ANCOVA Analysis

Version: 1.0.0

Commit Hash: 314dc446e35d98c5e9c58125980b3d058e910be2

Author: CauldronGO Team

Category: analysis

Analysis of Covariance (ANCOVA) for comparing group means while controlling for covariates, with FDR correction

Install with Cauldron get_app
README

ANCOVA Analysis

Installation

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

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

Manual installation:

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

ID: ancova-analysis
Version: 1.0.0
Category: analysis
Author: CauldronGO Team

Description

Analysis of Covariance (ANCOVA) for comparing group means while controlling for covariates, with FDR correction

Workflow Diagram

flowchart TD
    Start([Start]) --> step1
    step1[Running ANCOVA for {len(feature_names]
    step1 --> step2
    step2[Applying FDR correction (Benjamini-Hochberg]
    step2 --> step3
    step3[Generating plots...]
    step3 --> step4
    step4[Computing adjusted means for significant features...]
    step4 --> End([End])

Runtime

  • Environments: python

  • Entrypoint: ancova_analysis.py

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 Feature ID Column column-selector (single) No - Always visible
metadata_col Metadata Column column-selector (single) No - Always visible
factor_col Main Factor Column column-selector (single) Yes - Always visible
covariate_cols Covariate Columns column-selector (multiple) Yes - Always visible
ss_type Sum of Squares Type select (Type I (Sequential), Type II (Hierarchical), Type III (Marginal)) No 2 Always visible
alpha FDR Threshold number (min: 0, max: 0, step: 0) No 0.05 Always visible
log2 Log2 Transform boolean No false Always visible

Input Details

Input Data File (input_file)

Data matrix with features as rows and samples as columns. Data should be log2 transformed or enable Log2 Transform option.

Annotation File (annotation_file)

Sample annotation file with Sample, Condition, and covariate columns

Feature ID Column (index_col)

Column containing feature identifiers (e.g., protein IDs)

  • Column Source: input_file

Metadata Column (metadata_col)

Optional column for display name (e.g., gene name). Features will be shown as 'ID (Name)'

  • Column Source: input_file

Main Factor Column (factor_col)

Column in annotation file containing the main grouping factor (e.g., Condition)

  • Column Source: annotation_file

Covariate Columns (covariate_cols)

Columns in annotation file containing covariates to control for (e.g., Batch, Age)

  • Column Source: annotation_file

Sum of Squares Type (ss_type)

Type of sum of squares for ANCOVA

  • Options: 1 (Type I (Sequential)), 2 (Type II (Hierarchical)), 3 (Type III (Marginal))

FDR Threshold (alpha)

False Discovery Rate threshold (Benjamini-Hochberg)

Log2 Transform (log2)

Apply log2 transformation to data before analysis. Enable if input data is not already log2 transformed.

Outputs

Name File Type Format Description
ancova_results ancova_results.txt data tsv ANCOVA results with F-statistics, p-values, adjusted p-values, and effect sizes
adjusted_means adjusted_means.txt data tsv Covariate-adjusted means for significant features
analysis_summary analysis_summary.txt data tsv Summary of analysis parameters and results
volcano_plot volcano_plot.html html html Volcano plot showing effect size vs significance
effect_size_plot effect_size_plot.html html html Bar plot of top features by effect size
pvalue_histogram pvalue_histogram.html html html Distribution of raw and adjusted p-values
boxplots boxplots.html html html Box plots showing group distributions for top significant features
violin_plots violin_plots.html html html Violin plots showing group distributions for top significant features

Sample Annotation

This plugin supports sample annotation:

  • Annotation File: annotation_file

Visualizations

This plugin generates 1 plot(s):

Volcano Plot (ancova-volcano)

  • Type: scatter
  • Data Source: ancova_results
  • Default: Yes

Requirements

  • Python Version: >=3.11

Package Dependencies (Inline)

Packages are defined inline in the plugin configuration:

  • numpy>=1.24.0
  • pandas>=2.0.0
  • statsmodels>=0.14.0
  • plotly>=5.18.0
  • click>=8.0.0

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:

  input_file: diann/imputed.data.txt
  metadata_col: Genes
  covariate_cols: [Batch]
  ss_type: 2
  alpha: 0.05
  annotation_file: diann/annotation.txt
  index_col: Protein.Ids
  factor_col: Condition
  log2: true

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

Usage

Via UI

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

Via Plugin System

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