# CINDERELLA A semi-automatic user-friendly tool for curation of mass spectrometry metabolomic data

Nathalie Lacrampe, Mary-Lorène Goddard

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Nathalie Lacrampe, Mary-Lorène Goddard. CINDERELLA A semi-automatic user-friendly tool for curation of mass spectrometry metabolomic data. 16e Journées Scientifiques du Réseau Francophone de Métabolomique et de Fluxomique, Jun 2024, Saint - Malo, France. hal-05149270

HAL Id: hal-05149270

https://hal.science/hal-05149270v1

Submitted on 7 Jul 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# CINDERELLA
## A semi-automatic user-friendly tool for curation of mass spectrometry metabolomic data

Nathalie Lacrampe¹ and Mary-Lorène Goddard²,³

1. Laboratoire innovations en spectrométrie de masse pour la santé, CEA, Gif-sur-Yvette, France (curent position)

2. Laboratoire d'innovation moléculaire et applications, UMR 7042 CNRS Université de Haute-Alsace, Mulhouse, France

3. Laboratoire Vigne Biotechnologies et Environnement, UPR 3991 Université de Haute-Alsace, Colmar, France

e-mail: nathalie.lacrampe@gmail.com and mary-lorene.goddard@uha.fr

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Comprehensive integrated data editing
and refinement for metabolomics
by eliminating low-quality signals and artifacts

## Introduction

CINDERELLA is an **R** tool with a **user-friendly interface** for semi-automatic and supervised **artifact elimination** of non-targeted **GC-MS and LC-MS metabolomic data** prior to statistical analysis and biological interpretation. Based on pre-processed data from MS-Dial¹, Mzmine² or MetaboScape® (Bruker) along with sample metadata, CINDERELLA can, upon user request, (i) handle missing values, (ii) perform sample corrections using internal standard values and/or the amount of biological samples, (iii) correct batch effects, (iv) filter out artifacts and biologically irrelevant signals and (v) assess the quality of the remaining features. Each step is supported by interactive decision-support tables and graphs, which are compiled into an automatic output report and Excel file to enable the monitoring of the data treatment process.

### 1. Input

* Sample metadata (.xlsx) containing:
  Biological groups

Sample type: sample, blank, QC, diluted QC

Batches: extraction, analytic

* Additional biological information (.xlsx):

Sample mass: gDW, mgDW, gFW or mgFW

Water content: ratio DW/FW, %DW or %H₂O

* Unedited feature file from MS-Dial, Mzmine or Metaboscape®

→ Raw data table

### 3. Sample correction

Perform internal standard normalisation ?

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NO

Perform mass normalisation ?

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NO

Summarized distribution of internal standard by biological group

<table><thead><tr><th></th><th>Blank</th><th>QC</th><th>dQC</th><th>A</th><th>B</th><th>C</th></tr></thead><tbody><tr><th>Mean</th><td>9826.08</td><td>9886.09</td><td>1984.47</td><td>10064.77</td><td>12091.29</td><td>10358.27</td></tr><tr><th>SD</th><td>410.3</td><td>192.15</td><td>235.86</td><td>460.73</td><td>4468.86</td><td>227.41</td></tr><tr><th>RSD (%)</th><td>4.2</td><td>1.9</td><td>11.9</td><td>4.6</td><td>37</td><td>2.2</td></tr></tbody></table>

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Sample distribution before and after correction

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→ IS-corrected data table

### 6. Linearity assessment

Check if features are within the linear dynamic range of the instrument

$$ \frac{\text{Intensity (QC)}}{\text{Intensity (diluted QC)}} = \text{Theoretical dilution factor ?} $$

→ Linearity-flagged data table

### 2. Curation

* Deletion of sample and feature containing too many missing values (NA)

* Replacement of remaining missing values with a very small value or the mean / median of the biological group

* NAs in **all** biological groups (except blanks and QCs)

* NAs in **several** biological groups (except blanks and QCs)

* NAs in a **single** biological groups (except blanks and QCs)

* NAs only in blanks and QCs

→ NA-curated data table

→ Flagged data table

### 4. Batch effect correction³,⁴

Perform a batch effect correction ?

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NO

Which batch should be corrected?

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Extraction batches

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Analytical batches

Intensity of internal standard versus injection order and colored according to analytical batch

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Principal component analysis (PCA) colored according to analytical batch and guided PCA results[5]

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Analytical Batch:

Guided PCA

$$ \delta = 0.86 $$

$$ (\text{p-value} < 0.001) $$

Hierarchical clustering analysis of samples

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QC-based analysis

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→ Batch-corrected data table

### 5. Filtering

* Features with very small value

* Features with near-constant value

* Features with low repetability

* Features with a value close to blanks

Number of features after data curation: 224
Number of features removed by small values filtering: 0 (0%)
Number of features removed by near-constant values filtering: 11 (4.9%)
Number of features removed by low repetability filtering: 2 (0.9%)
Number of features removed by blank presence filtering: 119 (53.1%)
Number of features in the filtered dataset: 103

Overview of mean intensities by biological group, showing the effect of filtering features with a value close to blanks

<table><thead><tr><th></th><th>Filtered</th><th>Blank</th><th>QC</th><th>dQC</th><th>A</th><th>B</th><th>C</th></tr></thead><tbody><tr><th>ID001_03.55_352.050</th><td>FALSE</td><td>1761.9</td><td>2069.0</td><td>1768.3</td><td>6405.3</td><td>1369.6</td><td>6224.1</td></tr><tr><th>ID002_03.98_154.950</th><td>TRUE</td><td>261.2</td><td>196.8</td><td>170.3</td><td>313.1</td><td>373.8</td><td>317.1</td></tr><tr><th>ID003_04.10_102.050</th><td>FALSE</td><td>54.8</td><td>63.2</td><td>51.1</td><td>24.5</td><td>501.8</td><td>55.7</td></tr><tr><th>ID004_04.33_140.050</th><td>TRUE</td><td>2779.4</td><td>2445.5</td><td>2234.2</td><td>2600.1</td><td>2075.9</td><td>2583.7</td></tr></tbody></table>

Boxplot of filtered features

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→ Filtered data table

### 7. Export

* All data tables in one file (.xlsx)

* Report containing processing steps, figures and additional comments (.html)

STATISTICAL ANALYSIS &
BIOLOGICAL INTERPRETATION

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## References

1. Tsugawa et al., 2020 (DOI: 10.1038/s41587-020-0531-2); 2. Schmid et al., 2023 (DOI: 10.1038/s41587-023-01690-2); 3. Han and LI, 2022 (DOI: 10.1002/mas.21672);

4. Johnson et al., 2007 (DOI: 10.1093/biostatistics/kxj037); 5. Reese et al., 2013 (DOI: 10.1093/bioinformatics/btt480)

This project was supported by
the Rectorat de l'Académie de Strasbourg

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