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Currently, there are several different dataset objects specialized for each task and model (e.g., CellTypeDataset, ClusteringDataset), each of them takes a variety of specialized arguments that are not directly related to the underlying data, e.g., save path, processing scheme, choice of tissue. This complexity makes it quite hard to maintain the code base and implement new methods/datasets.
To improve this situation, we need to isolate raw dataset objects from transformation/processing methods.
Base data object
Take AnnData as an input and save it as a private attribute (read-only?).
Construct data loaders that load g, x, y, etc., to be passed to the model for training/evaluation.
Dataset object
Download option
Transformation option
Dataset from paper (preprocessed) -> used to benchmark the reproducibility of the reimplemented model
Transformation
Leverage functionalities from scanpy (recall that now the base data object store an AnnData object as a (private) attribute
Currently, there are several different dataset objects specialized for each task and model (e.g.,
CellTypeDataset
,ClusteringDataset
), each of them takes a variety of specialized arguments that are not directly related to the underlying data, e.g., save path, processing scheme, choice of tissue. This complexity makes it quite hard to maintain the code base and implement new methods/datasets.To improve this situation, we need to isolate raw dataset objects from transformation/processing methods.
AnnData
as an input and save it as a private attribute (read-only?).scanpy
(recall that now the base data object store anAnnData
object as a (private) attributeTo fix
Single modality
Spatial
Multi modality
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