Distortion-free PCA on sample space for highly variable gene detection from single-cell RNA-seq data

Abstract

Single-cell RNA-seq (scRNA-seq) allows the analysis of gene expression in each cell, which enables the detection of highly variable genes (HVG) that contribute to cell-to-cell variation within a homogeneous cell population. HVG detection is necessary for clustering analysis to improve the clustering result. scRNA-seq includes some genes that are expressed with a certain probability in all cells which make the cells indistinguishable. These genes are referred to as background noise. To remove the background noise and select the informative genes for clustering analysis, in this paper, we propose an effective HVG detection method based on principal component analysis (PCA). The proposed method utilizes PCA to evaluate the genes (features) on the sample space. The distortion-free principal components are selected to calculate the distance from the origin to gene as the weight of each gene. The genes that have the greatest distances to the origin are selected for clustering analysis. Experimental results on both synthetic and gene expression datasets show that the proposed method not only removes the background noise to select the informative genes for clustering analysis, but also outperforms the existing HVG detection methods.

Graphical abstract

Keywords

single-cell RNA-sequencing / feature selection / principal component analysis / highly variable gene detection / background noise / clustering analysis

Momo Matsuda is currently pursuing her PhD degree. She is now at the Department of Computer Science, University of Tsukuba, Japan. Her current research interests include dimensionality reduction, machine learning and clustering

Yasunori Futamura received his PhD degree in Engineering from University of Tsukuba, Japan in 2014. From April 2014 to Septemper 2014, he was a postdoctoral fellow at Faculty of Engineering, Information and Systems, University of Tsukuba. Since October 2014, he has been an assistant professor at Faculty of Engineering, Information and Systems, University of Tsukuba, Japan. He is a member of the Japan Society for Industrial and Applied Mathematics, Information Processing Society of Japan, and Society for Industrial and Applied Mathematics

Xiucai Ye received the PhD degree in computer science from the University of Tsukuba, Japan in 2014. She is currently an assistant professor with the Department of Computer Science, and Center for Artificial Intelligence Research (C-AIR), University of Tsukuba, Japan. Her current research interests include feature selection, clustering, bioinformatics, machine learning and its application fields

Tetsuya Sakurai is a professor of the Department of Computer Science, and the director of Center for Artificial Intelligence Research (C-AIR) at the University of Tsukuba, Japan. He is also a visiting professor at the Open University of Japan, and a visiting researcher of Advanced Institute of Computational Science at RIKEN. He received a PhD in computer engineering from Nagoya University, Japan in 1992. His research interests include high performance algorithms for large-scale simulations, data and image analysis, and deep neural network computations. He is a member of the Japan Society for Industrial and Applied Mathematics (JSIAM), the Mathematical Society of Japan (MSJ), Information Processing Society of Japan (IPSJ), Society for Industrial and Applied Mathematics (SIAM)

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Acknowledgements

This study was supported in part by the New Energy and Industrial Technology Development Organization (AJD30064) and JST COI-NEXT.

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