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Sfaira accelerates data and model reuse in single cell genomics.

ABSTRACT: 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.

SUBMITTER: Fischer DS

PROVIDER: S-EPMC8386039 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Sfaira accelerates data and model reuse in single cell genomics.

Fischer David S DS Dony Leander L König Martin M Moeed Abdul A Zappia Luke L Heumos Lukas L Tritschler Sophie S Holmberg Olle O Aliee Hananeh H Theis Fabian J FJ

Genome biology 20210825 1

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 ...[more]

PMID: 34433466

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