端到端原则
推论
计算机科学
计算生物学
数据挖掘
人工智能
生物
作者
Yuan Wang,William Thistlethwaite,Alicja Tadych,Frederique Ruf-Zamojski,Daniel J. Bernard,Antonio Cappuccio,Elena Zaslavsky,Xi Chen,Stuart C. Sealfon,Olga G. Troyanskaya
标识
DOI:10.1101/2023.11.01.564815
摘要
Summary 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. 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/ .
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