Probabilistic Models for Single-Cell RNA Sequencing Data
Trajectory and pseudo-time inference methods in single-cell RNA sequencing face challenges from the ambiguity of the static single-cell transcriptome snapshot data. In this project, we aim to tackle this challenge by means of advanced probabilistic methods.
Concretely, we aim to reconstruct unobserved cell ordering as latent pseudo-time by analyzing RNA spliced counts and corresponding derivative RNA velocity. Further, we aim to obtain uncertainty estimates of the latent cell ordering using Bayesian inference.
To achieve these goals, we will develop advanced latent Gaussian process models with the ability of utilizing derivative information to increase precision in estimating unobserved latent inputs. This model deploys derivative covariance kernel functions and modifications in the hyperparameter specifications, thus increasing capabilities for utilizing derivative information in a multi-output setting.
Although the primary motivation lies in applications in single-cell biology, this model has the potential to solve similar research problems dealing with multi-output data and its derivatives from diverse fields of study.