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

Yuan Wang, William Thistlethwaite, Alicja Tadych, Frederique Ruf-Zamojski, Daniel J. Bernard, Antonio Cappuccio, Elena Zaslavsky, Xi Chen, Stuart C. Sealfon, Olga G. Troyanskaya

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

To facilitate single-cell multi-omics analysis and improve reproducibility, we present single-cell pipeline for end-to-end data integration (SPEEDI), 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. SPEEDI's 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/. A record of this paper's transparent peer review process is included in the supplemental information.

Original languageEnglish (US)
Pages (from-to)982-990.e5
JournalCell Systems
Volume15
Issue number10
DOIs
StatePublished - Oct 16 2024

All Science Journal Classification (ASJC) codes

  • Pathology and Forensic Medicine
  • Histology
  • Cell Biology

Keywords

  • batch identification
  • cell-type mapping
  • information theory
  • integration
  • scATAC-seq
  • scRNA-seq
  • single-cell genomics

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