tidytof: a user-friendly framework for scalable and reproducible high-dimensional cytometry data analysis.

Area of Science:

• Computational Biology
• Bioinformatics

Background:

• High-dimensional cytometry data analysis involves complex algorithms.
• Current software implementations are often customized and lack interoperability.
• This necessitates users learning multiple, disparate package syntaxes for data processing.

Purpose of the Study:

• To develop an open-source R package for analyzing high-dimensional cytometry data.
• To provide a unified and interoperable solution for cytometry data analysis.
• To leverage the 'tidy data' interface for improved user experience.

Main Methods:

• Development of the tidytof R package.
• Implementation of the 'tidy data' principles for data manipulation.
• Ensuring compatibility across Linux, MS Windows, and MacOS.

Main Results:

• tidytof offers a streamlined approach to high-dimensional cytometry data analysis.
• The package provides a consistent and interoperable syntax for various analysis steps.
• It enhances accessibility for researchers working with complex cytometry datasets.

Conclusions:

• tidytof addresses the challenge of fragmented software in cytometry data analysis.
• The package promotes reproducible and efficient research in the field.
• It facilitates broader adoption of advanced analytical methods by researchers.