A Differential Evolution-based Pseudotime Estimation Method for Single-cell Data

The analysis of single-cell genomics data creates an intriguing opportunity for researchers to examine the complex biological system more closely but is challenging due to inherent biological and technical noise. One popular approach involves learning a lower dimensional manifold or pseudotime trajectory through the data that can capture the primary sources of variation in the data. A smooth function of pseudotime then can be used to align gene expression patterns through the lineages in the trajectory which later facilitates downstream analysis such as heterogeneous cell type identification. Here, we propose a differential evolution based pseudotime estimation method. The model operates on continuous search space and allows easy integration of the cell capture time information in the inference process. The suitability of the proposed model is investigated by applying it on benchmarking single-cell data sets collected from different organisms using different assaying techniques. The experimental result shows the model's capability of producing plausible biological insights about cell ordering which makes it an appealing choice for pseudoitme estimation using single-cell transcriptome data.