Recent Advances in Computer-Assisted Algorithms for Cell Subtype Identification of Cytometry Data.

Area of Science:

• Single-cell analysis
• Immunophenotyping
• Computational biology

Background:

• High-dimensional cytometry enables simultaneous analysis of numerous markers, resulting in large, complex datasets.
• Traditional manual gating methods are insufficient for analyzing these high-parameter datasets effectively.
• Automated computational tools are essential for clustering and identifying cell populations in multi-dimensional cytometry data.

Purpose of the Study:

• To review and categorize automated tools for clustering high-dimensional cytometry data.
• To compare the performance, usability, and speed of unsupervised and supervised clustering approaches.
• To identify current challenges and future directions in automated cell type identification.

Main Methods:

• Comprehensive review of unsupervised and supervised clustering tools for cytometry data.
• Focus on the top six unsupervised clustering tools, detailing their strengths and weaknesses.
• Direct comparison of selected unsupervised and supervised tools using a publicly available dataset.

Main Results:

• Analysis of tool popularity and usage patterns in the field.
• Comparative evaluation of usability, computational speed, and relative effectiveness of different clustering methods.
• Identification of key advantages and limitations for each reviewed tool category.

Conclusions:

• Automated clustering tools are crucial for handling high-dimensional cytometry data.
• Direct comparison reveals differences in performance and applicability between unsupervised and supervised methods.
• Future advancements are needed to address current challenges in automated cell type identification.