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Optimal tuning of weighted kNN- and diffusion-based methods for denoising single cell genomics data

View ORCID ProfileAndreas Tjärnberg, Omar Mahmood, View ORCID ProfileChristopher A Jackson, Giuseppe-Antonio Saldi, Kyunghyun Cho, Lionel A Christiaen, Richard A Bonneau
doi: https://doi.org/10.1101/2020.02.28.970202
Andreas Tjärnberg
1Center for Developmental Genetics, New York University, New York 10003 NY, USA
2Center For Genomics and Systems Biology, NYU, New York, NY 10008, USA
3Department of Biology, NYU, New York, NY 10008, USA
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  • ORCID record for Andreas Tjärnberg
  • For correspondence: at145@nyu.edu rb133@nyu.edu
Omar Mahmood
5Courant Institute of Mathematical Sciences, Computer Science Department, New York University, New York, NY 10003, USA
6Center For Data Science, NYU, New York, NY 10008, USA
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Christopher A Jackson
2Center For Genomics and Systems Biology, NYU, New York, NY 10008, USA
3Department of Biology, NYU, New York, NY 10008, USA
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Giuseppe-Antonio Saldi
2Center For Genomics and Systems Biology, NYU, New York, NY 10008, USA
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Kyunghyun Cho
5Courant Institute of Mathematical Sciences, Computer Science Department, New York University, New York, NY 10003, USA
6Center For Data Science, NYU, New York, NY 10008, USA
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Lionel A Christiaen
1Center for Developmental Genetics, New York University, New York 10003 NY, USA
3Department of Biology, NYU, New York, NY 10008, USA
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Richard A Bonneau
2Center For Genomics and Systems Biology, NYU, New York, NY 10008, USA
3Department of Biology, NYU, New York, NY 10008, USA
4Flatiron Institute, Center for Computational Biology, Simons Foundation, New York, NY 10010, USA
5Courant Institute of Mathematical Sciences, Computer Science Department, New York University, New York, NY 10003, USA
6Center For Data Science, NYU, New York, NY 10008, USA
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  • For correspondence: at145@nyu.edu rb133@nyu.edu
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Abstract

The analysis of single-cell genomics data presents several statistical challenges, and extensive efforts have been made to produce methods for the analysis of this data that impute missing values, address sampling issues and quantify and correct for noise. In spite of such efforts, no consensus on best practices has been established and all current approaches vary substantially based on the available data and empirical tests. The k-Nearest Neighbor Graph (kNN-G) is often used to infer the identities of, and relationships between, cells and is the basis of many widely used dimensionality-reduction and projection methods. The kNN-G has also been the basis for imputation methods using, e.g., neighbor averaging and graph diffusion. However, due to the lack of an agreed-upon optimal objective function for choosing hyperparameters, these methods tend to oversmooth data, thereby resulting in a loss of information with regard to cell identity and the specific gene-to-gene patterns underlying regulatory mechanisms. In this paper, we investigate the tuning of kNN- and diffusion-based denoising methods with a novel non-stochastic method for optimally preserving biologically relevant informative variance in single-cell data. The framework, Denoising Expression data with a Weighted Affinity Kernel and Self-Supervision (DEWÄKSS), uses a self-supervised technique to tune its parameters. We demonstrate that denoising with optimal parameters selected by our objective function (i) is robust to preprocessing methods using data from established benchmarks, (ii) disentangles cellular identity and maintains robust clusters over dimension-reduction methods, (iii) maintains variance along several expression dimensions, unlike previous heuristic-based methods that tend to oversmooth data variance, and (iv) rarely involves diffusion but rather uses a fixed weighted kNN graph for denoising. Together, these findings provide a new understanding of kNN- and diffusion-based denoising methods and serve as a foundation for future research. Code and example data for DEWÄKSS is available at https://gitlab.com/Xparx/dewakss/-/tree/Tjarnberg2020branch.

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  • https://gitlab.com/Xparx/dewakss

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Optimal tuning of weighted kNN- and diffusion-based methods for denoising single cell genomics data
Andreas Tjärnberg, Omar Mahmood, Christopher A Jackson, Giuseppe-Antonio Saldi, Kyunghyun Cho, Lionel A Christiaen, Richard A Bonneau
bioRxiv 2020.02.28.970202; doi: https://doi.org/10.1101/2020.02.28.970202
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Optimal tuning of weighted kNN- and diffusion-based methods for denoising single cell genomics data
Andreas Tjärnberg, Omar Mahmood, Christopher A Jackson, Giuseppe-Antonio Saldi, Kyunghyun Cho, Lionel A Christiaen, Richard A Bonneau
bioRxiv 2020.02.28.970202; doi: https://doi.org/10.1101/2020.02.28.970202

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