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Welcome, this is the data repository of scBaseTraj. This dataset contains more than 48 million trajectories spanning 71 tissues, with each trajectory covering an average of 9.3 consecutive states. Approximately three-quarters of trajectories are confined within a single CytoTRACE2 stage, corresponding to relatively stable cell states, while the remaining trajectories span multiple CytoTRACE2 stages and capture dynamic state transitions.
We used this dataset to train a temporal generative AI model, CellTempo, to forecast future cellular dynamics by representing cells as learned semantic codes and training an autoregressive generation decoder to predict ordered code sequences. It can forecast long-range cell-state transition trajectories and landscapes from snapshot data. For detailed usage of this dataset, please refer to https://github.com/EperLuo/CellTempo/data.
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