ZINBMM: a general mixture model for simultaneous clustering and gene selection using single-cell transcriptomic data

Li, Yang; Wu, Mingcong; Ma, Shuangge; Wu, Mengyun

doi:10.1186/s13059-023-03046-0

Cited by 2 publications

References 64 publications

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Deep Batch Integration and Denoise of Single‐Cell RNA‐Seq Data

Qin,

Zhang,

Zhang

et al. 2024

Advanced Science

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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.

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Deep Batch Integration and Denoise of Single‐Cell RNA‐Seq Data

Qin,

Zhang,

Zhang

et al. 2024

Advanced Science

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scCobra: Contrastive cell embedding learning with domain-adaptation for single-cell data integration and harmonization

Zhao

Wei

Xiong

et al. 2022

Preprint

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The ever-increasing availability of single-cell transcriptomic data offers unrivaled opportunities to profile cellular states in various biological processes at high resolution, which has brought substantial advancements in understanding complex mechanisms underlying a large variety of bioprocesses. As limited by the protocol and technology, single-cell measurements in one study are often performed in batches, which unavoidably induces biological and technical differences in the single-cell measurements of the same study. Consequently, it presents challenges in analyzing all single-cell data from different batches together, particularly if the measurements were assayed with different technologies. Several methods have been developed to remove the aforementioned batch effects recently. However, there remain challenges unaddressed with those existing methods, including but not limited to the risk of over-correction, the need for the assumption of gene expression distribution, and expensive computation. To mitigate those limitations, we develop a novel deep learning method called scCobra that combines contrastive learning, domain adaptation, and generative adversarial networks to remove batch effects in single-cell RNA-seq data. The contrastive learning network is utilized to learn latent embeddings to represent the cells, domain-adaptation is employed to batch-normalize the latent embeddings of cells from distinct batches, while generative adversarial networks further optimize the blending effect. The proposed method does not require any prior assumption of gene expression distribution. We applied the scCobra method to one simulated and two real single-cell datasets with significantly experimental differences. Our method outperforms other benchmarked methods in batch correction and biological conservation, and its running efficiency is also among the best.

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ZINBMM: a general mixture model for simultaneous clustering and gene selection using single-cell transcriptomic data

Cited by 2 publications

References 64 publications

Deep Batch Integration and Denoise of Single‐Cell RNA‐Seq Data

Deep Batch Integration and Denoise of Single‐Cell RNA‐Seq Data

scCobra: Contrastive cell embedding learning with domain-adaptation for single-cell data integration and harmonization

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