TY - JOUR
T1 - Automated single-cell omics end-to-end framework with data-driven batch inference
AU - Wang, Yuan
AU - Thistlethwaite, William
AU - Tadych, Alicja
AU - Ruf-Zamojski, Frederique
AU - Bernard, Daniel J.
AU - Cappuccio, Antonio
AU - Zaslavsky, Elena
AU - Chen, Xi
AU - Sealfon, Stuart C.
AU - Troyanskaya, Olga G.
N1 - Publisher Copyright:
© 2024 Elsevier Inc.
PY - 2024/10/16
Y1 - 2024/10/16
N2 - 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.
AB - 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.
KW - batch identification
KW - cell-type mapping
KW - information theory
KW - integration
KW - scATAC-seq
KW - scRNA-seq
KW - single-cell genomics
UR - http://www.scopus.com/inward/record.url?scp=85206957278&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85206957278&partnerID=8YFLogxK
U2 - 10.1016/j.cels.2024.09.003
DO - 10.1016/j.cels.2024.09.003
M3 - Article
C2 - 39366377
AN - SCOPUS:85206957278
SN - 2405-4712
VL - 15
SP - 982-990.e5
JO - Cell Systems
JF - Cell Systems
IS - 10
ER -