Recovering Gene Interactions from Single-Cell Data Using Data Diffusion

Dijk, David van; Sharma, Roshan; Nainys, Juoas; Yim, Kristina; Kathail, Pooja; Carr, A. J. H.; Burdziak, Cassandra; Moon, Kevin R.; Chaffer, Christine L.; Pattabiraman, Diwakar R.; Bierie, Brian; Mažutis, Linas; Wolf, Guy; Krishnaswamy, Smita; Pe’er, Dana

doi:10.2139/ssrn.3155779

Cited by 253 publications

(370 citation statements)

References 56 publications

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“…On a computer science oriented branch of the scRNA-seq methods field, many methods have been designed to correct dropout zeros in data, with the aim of letting a user predict what the expression level of a gene in a cell would have been, had there been no zero-inflation or dropouts (Azizi et al, 2017;van Dijk et al, 2018;Gong et al, 2018;Huang et al, 2018;Li and Li, 2018;Tang et al, 2018;Zhu et al, 2016).…”

Section: Valentine Svenssonmentioning

confidence: 99%

Droplet scRNA-seq is not zero-inflated

2020

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Section: Valentine Svenssonmentioning

confidence: 99%

Droplet scRNA-seq is not zero-inflated

2020

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“…There are also imputation methods designed to explicitly remove dropouts. A few recent examples include MAGIC 27 , SAVER 28 , scImpute 29 , and RESCUE 30 . These imputation methods typically use highly variable genes and dimension reduction to define gene-gene similarities or cell-cell similarities, which provide the basis for imputing the dropouts with appropriate values.…”

mentioning

confidence: 99%

Embracing the dropouts in single-cell RNA-seq analysis

2020

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One primary reason that makes single-cell RNA-seq analysis challenging is dropouts, where the data only captures a small fraction of the transcriptome of each cell. Almost all computational algorithms developed for single-cell RNA-seq adopted gene selection, dimension reduction or imputation to address the dropouts. Here, an opposite view is explored. Instead of treating dropouts as a problem to be fixed, we embrace it as a useful signal. We represent the dropout pattern by binarizing single-cell RNA-seq count data, and present a cooccurrence clustering algorithm to cluster cells based on the dropout pattern. We demonstrate in multiple published datasets that the binary dropout pattern is as informative as the quantitative expression of highly variable genes for the purpose of identifying cell types. We expect that recognizing the utility of dropouts provides an alternative direction for developing computational algorithms for single-cell RNA-seq analysis.

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“…To address the issue of dropout, several imputation methods have been developed, e.g., MAGIC [43], scImpute [44] and drImpute [45]. Inspired by the recent success of autoencoders for sparse matrix imputation in collaborative filtering for recommendation systems, Talwar et al proposed an autoencoder-based method called "AutoImpute" to handle the dropout in scRNAseq data [46].…”

Section: Deep Learning Methods For Scrna-seq Data Analysismentioning

confidence: 99%

Emerging deep learning methods for single‐cell RNA‐seq data analysis

Zheng

Wang

2019

Quant. Biol.

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Deep learning is making major breakthrough in several areas of bioinformatics. Anticipating that this will occur soon for the single-cell RNA-seq data analysis, we review newly published deep learning methods that help tackle computational challenges. Autoencoders are found to be the dominant approach. However, methods based on deep generative models such as generative adversarial networks (GANs) are also emerging in this area.

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Recovering Gene Interactions from Single-Cell Data Using Data Diffusion

Cited by 253 publications

References 56 publications

Droplet scRNA-seq is not zero-inflated

Droplet scRNA-seq is not zero-inflated

Embracing the dropouts in single-cell RNA-seq analysis

Emerging deep learning methods for single‐cell RNA‐seq data analysis

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