MLBKFD: Probabilistic Model Methods to Infer Pseudo Trajectories from Single-cell Data.

Cell trajectory inference is very important to understand the details of tissue cell development, state differentiation and gene dynamic regulation. However, due to the high noise and heterogeneity of the single-cell data, it is challenging to infer cell trajectory in complex biological processes. Here, we proposed a new trajectory inference method, called Metric Learning Bhattacharyya Kernel Feature Decomposition (MLBKFD). In MLBKFD, a statistical model was used to infer cell trajectory by calculating the Markov transition matrix and Bhattacharyya kernel matrix between cells. Before that, to expedite the matrix calculation in the statistical model, a deep feedforward neural network was used to perform dimensionality reduction on single-cell data. The MLBKFD was evaluated on four typical datasets as well as seven recent human fetal lung datasets. Comparisons with the two outstanding methods (i.e., DTFLOW and MARGARET) demonstrate that the MLBKFD is capable of accurately inferring cell development and differentiation trajectories from single-cell data with different sizes and sources. Notably, MLBKFD exhibits nearly twice the speed of DTFLOW while maintaining high precision, particularly when dealing with large datasets. MLBKFD provides accurate and efficient trajectory inference, empowering researchers to gain deeper insights into the complex dynamics of cell development and differentiation. 1. MLBKFD is a powerful probabilistic model to effectively infer cell pseudo trajectories from single-cell data by calculating the Markov transition matrix and Bhattacharyya kernel matrix between cells. 2. MLBKFD exhibits fast speed and excellent visualization effects on cell trajectory inference, by utilizing a deep feedforward neural networks for dimensionality reduction of single-cell data. 3. By iteratively refining cell labels in the deep neural networks, MLBKFD can achieve excellent cell clustering results, enabling it to infer cell trajectories without relying on true cell labels. [ABSTRACT FROM AUTHOR]