Author: Fischer, David S.; Dony, Leander; König, Martin; Moeed, Abdul; Zappia, Luke; Heumos, Lukas; Tritschler, Sophie; Holmberg, Olle; Aliee, Hananeh; Theis, Fabian J.
Title: Sfaira accelerates data and model reuse in single cell genomics Cord-id: 4wip7z4v Document date: 2021_8_25
ID: 4wip7z4v
Snippet: Single-cell RNA-seq datasets are often first analyzed independently without harnessing model fits from previous studies, and are then contextualized with public data sets, requiring time-consuming data wrangling. We address these issues with sfaira, a single-cell data zoo for public data sets paired with a model zoo for executable pre-trained models. The data zoo is designed to facilitate contribution of data sets using ontologies for metadata. We propose an adaption of cross-entropy loss for ce
Document: Single-cell RNA-seq datasets are often first analyzed independently without harnessing model fits from previous studies, and are then contextualized with public data sets, requiring time-consuming data wrangling. We address these issues with sfaira, a single-cell data zoo for public data sets paired with a model zoo for executable pre-trained models. The data zoo is designed to facilitate contribution of data sets using ontologies for metadata. We propose an adaption of cross-entropy loss for cell type classification tailored to datasets annotated at different levels of coarseness. We demonstrate the utility of sfaira by training models across anatomic data partitions on 8 million cells. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02452-6.
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