Quality_Control_for_data_quality

Quality control assessment of LC-MS data from Omics dataset to ensure data quality .

This repository contains R scripts and data for performing quality control (QC) analyses on both omics and xenobiotics datasets. The analyses follow standard reproducibility metrics and WADA guidelines for chemical and biological data.

Repository Structure

quality_control_for_data_quality/

├── QC_omics_statistical_analysis.Rmd

├──QC_xenobiotics_statistical_analysis.Rmd

├── input/

│ ├── formulas.csv

│ └── xenob_QC.csv

├── output/

├── quality_control_stats.Rproj

└── README.md

1. QC Analysis of Omics Data

File: QC_omics_statistical_analysis.Rmd

This analysis performs quality control on LC-HRMS omics datasets.

Key Steps:

1. Load raw data: Import the LC-HRMS measurements.
2. Clean data: Remove isotopologues, low-intensity features, and known contaminants.
3. Chemical filtering: Apply H/C, N/C, O/C ratios and magnitude thresholds.
4. Reproducibility metrics:

• Identify common vs. unique features across replicates.
• Calculate replicate intensity differences (RIdiff) and thresholds.

5. Average replicates: Compute mean peak intensities for repeated molecular formulas.
6. Visualizations:

• Average peak intensities per retention time
• RSD distribution across replicates

7. Export results: Cleaned datasets, reproducibility summary, and plots are saved in the output/ folder.

R Packages Used: tidyverse, data.table, NLP, jsonlite

2. QC Analysis of Xenobiotics Data

File: QC_xenobiotics_statistical_analysis.Rmd

This analysis performs quality control on xenobiotics datasets according to WADA guidelines.

Key Steps:

1. Load raw data: Import xenobiotics QC measurements.
2. Calculate measured concentration: Compute mean ratios, standard deviation, and CV (%).
3. Relative accuracy (% Bias): Compare blood and urine measurements against reference matrix.
4. Precision:

• Intra-day: Variability within the same day.
• Inter-day: Variability across different days.

5. System suitability: Assess retention time (RT) reproducibility and internal standard precision.
6. Visualizations:

• Accuracy and CV across matrices
• Intra- and inter-day precision

7. Export results: Processed datasets and plots are saved in the output/ folder. R Packages Used: tidyverse

How to Use

Clone the repository:

git clone git@github.com:beccamatos/quality_control_for_data_quality.git
cd quality_control_for_data_quality

Open the project in RStudio using quality_control_stats.Rproj. Render the Markdown files to HTML:

rmarkdown::render("QC_omics_statistical_analysis.Rmd")
rmarkdown::render("QC_xenobiotics_statistical_analysis.Rmd")