A Holistic Approach for Single-Cell Data Trajectory Inference Using Chromosome Physical Location and Ensemble Random Walk

Abstract:
Single-cell RNA sequencing technology enables the analysis of complex, heterogeneous cell samples. However, errors in data processing, dimension reduction, and clustering can negatively impact subsequent calculations, particularly when inferring cell trajectories using graph methods. We proposed a novel method for single-cell data Trajectory Inference using Chromosome physical location and ensemble Random Walk (scCRW). It utilizes entire chromosomes and their gene identifiers to enhance factor analysis, providing a more comprehensive view of biological processes. For trajectory inference, scCRW employs a random walk, which has been evaluated against other state-of-the-art methods using real single-cell RNA-seq datasets. These datasets include both linear and nonlinear data, showcasing scCRW’s capabilities in pseudotime and trajectory inference tasks. The results demonstrate that scCRW consistently achieves top or near-top correlation scores and excels in nonlinear metrics such as F1 branches and milestones. This approach provides accurate trajectory inference that closely aligns with ground truth, highlighting the utility of using chromosomes in factor analysis and random walk techniques for more precise data analysis.

Citation:
Cardoza-Aguilar, J., Milbourn, C., Zhang, Y., Yang, L., Dascalu, S.M., Harris, F.C. (2024). A Holistic Approach for Single-Cell Data Trajectory Inference Using Chromosome Physical Location and Ensemble Random Walk. In: Latifi, S. (eds) ITNG 2024: 21st International Conference on Information Technology-New Generations. ITNG 2024. Advances in Intelligent Systems and Computing, vol 1456. Springer, Cham. https://doi.org/10.1007/978-3-031-56599-1_64