Subject clustering by IF-PCA and several recent methods

Frontiers in Genetics

Genetics

Jiashun Jin¹  Zheng Tracy Ke²  Dieyi Chen² 

[1] Department of Statistics, Carnegie Mellon University, Pittsburgh, PA, United States;Department of Statistics, Harvard University, Cambridge, MA, United States;

关键词: gene microarray;    feature selection;    higher criticism threshold;    PCA;    ScRNA-seq;    sparsity;    subject clustering;    variational;   

DOI  :  10.3389/fgene.2023.1166404

 received in 2023-02-15, accepted in 2023-05-03,  发布年份 2023

来源: Frontiers

【 摘 要 】

Subject clustering (i.e., the use of measured features to cluster subjects, such as patients or cells, into multiple groups) is a problem of significant interest. In recent years, many approaches have been proposed, among which unsupervised deep learning (UDL) has received much attention. Two interesting questions are 1) how to combine the strengths of UDL and other approaches and 2) how these approaches compare to each other. We combine the variational auto-encoder (VAE), a popular UDL approach, with the recent idea of influential feature-principal component analysis (IF-PCA) and propose IF-VAE as a new method for subject clustering. We study IF-VAE and compare it with several other methods (including IF-PCA, VAE, Seurat, and SC3) on 10 gene microarray data sets and eight single-cell RNA-seq data sets. We find that IF-VAE shows significant improvement over VAE, but still underperforms compared to IF-PCA. We also find that IF-PCA is quite competitive, slightly outperforming Seurat and SC3 over the eight single-cell data sets. IF-PCA is conceptually simple and permits delicate analysis. We demonstrate that IF-PCA is capable of achieving phase transition in a rare/weak model. Comparatively, Seurat and SC3 are more complex and theoretically difficult to analyze (for these reasons, their optimality remains unclear).

【 授权许可】

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Copyright © 2023 Chen, Jin and Ke.