What is it about?
Because single cell RNA-seq does not have labels of individual cells, in order to select genes we need unsupervised method that does not require sample labeling.
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Why is it important?
Genes selected by PCA based unsupervised FE is most biologically significant.
Perspectives
As a bioinformatics researcher, I developed PCA based unsupervised FE, that I applied to scRNA-seq.
Professor Y-h. Taguchi
Chuo Daigaku
Read the Original
This page is a summary of: Principal component analysis-based unsupervised feature extraction applied to single-cell gene expression analysis, May 2018, Cold Spring Harbor Laboratory Press,
DOI: 10.1101/312892.
You can read the full text:
Resources
Principal Component Analysis-Based Unsupervised Feature Extraction Applied to Single Cell Gene Expression Analysis
Presentation at ICIC2018 http://ic-ic.tongji.edu.cn/2018/index.htm papers
主成分分析を用いた教師無し学習による変数選択の一細胞RNA-seqへの応用
Japanese Presentation at SIGBIO57 http://www.ipsj.or.jp/kenkyukai/event/bio57.html
Principal component analysis-based unsupervised feature extraction applied to single-cell gene expression analysis
Published paper in ICIC2018
Contributors
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