Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space.
Nat Commun
; 13(1): 6118, 2022 Oct 17.
Статья
в английский
| MEDLINE | ID: covidwho-2077050
ABSTRACT
Computational tools for integrative analyses of diverse single-cell experiments are facing formidable new challenges including dramatic increases in data scale, sample heterogeneity, and the need to informatively cross-reference new data with foundational datasets. Here, we present SCALEX, a deep-learning method that integrates single-cell data by projecting cells into a batch-invariant, common cell-embedding space in a truly online manner (i.e., without retraining the model). SCALEX substantially outperforms online iNMF and other state-of-the-art non-online integration methods on benchmark single-cell datasets of diverse modalities, (e.g., single-cell RNA sequencing, scRNA-seq, single-cell assay for transposase-accessible chromatin use sequencing, scATAC-seq), especially for datasets with partial overlaps, accurately aligning similar cell populations while retaining true biological differences. We showcase SCALEX's advantages by constructing continuously expandable single-cell atlases for human, mouse, and COVID-19 patients, each assembled from diverse data sources and growing with every new data. The online data integration capacity and superior performance makes SCALEX particularly appropriate for large-scale single-cell applications to build upon previous scientific insights.
Полный текст:
Имеется в наличии
Коллекция:
Международные базы данных
база данных:
MEDLINE
Основная тема:
Single-Cell Analysis
/
COVID-19
Тип исследования:
Рандомизированные контролируемые испытания
Пределы темы:
Животные
/
Люди
Язык:
английский
Журнал:
Nat Commun
Тематика журнала:
Биология
/
Наука
Год:
2022
Тип:
Статья
Аффилированная страна:
S41467-022-33758-z
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