Reproducible Data Analysis With R in Laboratory Hematology

Area of Science:

• Laboratory Medicine
• Bioinformatics
• Computational Biology

Background:

• Traditional spreadsheet workflows in laboratory hematology are insufficient for large, complex datasets.
• Spreadsheets lack transparency, auditability, and reproducibility due to manual data manipulation and obscured analytical logic.
• These limitations pose significant challenges in regulated clinical laboratory environments.

Purpose of the Study:

• To outline the limitations of spreadsheet-based analysis in laboratory hematology.
• To introduce script-based data analysis as a reproducible alternative.
• To highlight practical applications of script-based analysis in hematology.

Main Methods:

• Review of structural limitations of spreadsheet-centered analysis.
• Introduction of script-based analysis using the R programming language.
• Discussion of complementary technologies: IDEs, version control, and LLMs.

Main Results:

• Spreadsheets obscure analytical logic, leading to potential errors and limiting reproducibility.
• Script-based analysis provides an explicit, ordered, and reusable framework for hematology workflows.
• Applications demonstrated in method comparison, reference interval estimation, quality control, and flow cytometry.

Conclusions:

• Script-based analysis, particularly with R, enhances data integrity and analytical rigor in laboratory hematology.
• Adoption of reproducible tools is crucial for advancing analytical techniques and regulatory compliance.
• This approach prepares laboratories for future data-intensive challenges and advanced analytics.