Parametric analysis of histograms measured in flow cytometry

Area of Science:

• Biomedical Engineering
• Computational Biology
• Cell Biology

Background:

• Flow cytometry generates histograms with overlapping components, complicating data analysis.
• Accurate decomposition of these histograms is crucial for various biological and medical applications.

Purpose of the Study:

• To investigate the maximum likelihood approach for histogram decomposition in flow cytometry.
• To assess the robustness and performance of iterative maximum likelihood methods.

Main Methods:

• Detailed investigation of the maximum likelihood estimation (MLE) approach.
• Development and presentation of algorithms for obtaining initial values for iterative methods.
• Testing the performance of the MLE method on simulated and real flow cytometry data.

Main Results:

• The iterative maximum likelihood method demonstrates well-behaved convergence across various initial values.
• The approach effectively decomposes overlapping components in flow cytometric histograms.
• Successful application to the analysis of DNA content in heterogeneous cell populations was demonstrated.

Conclusions:

• The maximum likelihood approach provides a reliable and effective method for flow cytometry histogram decomposition.
• The developed algorithms enhance the practical application of this technique for analyzing complex cell populations.