Amblard E, Bac J, Chervov A, Soumelis V, Zinovyev A. Hubness reduction improves clustering and trajectory inference in single-cell transcriptomic data.
Bioinformatics 2022;
38:1045-1051. [PMID:
34871374 DOI:
10.1093/bioinformatics/btab795]
[Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 11/05/2021] [Accepted: 11/17/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION
Single-cell RNA-seq (scRNAseq) datasets are characterized by large ambient dimensionality, and their analyses can be affected by various manifestations of the dimensionality curse. One of these manifestations is the hubness phenomenon, i.e. existence of data points with surprisingly large incoming connectivity degree in the datapoint neighbourhood graph. Conventional approach to dampen the unwanted effects of high dimension consists in applying drastic dimensionality reduction. It remains unexplored if this step can be avoided thus retaining more information than contained in the low-dimensional projections, by correcting directly hubness.
RESULTS
We investigated hubness in scRNAseq data. We show that hub cells do not represent any visible technical or biological bias. The effect of various hubness reduction methods is investigated with respect to the clustering, trajectory inference and visualization tasks in scRNAseq datasets. We show that hubness reduction generates neighbourhood graphs with properties more suitable for applying machine learning methods; and that it outperforms other state-of-the-art methods for improving neighbourhood graphs. As a consequence, clustering, trajectory inference and visualization perform better, especially for datasets characterized by large intrinsic dimensionality. Hubness is an important phenomenon characterizing data point neighbourhood graphs computed for various types of sequencing datasets. Reducing hubness can be beneficial for the analysis of scRNAseq data with large intrinsic dimensionality in which case it can be an alternative to drastic dimensionality reduction.
AVAILABILITY AND IMPLEMENTATION
The code used to analyze the datasets and produce the figures of this article is available from https://github.com/sysbio-curie/schubness.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
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