scAdapt: virtual adversarial domain adaptation network for single cell RNA-seq data classification across platforms and species.
Brief Bioinform
; 22(6)2021 11 05.
Article
in English
| MEDLINE | ID: covidwho-1324576
ABSTRACT
In single cell analyses, cell types are conventionally identified based on expressions of known marker genes, whose identifications are time-consuming and irreproducible. To solve this issue, many supervised approaches have been developed to identify cell types based on the rapid accumulation of public datasets. However, these approaches are sensitive to batch effects or biological variations since the data distributions are different in cross-platforms or species predictions. In this study, we developed scAdapt, a virtual adversarial domain adaptation network, to transfer cell labels between datasets with batch effects. scAdapt used both the labeled source and unlabeled target data to train an enhanced classifier and aligned the labeled source centroids and pseudo-labeled target centroids to generate a joint embedding. The scAdapt was demonstrated to outperform existing methods for classification in simulated, cross-platforms, cross-species, spatial transcriptomic and COVID-19 immune datasets. Further quantitative evaluations and visualizations for the aligned embeddings confirm the superiority in cell mixing and the ability to preserve discriminative cluster structure present in the original datasets.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Single-Cell Analysis
/
Transcriptome
/
SARS-CoV-2
/
COVID-19
Type of study:
Experimental Studies
/
Prognostic study
/
Randomized controlled trials
Limits:
Humans
Language:
English
Journal subject:
Biology
/
Medical Informatics
Year:
2021
Document Type:
Article
Affiliation country:
Bib
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