Principal component analysis-based unsupervised feature extraction applied to single-cell gene expression analysis¹

Abstract

Due to missed sample labeling, unsupervised feature selection during single-cell (sc) RNA-seq can identify critical genes under the experimental conditions considered. In this paper, we applied principal component analysis (PCA)-based unsupervised feature extraction (FE) to identify biologically relevant genes from mouse and human embryonic brain development expression profiles retrieved by scRNA-seq. When evaluating the biological relevance of selected genes by various enrichment analyses, the PCA-based unsupervised FE outperformed conventional unsupervised approaches that select highly variable genes as well as bimodal genes in addition to the recently proposed dpFeature.

Shortlisting genes important in seed maturation by principal component analysis of gene expression data

“…PC5 was able to separate all the developmental stages between the mutants and wild type (Figure 1) in abi 3-1. PCA is performed by considering either genes [3], [19] or the experimental conditions as the variable [20], [21].…”

“…PCA is an unsupervised machine learning method used to increase the interpretability of gene expression data without loss of information [1]- [3]. It is especially suitable for time series data and allows filtering artifactual variations.…”