Deep Batch Integration and Denoise of Single-Cell RNA-Seq Data.
Adv Sci (Weinh)
; 11(29): e2308934, 2024 Aug.
Article
in En
| MEDLINE
| ID: mdl-38778573
ABSTRACT
Numerous single-cell transcriptomic datasets from identical tissues or cell lines are generated from different laboratories or single-cell RNA sequencing (scRNA-seq) protocols. The denoising of these datasets to eliminate batch effects is crucial for data integration, ensuring accurate interpretation and comprehensive analysis of biological questions. Although many scRNA-seq data integration methods exist, most are inefficient and/or not conducive to downstream analysis. Here, DeepBID, a novel deep learning-based method for batch effect correction, non-linear dimensionality reduction, embedding, and cell clustering concurrently, is introduced. DeepBID utilizes a negative binomial-based autoencoder with dual Kullback-Leibler divergence loss functions, aligning cell points from different batches within a consistent low-dimensional latent space and progressively mitigating batch effects through iterative clustering. Extensive validation on multiple-batch scRNA-seq datasets demonstrates that DeepBID surpasses existing tools in removing batch effects and achieving superior clustering accuracy. When integrating multiple scRNA-seq datasets from patients with Alzheimer's disease, DeepBID significantly improves cell clustering, effectively annotating unidentified cells, and detecting cell-specific differentially expressed genes.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
RNA-Seq
/
Single-Cell Gene Expression Analysis
Limits:
Humans
Language:
En
Journal:
Adv Sci (Weinh)
/
Advanced science (Weinheim)
Year:
2024
Document type:
Article
Affiliation country:
China
Country of publication:
Alemania