BERMUDA: a novel deep transfer learning method for single-cell RNA sequencing batch correction reveals hidden high-resolution cellular subtypes

Wang, Tongxin; Johnson, Travis; Shao, Wei; Lu, Zixiao; Helm, Bryan R.; Zhang, Jie; Huang, Kun

doi:10.1186/s13059-019-1764-6

Cited by 119 publications

(87 citation statements)

References 44 publications

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“…We compared BATMAN with three other state-of-the-art tools which operate in the gene space: Seurat v3.0, MNN, and Scanorama. We did not include tools such as limma and ComBat in the comparison, as they are are more appropriate for bulk RNA-Seq analysis and have been shown to fail in more complicated scenarios characteristic of single-cell datasets 11 . We also did not include SAUCIE, BERMUDA, and Harmony in the comparison since they operate on the latent spaces.…”

Section: Resultsmentioning

confidence: 99%

“…Traditionally, scRNA-Seq dataset integration quality has been assessed visually using UMAP and/or tSNE plots. However, there are multiple quantitative evaluation metrics available 7,11,14 . All of the metrics are based on scanning local neighborhoods of the cells in the combined dataset (i.e., after integration) and testing if the proportion of cells from the two datasets is the same as globally, for example, using the entropy mixing score.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…These assumptions may not hold true for some real datasets. Additionally, ComBat and limma were not designed to handle datasets consisting of multiple cell types 11 . As it has been previously shown, in more complicated scenarios characteristic to single-cell datasets, these methods perform poorly 11 .…”

Section: Introductionmentioning

confidence: 99%

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BATMAN: fast and accurate integration of single-cell RNA-Seq datasets via minimum-weight matching

Mandric

Hill

Freund

et al. 2020

Preprint

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Single-cell RNA-Sequencing (scRNA-Seq) is a set of technologies used to profile gene expression at the level of individual cells. Although the throughput of scRNA-Seq experiments is steadily growing in terms of the number of cells, large datasets are not yet commonly used due to prohibitively high costs. Integrating multiple datasets into one can improve power in scRNA-Seq experiments, and efficient integration is very important for downstream analyses such as identifying cell-type-specific eQTLs. State-of-the-art scRNA-Seq integration methods are based on the mutual nearest neighbors paradigm and fail to both correct for batch effects and maintain the local structure of the datasets. In this paper, we propose a novel scRNA-Seq dataset integration method called BATMAN (BATch integration via minimum-weight MAtchiNg). Across multiple simulations and real datasets, we show that our method significantly outperforms state-of-the-art tools with respect to existing metrics for batch effects by up to 80% while retaining cell-to-cell relationships. BATMAN is available at https://github.com/mandricigor/batman .

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Section: Resultsmentioning

confidence: 99%

Section: Evaluation Metricsmentioning

confidence: 99%

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BATMAN: fast and accurate integration of single-cell RNA-Seq datasets via minimum-weight matching

Mandric

Hill

Freund

et al. 2020

Preprint

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“…Although a simple and effective algorithm for graph building is implemented, we intentionally allow the use of custom similarity graphs to cope with batch effects. A plethora of batch correction methods has been proposed and northstar is designed to be compatible with most of them [12,13,24,25] .…”

Section: Discussionmentioning

confidence: 99%

“…This method is resource intensive and often leads to ambiguous classification, because atlas cell types can split into subclusters or merge into superclusters. Batch correction techniques can also be adapted for this task but are equally greedy for computational resources [12,13] . Supervised learning approaches -training a classifier on an atlas and using it on the new dataset -are too restrictive because cell states missing from the atlas (e.g.…”

Section: Introductionmentioning

confidence: 99%

Northstar enables automatic classification of known and novel cell types from tumor samples

Zanini

Berghuis

Jones

et al. 2019

Preprint

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Cell atlases are revolutionizing our understanding of tissue and disease heterogeneity, yet most single-cell transcriptomic analyses on tumors are not leveraging atlases effectively. We developed northstar, a computational approach to classify cells in tumor datasets guided by but not restricted by previously annotated cell atlases. To benchmark northstar, we transferred annotations from a human brain atlas to a published dataset on glioblastoma and could recapitulate the tumor composition accurately and within seconds. We then collected 1,622 cells from 11 pancreatic tumors and could robustly identify healthy pancreatic and immune cells and neoplastic cell states. Three cell populations were shared across patients while five were private to a single sample. northstar's cell type classification offered rapid insight into the origins of neuroendocrine and exocrine tumors and fibromatosis. northstar is a useful tool to classify single-cell transcriptomes into known and novel cell types in the age of cell atlases.

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Distribution‐Agnostic Deep Learning Enables Accurate Single‐Cell Data Recovery and Transcriptional Regulation Interpretation

Su,

Yu,

Yang

et al. 2024

Advanced Science

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Single‐cell RNA sequencing (scRNA‐seq) is a robust method for studying gene expression at the single‐cell level, but accurately quantifying genetic material is often hindered by limited mRNA capture, resulting in many missing expression values. Existing imputation methods rely on strict data assumptions, limiting their broader application, and lack reliable supervision, leading to biased signal recovery. To address these challenges, authors developed Bis, a distribution‐agnostic deep learning model for accurately recovering missing sing‐cell gene expression from multiple platforms. Bis is an optimal transport‐based autoencoder model that can capture the intricate distribution of scRNA‐seq data while addressing the characteristic sparsity by regularizing the cellular embedding space. Additionally, they propose a module using bulk RNA‐seq data to guide reconstruction and ensure expression consistency. Experimental results show Bis outperforms other models across simulated and real datasets, showcasing superiority in various downstream analyses including batch effect removal, clustering, differential expression analysis, and trajectory inference. Moreover, Bis successfully restores gene expression levels in rare cell subsets in a tumor‐matched peripheral blood dataset, revealing developmental characteristics of cytokine‐induced natural killer cells within a head and neck squamous cell carcinoma microenvironment.

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BERMUDA: a novel deep transfer learning method for single-cell RNA sequencing batch correction reveals hidden high-resolution cellular subtypes

Cited by 119 publications

References 44 publications

BATMAN: fast and accurate integration of single-cell RNA-Seq datasets via minimum-weight matching

BATMAN: fast and accurate integration of single-cell RNA-Seq datasets via minimum-weight matching

Northstar enables automatic classification of known and novel cell types from tumor samples

Distribution‐Agnostic Deep Learning Enables Accurate Single‐Cell Data Recovery and Transcriptional Regulation Interpretation

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