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Automated single-cell omics end-to-end framework with data-driven batch inference
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 an...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10635042/ https://www.ncbi.nlm.nih.gov/pubmed/37961197 http://dx.doi.org/10.1101/2023.11.01.564815 |
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author | Wang, Yuan Thistlethwaite, William Tadych, Alicja Ruf-Zamojski, Frederique Bernard, Daniel J Cappuccio, Antonio Zaslavsky, Elena Chen, Xi Sealfon, Stuart C. Troyanskaya, Olga G. |
author_facet | Wang, Yuan Thistlethwaite, William Tadych, Alicja Ruf-Zamojski, Frederique Bernard, Daniel J Cappuccio, Antonio Zaslavsky, Elena Chen, Xi Sealfon, Stuart C. Troyanskaya, Olga G. |
author_sort | Wang, Yuan |
collection | PubMed |
description | 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/. |
format | Online Article Text |
id | pubmed-10635042 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-106350422023-11-13 Automated single-cell omics end-to-end framework with data-driven batch inference Wang, Yuan Thistlethwaite, William Tadych, Alicja Ruf-Zamojski, Frederique Bernard, Daniel J Cappuccio, Antonio Zaslavsky, Elena Chen, Xi Sealfon, Stuart C. Troyanskaya, Olga G. bioRxiv Article 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/. Cold Spring Harbor Laboratory 2023-11-04 /pmc/articles/PMC10635042/ /pubmed/37961197 http://dx.doi.org/10.1101/2023.11.01.564815 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator. |
spellingShingle | Article Wang, Yuan Thistlethwaite, William Tadych, Alicja Ruf-Zamojski, Frederique Bernard, Daniel J Cappuccio, Antonio Zaslavsky, Elena Chen, Xi Sealfon, Stuart C. Troyanskaya, Olga G. Automated single-cell omics end-to-end framework with data-driven batch inference |
title | Automated single-cell omics end-to-end framework with data-driven batch inference |
title_full | Automated single-cell omics end-to-end framework with data-driven batch inference |
title_fullStr | Automated single-cell omics end-to-end framework with data-driven batch inference |
title_full_unstemmed | Automated single-cell omics end-to-end framework with data-driven batch inference |
title_short | Automated single-cell omics end-to-end framework with data-driven batch inference |
title_sort | automated single-cell omics end-to-end framework with data-driven batch inference |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10635042/ https://www.ncbi.nlm.nih.gov/pubmed/37961197 http://dx.doi.org/10.1101/2023.11.01.564815 |
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