Automated single-cell omics end-to-end framework with data-driven batch inference

Wang, Y.; Thistlethwaite, W.; Tadych, A.; Ruf-Zamojski, F.; Bernard, D. J.; Cappuccio, A.; Zaslavsky, E.; Chen, X.; Sealfon, S. C.; Troyanskaya, O. G.

doi:10.1101/2023.11.01.564815

bioRxiv · 10.1101/2023.11.01.564815

Automated single-cell omics end-to-end framework with data-driven batch inference

Wang, Y.·Thistlethwaite, W.·Tadych, A.·Ruf-Zamojski, F.·Bernard, D. J.·Cappuccio, A.·Zaslavsky, E.·Chen, X.·Sealfon, S. C.·Troyanskaya, O. G.

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Abstract

To facilitate single-cell multi-omics analysis and improve reproducibility, we present SPEEDI (Single-cell Pipeline for End to End Data Integration), a fully automated end-to-end framework for batch inference, data integration, and cell type labeling. SPEEDI introduces data-driven batch inference and transforms the often heterogeneous data matrices obtained from different samples into a uniformly annotated and integrated dataset. Without requiring user input, it automatically selects parameters and executes pre-processing, sample integration, and cell type mapping. It can also perform downstream analyses of differential signals between treatment conditions and gene functional modules. SPEEDIs data-driven batch inference method works with widely used integration and cell-typing tools. By developing data-driven batch inference, providing full end-to-end automation, and eliminating parameter selection, SPEEDI improves reproducibility and lowers the barrier to obtaining biological insight from these valuable single-cell datasets. The SPEEDI interactive web application can be accessed at https://speedi.princeton.edu/.

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Wang, Y., Thistlethwaite, W., Tadych, A., Ruf-Zamojski, F., Bernard, D. J., Cappuccio, A., Zaslavsky, E., Chen, X., Sealfon, S. C., Troyanskaya, O. G.. 2023-11-04. Automated single-cell omics end-to-end framework with data-driven batch inference. https://doi.org/10.1101/2023.11.01.564815

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