Data Normalization

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

Description

Normalize data using various scaling methods

Runtime

• Type: python
• Script: normalization.py

Inputs

Name	Label	Type	Required	Default	Visibility
input_file	Input File	file	Yes	-	Always visible
columns_name	Sample Columns	column-selector (multiple)	Yes	-	Always visible
scaler_type	Scaler Type	select (minmax, standard, robust, quantile, normalizer, power)	Yes	minmax	Always visible
with_centering	With Centering	boolean	No	false	Visible when scaler_type = standard
with_scaling	With Scaling	boolean	No	true	Visible when scaler_type = standard
n_quantiles	Number of Quantiles	number (min: 100, max: 10000, step: 100)	No	1000	Visible when scaler_type = quantile
output_distribution	Output Distribution	select (uniform, normal)	No	uniform	Visible when scaler_type = quantile
norm	Normalization Method	select (l1, l2, max)	No	l2	Visible when scaler_type = normalizer
power_method	Power Transform Method	select (yeo-johnson, box-cox)	No	yeo-johnson	Visible when scaler_type = power

Input Details

Input File (input_file)

Data file to normalize

Sample Columns (columns_name)

Select columns to normalize

• Column Source: input_file

Scaler Type (scaler_type)

Normalization method to use

• Options: minmax, standard, robust, quantile, normalizer, power

With Centering (with_centering)

Center the data before scaling

With Scaling (with_scaling)

Scale the data to unit variance

Number of Quantiles (n_quantiles)

Number of quantiles for quantile transformation

Output Distribution (output_distribution)

Distribution for quantile transformation output

• Options: uniform, normal

Normalization Method (norm)

Norm to use for normalizer scaler

• Options: l1, l2, max

Power Transform Method (power_method)

Method for power transformation

• Options: yeo-johnson, box-cox

Outputs

Name	File	Type	Format	Description
normalized_data	normalized.data.txt	data	tsv	Normalized data matrix

Requirements

• Python: >=3.11
• Packages:
  • numpy>=1.24.0
  • pandas>=2.0.0
  • scikit-learn>=1.3.0

Example Data

This plugin includes example data for testing:

  input_file: diann/imputed.data.txt
  columns_name_source: diann/imputed.data.txt
  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-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]
  scaler_type: minmax

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

Usage

Via UI

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

Via Plugin System

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