Analysis of cell deconvolution methods

Abstract

In recent years, a new way of cancer diagnostics has emerged, the analysis of DNA fragments circulating in the blood of cancer patients known as fragmentomics. This DNA, known as cell-free DNA (cfDNA), is an easily available biomarker for cell types. Deducing the tissue origin of cfDNA can reveal anomalies in cell death caused by diseases. That holds great potential in cancer detection and monitoring. The process of establishing the cell composition of a blood sample is called cell deconvolution. This research paper focuses on the comparison of two methods of cell deconvolution. The first one UXM, solves this problem by employing a reference-based technique using a methylation atlas. The second one reference-free cfSort, utilizes a Deep Learning Neural Network to perform the sample analysis. In the paper, however, a simpler architecture was trained due to the difficulties in reproduction. Experiments have been conducted to assess the sensitivity of both methods. Experiments consisted of 5 major cell types together mixed with white blood cell DNA fragments to assess the sensitivity of each method. Furthermore, different metrics such as Pearson's correlation coefficient have been used to determine the accuracy of both methods. In the end, UXM outperformed cfSort in most metrics, including Pearson's correlation coefficient, indicating its superior accuracy in deconvolution tasks. However, cfSort showed potential for higher prediction accuracy with further development and better documentation. The findings highlight the strengths and limitations of both methods. This study suggests that while UXM is currently more reliable, future improvements in cfSort could make it a viable alternative. Continued research is recommended to enhance the accuracy and transparency of these methods, ensuring their effectiveness in real-world healthcare applications.