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. Code and example data for DEWÄKSS is available at https://gitlab.com/Xparx/dewakss/-/tree/Tjarnberg2020branch.
Highlights
Single-cell RNA-seq experimental methods measure gene expression in individual cells from heterogeneous samples
Existing denoising and imputation methods are of varying complexity, and it is difficult to determine if an output is optimally denoised
We have developed and evaluated an objective function that can be reliably minimized for optimally denoising single cell data on a graph, DEWA KSS
Summary
Single-cell RNA-seq (scRNA-seq) experimental methods measure gene expression in individual cells from heterogeneous samples. This allows identification of different cell subpopulations, and has been extensively used to map developmental trajectories. ScRNA-seq experiments yield data with hundreds to hundreds of thousands of individual cell observations; the measured gene expression in each cell is noisy, due to undersampling caused by the extremely low quantities of RNA present in any individual cell [1]. Many computational applications have been developed that leverage the advantages of scRNA-seq experiments [2,3,4,5]. The modeling and motivational assumptions of these approaches vary and include cell-cell similarity, gene covariance, and temporal/trajectory stability
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