contrast_matrix_info.txt data tsv Contrast matrix information showing comparison directions

Requirements

  • R Version: >=4.0

R Dependencies (External File)

Dependencies are defined in: r-packages.txt

  • QFeatures
  • limma
  • MsCoreUtils
  • impute

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:

  comparisons: differential_analysis/comparison.bca.txt
  index_col: Protein.Ids
  log2: true
  input_file: diann/imputed.data.txt
  annotation_file: differential_analysis/annotation.txt

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

Usage

Via UI

  1. Navigate to analysis → Limma Differential Expression
  2. Fill in the required inputs
  3. Click Run Analysis

Via Plugin System

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