Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis

By Xiangjie Li, Kui Wang, Yafei Lyu, Huize Pan, Jingxiao Zhang, D. Stambolian, K. Suszták, M. Reilly, Gang Hu, Mingyao Li. Nature Communications, 2020

372 citations recorded by the source corpus.

Single-cell RNA sequencing (scRNA-seq) can characterize cell types and states through unsupervised clustering, but the ever increasing number of cells and batch effect impose computational challenges. We present DESC, an unsupervised deep embedding algorithm that clusters scRNA-seq data by iteratively optimizing a clustering objective function. Through iterative self-learning, DESC gradually removes batch effects, as long as technical differences across batches are smaller than true biological variations. As a soft clustering algorithm, cluster assignment probabilities from DESC are biologically interpretable and can reveal both discrete and pseudotemporal structure of cells. Comprehensive evaluations show that DESC offers a proper balance of clustering accuracy and stability, has a small footprint on memory, does not explicitly require batch information for batch effect removal, and can utilize GPU when available. As the scale of single-cell studies continues to grow, we believe DESC will offer a valuable tool for biomedical researchers to disentangle complex cellular heterogeneity. Increasingly large scRNA-seq datasets demand better and more scalable analysis tools. Here, the authors introduce a scalable unsupervised deep embedding algorithm that clusters scRNA-seq data by iteratively optimizing a clustering objective function and enables removal of batch effects.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2406b53314c413a4974d47b251204a053b7eb3b1

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In Biophysics

1. Blaz Zupan
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Citations in the corpus

• Embedding to reference t-SNE space addresses batch effects in single-cell classification (2019, background): “, 2018), while others employ non-linear techniques from deep learning (Li et al., 2020; Lopez et al., 2018).”