Yuhan Wang

Single-cell RNA sequencing destroys each cell as it measures it and samples only discrete timepoints, so one cell's expression profile cannot be followed continuously. The Latent Cell-State Model approximates cell-state transitions as a Markov chain and adds a low-dimensional continuous latent variable to generate expression counts given cell state, time and gene. On the pancreatic development and Waddington-OT reprogramming datasets, maximum mean discrepancy between generated and real samples at held-out timepoints was 0.011 and 0.006 (pancreatic days 3 and 6) and 0.030 and 0.018 (reprogramming days 3.5 and 4), lower than the three compared methods.

Single-cell RNA sequencing destroys each cell as it measures it and samples only discrete timepoints, so one cell's expression profile cannot be followed continuously. The Latent Cell-State Model approximates cell-state transitions as a Markov chain and adds a low-dimensional continuous latent variable to generate expression counts given cell state, time and gene. On the pancreatic development and Waddington-OT reprogramming datasets, maximum mean discrepancy between generated and real samples at held-out timepoints was 0.011 and 0.006 (pancreatic days 3 and 6) and 0.030 and 0.018 (reprogramming days 3.5 and 4), lower than the three compared methods.